CONTRACT REPORT. dtims Asset Management Tool User Documentation. Project No: Hui Chen and Dr Tim Martin

Transcription

1 CONTRACT REPORT dtims Asset Management Tool User Documentation Project No: by Hui Chen and Dr Tim Martin for WALGA

2 dtims Asset Management Tool User Documentation ROMAN II ARRB Group Ltd ABN Victoria 500 Burwood Highway Vermont South VIC 3133 Australia P: F: info@arrb.com.au for WALGA Western Australia 191 Carr Place Leederville WA 6007 Australia P: F: arrb.wa@arrb.com.au New South Wales 2-14 Mountain St Ultimo NSW 2007 Australia P: F: arrb.nsw@arrb.com.au Queensland 123 Sandgate Road Albion QLD 4010 Australia P: F: arrb.qld@arrb.com.au Project Leader Quality Manager Version 1.1 September 2012 Norbert Michel Tyrone Toole Reviewed South Australia Level 5, City Central, Suite 507, 147 Pirie Street Adelaide SA 5000 Australia P: F: arrb.sa@arrb.com.au Luxmoore Parking Consulting Ground Floor 12 Wellington Parade East Melbourne, VIC 3002 P: F: International offices: Dubai, United Arab Emirates Xiamen, People s Republic of China September 2012

3 Draft - Version Control ARRB Project No Author PL Hui Chen QM Tyrone Toole Version Date Comments/Revisions By May 2011 Final documentation Hui Chen September 2012 Added changes to rutting works effects model descriptions to reflect changes made in dtims Hui Chen The updated sections are as follows: - Section Works Effects - Section Works Effects - Section Works Effects - Section Works Effects Comments - iii - September 2012

4 SUMMARY The dtims asset management tool which has been adapted for use by Western Australia Local Government Agencies (WALGA) is intended to be used to assist asset managers in predicting future road network conditions and to predict the appropriate amount of funding required to meet both specified levels of service and to optimise the user of the available budget. This documentation provides the user with detailed knowledge and guidance of the different components within the dtims asset management tool to allow effective application. The components are: the deterioration models used to predict future pavement conditions; the data required in order to run the pavement deterioration models; the basic operation of the dtims asset management tool for connectivity with RAMM; system understanding to provide the user the ability to customise dtims to suit their local conditions. Although the Report is believed to be correct at the time of publication, ARRB Group Ltd, to the extent lawful, excludes all liability for loss (whether arising under contract, tort, statute or otherwise) arising from the contents of the Report or from its use. Where such liability cannot be excluded, it is reduced to the full extent lawful. Without limiting the foregoing, people should apply their own skill and judgement when using the information contained in the Report. - iv - September 2012

5 CONTENTS 1 INTRODUCTION BACKGROUND Overview of Tool dtims Tool Functionality Input network Analysis Data Deterioration Models Treatments Analysis Optimisation Reporting User Access Levels Additional Requirements DATA AND INFORMATION REQUIREMENTS Required Data and Information Overview Required Data Fields Network Definition Inventory Data Road Condition Data Traffic Data Weather and Environmental Conditions Committed Treatments DETERIORATION MODELS Deterioration modelling approach Sprayed Seal and Thin Asphalt Surfaced Roads Cumulative Roughness Deterioration Cumulative Rutting Deterioration, rut Cumulative Cracking Progression, crx Cumulative Pavement Structural Deterioration, STRUC Cumulative Environmental Effect on Roughness, ENVIR Incremental Roughness Deterioration Unsealed Roads Gravel Loss Roughness Progression between Routine Maintenance Model Parameters and Lookup Values ROAD TREATMENTS AND WORK EFFECTS Treatments and Work Effects Modelling Chip Reseal Thin Asphalt Overlay Granular Re-sheeting Asphalt Overlay Mill and Replace Unsealed Road Routine Maintenance (grading) v - September 2012

6 5.1.7 Pavement Reconstruction Budget and Treatment Costs Treatment Costs Budget Allocation Benefit and cost of road treatments DTIMS SETUP AND USER PERMISSIONS dtims User Access Levels and Login Viewer Access Level Analyst Access Level Programmer Access Level Creating dtims Logins DTIMS-RAMM INTEGRATION Exporting RAMM data to dtims Loading Road Section Data into dtims Updating Existing Databases Exporting Analysis Results for RAMM Importing dtims Data into RAMM USER CREATED MODELS AND UPDATING ARRB DEFAULT MODELS User Created Models Updating ARRB Default Models Exporting/Importing dtims Items Importing User Logins REFERENCES APPENDIX A THORNTHWAITE MOISTURE INDEX AND TEAMPERATURE READINGS APPENDIX B ANNUAL PRECIPITATION AND EVAPORATION vi - September 2012

7 TABLES Table 4.1: Pavement seal risk factor and design life Table 4.2: Field layer thickness factor, B Table 4.3: Default particle distribution values and plastic index for different size crushed rock and gravel Table 5.1: RAMM default treatments Table 5.2: dtims treatment types Table 5.3: Chip reseal default intervention values and limits Table 5.4: Thin asphalt overlay default intervention values and limits Table 5.5: Granular re-sheeting default intervention values and limits Table 5.6: Asphalt overlay default intervention values and limits Table 5.7: Mill and replace treatment default intervention values and limits Table 5.8: Grading frequencies Table 5.9: Pavement reconstruction default intervention values Table 5.10: Pavement reconstruction default intervention values for unsealed roads Table 5.11: Unit rates for all treatments Table 5.12: Budget categories Table 8.1: Expressions naming convention... Error! Bookmark not defined. Table 8.2: Naming convention used for analysis variables... Error! Bookmark not defined. Table 8.3: Major treatments naming convention... Error! Bookmark not defined. FIGURES Figure 4.1: Example roughness progression trend showing contribution of individual components... 9 Figure 4.2: Dependence of roughness development on model parameters Figure 4.3: Cumulative roughness prediction for changes in climate, TI Figure 4.4: Cumulative roughness prediction for changes in initial pavement/subgrade strength, SNC Figure 4.5: Thornthwaite Moisture Index by LGA Figure 4.6: Cumulative rutting prediction with changes in traffic loading, MESA Figure 4.7: Cumulative rutting prediction with changes in climate, TI Figure 4.8: Figure 4.9: Figure 4.10: Figure 4.11: Figure 4.12: Cumulative rutting prediction with changes in initial pavement strength, SNCo Cumulative cracking prediction for sprayed seals with changes in climate, TI Cumulative cracking prediction for asphalt surfacing with changes in climate, TI Cumulative structural deterioration prediction with changes in initial pavement strength, SNCo Cumulative structural deterioration prediction with changes in traffic loading, MESA Figure 4.13: Incremental roughness prediction for changes in traffic loading, MESA Figure 4.14: Gravel loss prediction for changes in precipitation, Pa Figure 4.15: Gravel loss prediction for changes in traffic loading, ADT Figure 4.16: Roughness prediction for changes in traffic volume, ADT Figure 4.17: Roughness prediction for changes in climate, S Figure 6.1: Citrix login screen Figure 6.2: Download and installation screen Figure 6.3: dtims access screen Figure 6.4: Main menu Figure 6.5: File security prompt vii - September 2012

8 Figure 6.6: dtims login menu Figure 6.7: User settings Figure 6.8: Users creation/modification screen Figure 7.1: RAMM database selection Figure 7.2: Works selection navigator dtims menu Figure 7.3: dtims export menu Figure 7.4: Treatment summarise warning Figure 7.5: dtims export confirmation Figure 7.6: Navigation panel Figure 7.7: Perspectives common tasks panel Figure 7.8: Work space tabs Figure 7.9: Table import Figure 7.10: Data sheet import options menu Figure 7.11: Budget scenario export options Figure 7.12: RAMM database selection Figure 7.13: Works selection navigator dtims menu Figure 7.14: dtims import menu Figure 7.15: Selecting imported scenarios Figure 8.1: dtims analysis process... Error! Bookmark not defined. Figure 8.2: Attribute Properties: a) date attribute b) double attributeerror! Bookmark not defined. Figure 8.3: Expression for nc_cnd_rut... Error! Bookmark not defined. Figure 8.4: Analysis variable properties... Error! Bookmark not defined. Figure 8.5: Analysis variable expressions window... Error! Bookmark not defined. Figure 8.6: Boolean filter expression... Error! Bookmark not defined. Figure 8.7: Treatments resets... Error! Bookmark not defined. Figure 9.1: Exporting items Figure 9.2: Entering table name Figure 9.3: Access database tables Figure 9.4: Importing items Figure 9.5: Table selection for importing viii - September 2012

9 1 INTRODUCTION The dtims asset management tool has been adapted for Local Government Agencies in Western Australia. It is intended to assist asset managers in predicting future road network conditions and the appropriate amount of funding required to meet both specified levels of service, and to optimise the use of the available budget. This report describes the inputs, outcomes, operation, limitations and assumptions made during the adaptation of the dtims asset management tool. It describes: the deterioration models used to predict future pavement condition of the WALGA spray seal, thin asphalt and unsealed road networks the data required in order to run pavement deterioration models the operation of the dtims asset management tool for analysis and reporting of results customisation of dtims asset management tool to allow for regional differences between each Local Government Agency (LGA) The objectives of the dtims asset management tool are to: determine the future condition of road using available pavement condition data allow users to generate works programmes for their road network allow users to allocate funds and review different budget scenarios based on current and future funding compare different works programs based on the type of pavement and treatments allocated September 2012

10 2 BACKGROUND 2.1 Overview of Tool The dtims software provides LGAs with an open asset management analysis and reporting tool which they can adapt to specifically catered asset management programs for their individual road networks. It is focussed on satisfying the strategic planning component of a pavement management approach. The outcomes can then be used as the basis for the development of a detailed works program and maintenance plan. The tool examines the network s current condition and generates a pavement performance forecast over a period of years using prediction models developed by ARRB. Treatments are then applied to each individual road section based on their predicted performance. A list of strategies will be generated for each road section based on the treatments applied. The tool will also select the most appropriate strategy for each road section based on the allocated budget and road priority set by the user. From this a final pavement renewal plan will be generated for the entire road network outlining the works required to be undertaken and the costs it will incur based on the available budget. Multiple scenarios can be generated for the network, which will assist asset managers the best possible management plan for their network. 2.2 dtims Tool Functionality The following sections outline the relevant requirements of the dtims tool as identified in the ROMAN II contract. The dtims tool must have the ability to do the following: Define treatments and costs for works; Store deterioration models (including calibration factors) and undertake deterioration modelling; Generate life-cycle costing analysis; Predict pavement condition over time (and not just employ current condition); Output scenarios showing treatment options against pavement condition and other performance indicators within budget and other constraints; Reporting using the existing dtims reporting module Input network Analysis Data Network data to be used in the analysis shall be generated from the file transferred from RAMM (treatment length table). This information would consist of the necessary parameters as inputs into the life cycle modelling and treatment selection process and expressions Deterioration Models Initially the current ARRB local road deterioration study models for Western Australia will be implemented, with their suitability to the WA network being reviewed once the national models are released by ARRB. If it is deemed appropriate by WALGA, these national models will then be implemented within the software and deployed to all who currently have access. The use of a new set of national models and internationals were later approved as a basis for providing transferable models which could be applied with reasonable confidence throughout September 2012

11 Western Australia. Further details are provided in Section 4 (deterioration) and Section 5 (works effects) Treatments A range of generic treatment types will be built into the initial release of dtims tool that are appropriate for application in WA. As it is understood that treatments can vary from location to location, a suitable selection will be adopted. This facilitates the use of treatments which have been proven best under local conditions. The software must also be able to take into consideration the variation of costs and unit rates within different LGAs and produce scenarios outlining these impacts on budget profiles and network performance Analysis Optimisation dtims allows the user to optimise to any selected objective function or to the specific key performance indicators. Finding the minimum network maintenance cost (minimum budget) and optimising across budget categories or sub networks is built into the software. The user can customise their optimisation models. Similarly, the user can choose which parameter or parameters are the most important for the optimisation, i.e. wishing to achieve the best network level roughness, rutting or a combination of these Reporting Reporting queries and charts are built into the dtims framework. In addition to the built in facilities, all details are available in standard data formats, so users are free to create their own reports. The primary output of dtims is a multi-year, optimised maintenance and rehabilitation program with supporting graphs for average network condition, return on investment and condition distribution. The secondary output of the software is a series of long-term impact charts that assist managers with the planning and financing aspects of their duties. The purpose of these charts is to visually summarise the impact of different budget scenarios on the long-term performance of the network of assets being maintained User Access Levels The dtims tool must come with user access levels which allow certain users to freely create any of the components mentioned above for their own use, not necessary for the management of roads but for other assets the LGA may be responsible for. Other user access levels must also be created to simplify the interface as much as practicable through the use of the inbuilt read/write access security levels (i.e. restricting access to certain functions in the software to simplify the setup and analysis process) Additional Requirements Supply dtims manuals and guides in conjunction with this document to provide users with the ability to review expressions, error tracing and system understanding September 2012

12 3 DATA AND INFORMATION REQUIREMENTS Accurate data and relevant inventory information is required in order for the deterioration models loaded in the dtims asset management tool to function properly. In order to provide LGAs with an understanding of what information they are required to provide, the following sections will outline the different data fields needed and how they will be used. It is the responsibility of LGAs to collect all of the data described in the following sections. Any missing data will be replaced by default values set by dtims, these default values do not represent the true state of the pavement condition, and whilst the values have been chosen based on experience, any analysis performed with default values may produce inaccurate and misleading results. 3.1 Required Data and Information Overview The data and information required for the prediction models and the operation of the dtims asset management tool includes: a network definition representative of the roads and road sections within the network the LGA is responsible for; inventory data of the road sections such as road name, start point, end point, road class, surface and pavement type, etc; condition data such as surface distress data, pavement strength, roughness data, etc; traffic data such as traffic volumes and composition; major treatments previously performed on road sections and treatment programs for unsealed roads; future budget allocations for up to 20 years; climatic and environmental conditions if available to LGAs; committed treatments. 3.2 Required Data Fields The following sections will describe in detail the data fields required for the operation of the dtims asset management tool. These data fields will be provided for each LGA through exports obtained from the RAMM databases. Some of these data fields also require transformation in terms of format and through calculations before an analysis can be performed Network Definition It is acknowledged that all LGA s currently have a defined network contained within their current asset register or ROMAN databases. The dtims asset management tool requires an up to date and accurate version of the network in order to identify what type of roads currently exist within the network. The following RAMM data fields are required in order to define the network: ElementID the treatment length ID, which is unique for each treatment length. Road road number corresponding to each road. A road may contain multiple treatment lengths. road_council the LGA number assigned to the particular road council. This data field allows dtims to apply location based data such as weather and environmental values appropriately to the treatment length based on the LGA it belongs to September 2012

13 3.2.2 Inventory Data Inventory data is critical to facilitate effective management of pavement assets. Knowledge of pavement characteristics such as surface type, width, pavement and surfacing age and road class are critical to predicting its future performance. Many pavement treatments are reliant upon the age of the pavement and surfacing, hence the need to ensure that accurate and most up to date data is available for the network. The following RAMM data fields are required for dtims to determine the pavement characteristics: From start chainage of treatment length To end chainage of treatment length Length length of treatment length tl_width width of treatment length tl_area area of the treatment length, calculated using tl_width and Length and will be used for calculating cost of treatments urban_rural indicates whether the treatment length is in an urban or rural environment cway_hierarchy defines the functional class of the road e.g. local road or highway surf_material the type of pavement surface for the treatment length e.g. asphalt, chip seal or unsealed road first_chip_size the size of aggregate used on the first sealing second_chip_size the size of aggregate use on the second sealing year_last_reseal the date of which the most recent resealing treatment was performed on the treatment length, which will be used to determine the seal age year_last_overlay the date of which the most recent overlay treatment was performed on the treatment length year_last_reconstruction the date of last pavement reconstruction, which will be used to determine the age of the pavement gradings_per_year the number of gradings performed each year on unsealed roads Road Condition Data Without the most up to date or current pavement condition data it is impossible to estimate the future performance of the road section. The following section describes the pavement condition data that is currently available in RAMM how they are used for the operation of dtims. Roughness condition data The following data fields will be used to determine the current or most up to date roughness condition of the pavement: naasra_avg the average roughness of the treatment length in NAASRA counts naasra_max_date the date of which the NAASRA roughness data was recorded hsd_iri_avg the average roughness measured in IRI using high speed data collection avg_lane_iri_qc the average lane roughness measured in IRI using the quarter car model September 2012

14 The roughness condition of the pavement is represented by the data field naasra_avg, if this field is empty the data field hsd_iri_avg will be used. However, if both of these fields are empty then the data field avg_lane_iri_qc will be used. The data field naasra_max_date will be used to determine the pavement age at which the roughness was recorded. This will be used to estimate the initial roughness, IRI 0, as defined in Section (Cumulative roughness model predictions). Rutting condition The following data fields will be used to determine the current or most up to date rutting condition of the pavement: hsd_rutting_avg the rutting in millimetres measured using high speed data collection based on an equivalent 2 m straight edge rutting_severity_avg_mm the average severity of rutting of the left and right wheel paths measured in millimetres hsd_rutting_survey_date date at which the high speed rutting data was collected. The pavement rutting condition is represented by the data field hsd_rutting_avg ; if this data field is empty then the value within rutting_severity_avg will be used. At least one of these fields must contain a value, as no default values are set for rutting. The data field hsd_rutting_survey_date will be used to determine the pavement age at which the rutting condition was recorded. Pavement strength The following data fields will be used to determine the current strength of the pavement: snp adjusted structural number snp_date the date of which the adjusted structural number was collected. The current strength of the pavement will be represented by the data field snp. This field must be provided by the LG as no reliable default value can be used. The data field snp_date will be used to determine the age of the pavement at which the snp was recorded. Cracking condition Cracking condition of the pavement and the date of the pavement at which the cracking condition was recorded is defined by the following data fields: cracking_extent_avg_pct the average extent of surface cracking measured percentage of area crack_date the date of which the cracking extent was measured Traffic Data In order for the models to provide an accurate estimate of future pavement performance, it is critical to have information on the traffic volumes and the composition in terms of the types of vehicles using the road section. This is very important as a road with a higher percentage of heavy vehicles will deteriorate faster than a road that experiences mainly light vehicle traffic. The following data fields obtained through RAMM will be utilised for representing the traffic volume and vehicle composition: ADT average daily traffic used for traffic volumes on all roads PcHeavy percentage of heavy vehicles within the total traffic volume EstAADT estimated annual average daily traffic September 2012

15 traffic_growth percentage of traffic growth experienced by the road per year. It is up to the LGAs to regularly maintain and update the traffic data stored in these data fields to ensure that the dtims asset management tool can operate as intended. Data from EstAADT is obtained from data provided by MetroCount, if this field is empty data from ADT will be used instead. The traffic growth variable will be defaulted to 1 percent growth per year if the field traffic_growth is empty Weather and Environmental Conditions Climatic and environmental factors play a significant role in pavement performance. Environmental factors greatly affect the life of the seal and the performance of both sealed and unsealed roads. The data required by dtims include: Thornthwaite Moisture Index maximum temperature minimum temperature annual precipitation evaporation. Currently none of these data fields have been stored within the RAMM databases. However, they can be obtained using the data recorded by the Bureau of Meteorology (BOM) Committed Treatments The import database must also include all committed treatments that are to be performed on the network. Committed treatments will be used during dtims data analysis to ensure that the selected strategy for each of the road sections will include all the treatments committed by the asset manager. dtims will require the following for each committed treatment: the section of road the treatment is to be applied to (identified by ElementID, road number, start chainage, end chainage) year when the treatment is to be performed type of treatment (currently the only types of treatments available are reseal, rehabilitation or reconstruction based on the results of the Work Selection Tool) cost of treatment September 2012

16 4 DETERIORATION MODELS The majority of the LG road network in Western Australia consists of sealed and unsealed roads of various types. Typically the sealed roads are either sprayed seal or possess thin asphalt surfacing over an unbound granular pavement base. Road deterioration (RD) models for sealed and unsealed roads are implemented within the dtims asset management tool to predict future conditions of all types of road. This section starts first with describing the modelling approach adopted, and its basis, and then describes the individual models and their interactions. Whereas the original contract for Roman II required use of the interim ARRB local road deterioration models, and that these would be replaced at some point with final models, authorisation was obtained to employ national models based on a more structured modelling approach which could be further adapted using local observational data. The main basis for this was to ensure the final models could be applied with reasonable confidence throughout Western Australia, and not be constrained by the limitations of the available observational data. 4.1 Deterioration modelling approach Pavement deterioration prediction models are an important component of a pavement management system (PMS) (PIARC 1995). The following approaches are normally used to predict pavement performance (Haas et al. 1994, Lytton 1987, Mahoney 1990): Probabilistic, or trend, approach that inherently recognises the stochastic nature of pavement performance by predicting the variability of the dependent variable Deterministic approach that predicts a single value of the dependent variable from pavement performance prediction models based on statistical relationships between the dependent and independent pavement performance indicators. In recent years, deterministic models which employ pavement performance relationships composed of the variables understood or assumed to influence pavement performance have been widely used because they lend themselves to adaptation (or calibration) using local observational data, and are also well suited for network planning and programming. The most common type is mechanistic-empirical models which are based on theoretical postulations about pavement performance, but are calibrated, using regression analyses, by observational data (Lytton 1987). These models must adhere to known boundary conditions and physical limits, and can incorporate interactive forms of distress near the end of pavement life, such as the interaction of rutting with cracking, when these interactions are well understood. If these models are theoretically sound and correctly calibrated, they may be applied beyond the range of data from which they were developed. The most common functional forms of these road deterioration (and works effects) models have generally built on HDM technology (Paterson 1987 and Morosiuk et al 2006). Studies in Australia and elsewhere have provided the basis for significant adaptation to achieve results which are consistent with local experience. The emphasis is therefore on providing a structured approach to modelling for both sealed and unsealed roads, and provides a sounder basis to modelling than simpler empirical models developed using regression techniques, the major drawback being even the best models can become unstable beyond the range of data from which they were developed September 2012

17 The main need is therefore to ensure transferability, given the wide range of conditions within Western Australia, and sufficient statistical significance or explanatory power to ensure any changes in inputs are adequately reflected in changes in the output estimates, or predictions. The functional forms used draw on ARRB s Austroads program for sealed roads (Martin 2009, Austroads 2010a), and the unsealed models draw on South African and international studies (Paige-Green 1988, Paterson 1987 and Morosiuk et al 2006). The models for sealed roads include separate relationships for the prediction of individual distresses, e.g. cracking and rutting, and the accumulation of surface disintegration and road roughness. Models exist which represent the different phases of deterioration, for example: initiation and progression of all cracking and wide cracking initiation of potholing from cracking or ravelling initial densification, structural rut depth progression, plastic deformation and acceleration of rutting from cracking, including impacts of rainfall and changes in structural strength. The roughness progression models include contributions from primary surface distresses and overall structural-traffic balance and age-environment contributions, based on broad climate categories considering moisture-temperature differences and evapo-transpiration potential as shown in Figure 4.1. This offers the potential for investigating the consequences of different combinations of input conditions, and their impact on future condition. The basic models are also referred to as incremental, and recursive, as they predict annual changes and allow interaction between variables, as illustrated infigure 4.2. However, simpler cumulative distress models, with more limited interaction, have also been developed which retain the same overall form and provide sufficient explanatory power for most applications. Source: Morosiuk et al. (2006). Figure 4.1: Example roughness progression trend showing contribution of individual components September 2012

18 Figure 4.2: Dependence of roughness development on model parameters Separate models also exist to measure responses to major treatments, for example the effects of overlay or resurfacing treatments on the change in rut depth or roughness, and allow the modelling of all deterioration phases (Section 5). Through an elaborate configuration, calibration and computation process they allow the response to different maintenance and design regimes to be investigated and offer the possibility of greater transferability. Further details on modelling are provided in Austroads (2009). 4.2 Sprayed Seal and Thin Asphalt Surfaced Roads The functional forms of the RD models used for sprayed seal and thin surfaced asphalt roads over an unbound granular base were based on research outcomes for arterial roads from Austroads funded projects (Martin 2009, Austroads 2010a) from long-term observational road deterioration data collected annually from road sites in several states for up to 16 years. These were then calibrated to suit local road conditions using local observational data from the local road deterioration studies. In addition, experimental test pavement deterioration data based on accelerated load testing, using the accelerated loading facility (ALF), was used to quantify the impacts of various surface maintenance treatments and increases in axle loads on pavement deterioration. The main RD (roughness) model starts from the measured roughness condition of the road and predicts the cumulative increase in this roughness (deterioration) under given and changing conditions of traffic load and maintenance regime. The roughness deterioration model predicts these cumulative roughness changes between major maintenance and rehabilitation works that reduce roughness, called works effects (WE). The roughness deterioration model is based on four road distress conditions that are independent model variables. The contributions to roughness deterioration by the four distress conditions are shown in Figure 4.2, the details of each variable are described below: September 2012

19 cumulative pavement rutting contribution to cumulative roughness deterioration due to increased rutting of the pavement surface cumulative pavement cracking contribution to cumulative roughness deterioration due to increased surface cracking, with cracking initiating on the basis of the oxidation age of the surfacing a structural component contribution to cumulative roughness deterioration due to the impact cumulative pavement cracking and the traffic load on structural conditions within the base, sub base and subgrade of the pavement cumulative environmental effects cumulative roughness deterioration caused by environmental effects with increasing pavement age using the Thornthwaite Moisture Index (TI) as a measure of climate Cumulative Roughness Deterioration The roughness deterioration model used in the dtims database predicts roughness deterioration in terms of cumulative roughness, ΔIRI i, where traffic loading is either a constant annual value or is an increased annual value to account for annual traffic load growth. The total roughness, IRI i, for the year i after the initial roughness, IRI 0, at zero pavement age, AGE 0, is the sum of the initial roughness and cumulative roughness as shown in Equation 1: IRI = i IRI 0 IRI i The expression for predicting cumulative roughness deterioration, ΔIRI i, under constant annual traffic loading, MESA, per lane is as follows: where =.74 STRUC crx 0.25 rut ENVIR IRI i k r k = calibration coefficient for roughness (default = 1.0) r 1 IRI i = cumulative lane roughness at the year i after the initial roughness IRI 0 (m/km) rut = cumulative lane rut depth at the year i after initial densification (mm), see Equation 5 crx = cumulative percentage area (%) of cracking after cracking has commenced, see Equation 18 STRUC = cumulative structural condition of the pavement i years after construction/rehabilitation under traffic loading, see Equation 20 ENVIR = cumulative environmental damage on the pavement i years after construction/rehabilitation, see Equation 22. The expression for predicting cumulative roughness deterioration, ΔIRI Δit, under an increasing annual traffic loading value, MESA i, is shown in Equation 26. Under these conditions the total roughness, IRI i, for the year i after the initial roughness, IRI 0, at zero pavement age, AGE 0, is the September 2012

20 Roughness, IRI (m/km) dtims Asset Management Tool User Documentation Version 1.1 sum of the initial roughness and the sum of the annual incremental roughness, ΔIRI Δit, increase for the years i as shown in Equation 3: IRI = i IRI IRI 3 0 it Cumulative roughness model predictions Figure 4.3 and Figure 4.4 each show the cumulative roughness predictions using Equation 2 for various values of Thornthwaite Moisture Index, TI and initial pavement/subgrade strength, SNC 0. As Thornthwaite Moisture Index increases, the predicted roughness will also increase. On the other hand Figure 4.4 as SNC 0 increases the predicted roughness value decreased SNC = 4.5 AND TI = 12 SNC = 4.5 AND TI = 60 SNC = 4.5 AND TI = AGE (years) Figure 4.3: Cumulative roughness prediction for changes in climate, TI September 2012

21 Roughness, IRI (m/km) dtims Asset Management Tool User Documentation Version TI = 12 and SNC = 4 TI = 12 and SNC = 2 TI = 12 and SNC = AGE (years) Figure 4.4: Cumulative roughness prediction for changes in initial pavement/subgrade strength, SNC Cumulative Rutting Deterioration, rut Rutting deterioration is defined as the cumulative lane rutting, Δrut i, at year i after an initial period of densification of one year following pavement reconstruction or rehabilitation. The total rut depth, rut i, for the year i after the initial rut depth, R 0, at zero pavement age, AGE 0, is the sum of the initial rut depth, R 0, and cumulative rutting, Δrut i, as shown in Equation 4: rut = R ruti i 0 4 The expression for the cumulative increase in rutting under constant annual traffic loading, MESA, per lane is shown below: ruti =.617 k AGE TI/ SNC MESA me where rut i k = calibration coefficient for local conditions (default value = 1.0) rut AGE i = age of the pavement at time i since construction/rehabilitation (years) TI = Thornthwaite Moisture Index (Thornthwaite 1948) MESA = constant annual traffic load per lane (millions of ESAs) with no annual growth (R = 0), see Equation 9 me annualised pavement maintenance expenditure ($/lane-km/year), see Equation 10 to Equation September 2012

22 SNC = overall pavement strength at construction or rehabilitation where 0 Equation 6. AGE i = 0, see Thornthwaite Moisture Index The Thornthwaite Moisture Index,TI, is used as an indication of the precipitation and evaporation experienced in a certain region. TI values range from -40 to 80, where lower values indicates drier conditions. Currently LGAs does not record TI values within their ROMAN databases and was obtained for each of the 141 LGAs in Western Australia using the climate tool developed by Byrne and Aguiar (2009) which can estimate TI values for any site based on GPS coordinates and rainfall and temperature data published by the Australian Bureau of Meteorology (BOM) (Appendix A) for the estimation of Thornthwaite Moisture Index values for each LGA. Figure 4.5 shows the range of TI values for each of the LGAs. Figure 4.5: Thornthwaite Moisture Index by LGA September 2012

23 Initial pavement strength, SNC 0 The initial strength of the pavement at the time of construction is defined in terms of the modified structural number, SNC 0 1. The dtims asset management tool will use any recorded data provided by LGAs if it is available. However, in the event that the data is not available, the following expression will be used to calculate SNC 0 (Austroads 2010b) based on an estimated value of overall pavement strength, SNC i, using the surface deflection measured at year i from a Falling Weight Deflectometer (FWD) or similar device: SNC = 0 where SNC exp AGE / DL TI TI = Thornthwaite Moisture Index i SNCi SNC = measured modified structural number at time i i 6 = D (for unbound granular pavements, Paterson 1987) D = 0 maximum deflection (mm) at load centre at AGE i = i AGE = i pavement age at the time of last recorded SNC i at time i DL = K DL P P = pavement design life (years, see Table 4.1) K = calibration factor for structural deterioration (see Table 4.1). DL The pavement design life (years) is obtained from Table 4.1 based on current pavement design practice based on empirical evidence rather than actual design guides. A design life is allocated to each of the different road types. The road types of each road section are obtained from the cway_hierarchy attribute. In the event that the current modified structural number, SNC i, is not available, a default SNC 0 value can be calculated. The default SNC 0 is calculated using the following expression derived from NAASRA (1979) and Hodges et al. (1975). The expression is based on the design life of the pavement and the traffic load it is expected to experience over the nominal pavement design life: where SNC = Log ( MESA /12010 ) DL MESA DL = millions of cumulative equivalent standard axles per lane over nominal design life (DL), allowing for annual growth (%) 1 SNC represents the modified structural number of a papvement, and for Australian conditions is widely considered as being equivalent to the snp variable stored in RAMM. See Morosiuk et al (2006) for a discussion of alternative measures September 2012

24 = MESA/lane/year CGF CGF = Cumulative Growth Factor = ( R) P R P = design life (years, see Table 4.1) R = annual growth rate (%). Equation 7 is based on an assumed design California Bearing Ratio (CBR) of the pavement subgrade of 5%. Traffic loading, MESA The data provided by LGAs contains the attributes EstAADT and PctHeavy which are the annual average daily traffic on the road section and the percentage of heavy vehicles respectively. In order to obtain MESA, the annual traffic load per lane in millions of equivalent standard axles, a conversion formula is needed using the attribute EstAADT. The formula is as follows: where MESA i = EstAADT PctHeavy f 365 MESA i = annual traffic load per lane (millions ESAs) at year i EstAADT = AADT of the road section per lane as provided by the LGA (veh/day) PctHeavy = percentage of heavy vehicles travelling on the road (%) f = traffic factor (ESA/HV) G i 9 = = 1.8 for urban roads (Austroads 2008) 2.5 for rural roads (Austroads 2008) G i = annual traffic growth factor at year i = ( R ) i R = annual growth rate (%). Maintenance expenditure Maintenance expenditure is the annual expenditure, in terms of $/lane-km/year, incurred in maintaining each road section. Maintenance expenditure is defined as the sum of the annual routine maintenance (pothole repairs, crack sealing, shoulder grading, drain clearing, etc.) and the annualised expenditure associated with resealing the pavement (periodic maintenance). Maintenance expenditure, me, is an input variable for estimating cumulative rutting in Equation September 2012

25 This data is not likely to be provided by LGAs and hence needs to be estimated based on the expected routine maintenance expenditure and the expected annualised resealing expenditure which depends on the expected seal life and the type of seal. Each different road type can have a different expression for estimating its annual maintenance expenditure because its seal type and life can vary with road type. The equations obtained by annualising the reseal cost for the pavement, the costs are calculated based on the unit rate for resealing in dollars per square metre, the reseal cost in dollars per lane kilometre based on a 3.5 m wide road and the estimated seal life, y, of the pavement. The annual maintenance expenditure, me, for the following local road types is estimated as follows (Austroads 2010a) using resealing cost rates based on those in the year 2000: Major Collector, me = Y Minor Collector, me = Y Local Access (HV), me = Y Local Access (no HV), me = Y where y = seal life (years), see Equation 15. However, the maximum allowable value of maintenance expenditure, me, estimated from Equation 10 to Equation 13 must be limited to the following value for use in Equation 5 to avoid reducing the cumulative rutting rate: me(max) = MESA It is expected that for lightly trafficked local roads Equation 14 will be used more frequently. The expected seal life, y, is calculated using the bitumen hardening equation (Austroads 2010c), which represents the number of years to reach critical viscosity when cracking of the bitumen occurs due to its hardening. The seal life model is dependent on the temperature, the bitumen binder film thickness which is related to the nominal size of the surface stone aggregate and the durability of the bitumen as represented by the variable D. The model also includes a risk factor, R, which varies based on the type of road and the traffic volume. The equation for seal life, y, based on bitumen hardening is defined below September 2012

26 y 0.158T = MIN R T D S where T = T MAX T MIN 16 2 T = yearly mean of the daily minimum air temperature ( C) MIN T = yearly mean of the daily maximum air temperature ( C) MAX D = ARRB durability test result (days) = 9.5 (default) S = seal aggregate size (mm) R = Seal risk factor (Table 4.1) The minimum and maximum temperatures for each of the 141 LGAs were estimated using the climate tool described in Section (Thornthwaite Moisture Index) based on BOM 2007 database. For the full list of minimum and maximum temperatures for each of the LGAs refer to Appendix A. The seal aggregate size will be obtained from the attributes first_chip_size and second_chip_size, dtims will automatically use second_chip_size to determine the seal aggregate size, if it is empty first_chip_size will be used, if both of these fields are empty it will take a default size of 14 mm. The seal risk factor, R, is obtained from Table 4.1 below. The values are separated by different road types, the road types of each road section are obtained from the cway_hierarchy attribute. Table 4.1: Pavement seal risk factor and design life cway_hierarchy Road type Seal risk factor, R Design life (years) Calibration, KDL Primary Distributor Major Collector Regional Distributor Minor Collector Distributor A Major Collector Distributor B Major Collector Local Distributor Minor Collector Access Road Local Access (HV) Initial rutting, R 0 When pavement age, AGE i = 1, the initial rut depth of the pavement, R 0, is estimated based on the initial densification of the pavement which is defined by the HDM-4 model (Morosiuk et al. 2006) shown below: R = SNC k rid MESA 0 10 SNC September 2012

27 Cumulative rut (mm) dtims Asset Management Tool User Documentation Version 1.1 where k = calibration coefficient for initial densification (default = 1.0) rid MESA = annual traffic loading at pavement age zero (millions ESA) in year 1 0 SNC = as defined above. 0 Cumulative rutting model predictions Figure 4.6, Figure 4.7 and Figure 4.8 each show the cumulative rutting predictions using Equation 5 for separate changes in the independent variables for traffic load, MESA i, initial pavement/subgrade strength, SNC 0, and climate, TI. The mean values used are TI = 12, SNC 0 = 4.5 and MESA i = The plotted graphs do not include the initial densification rutting, R 0. An increase in traffic loading, MESA i, or Thornthwaite moisture index, TI, will both increase the predicted cumulative rutting as shown in Figure 4.6 and Figure 4.7. While an increase in pavement/subgrade strength, SNC 0, will decrease the predicted cumulative rutting (Figure 4.8) Equation Mean TI, SNCo & MESA Equation Mean TI, SNCo & MESA = Equation Mean TI, SNCo & MESA = AGE (years) Figure 4.6: Cumulative rutting prediction with changes in traffic loading, MESA September 2012

28 Cumulative rut (mm) Cumulative rut (mm) dtims Asset Management Tool User Documentation Version Equation Mean SNCo, MESA & TI Equation Mean SNCo, MESA & TI = 80 Equation Mean SNCo, MESA & TI = AGE (years) Figure 4.7: Cumulative rutting prediction with changes in climate, TI Equation Mean TI, MESA & SNCo Equation Mean TI, MESA & SNCo = 1.8 Equation Mean TI, MESA & SNCo = AGE (years) Figure 4.8: Cumulative rutting prediction with changes in initial pavement strength, SNCo September 2012

29 4.2.3 Cumulative Cracking Progression, crx The cumulative cracking progression model, calculates the surface cracking progression in terms of the percentage (%) of the total pavement area at time i years once it has reached its seal life (refer to Section (Maintenance expenditure) for seal life calculation) and commences cracking. The cracking progression model is as shown below: crx i = K n crxage i exp ((200 ) / 25) TI where K = calibration coefficient for cracking (default = 1.0) crxage = cracking age (elapsed time from the commencement of cracking, years) i = Seal _ Age Seal _ life 19 Seal _ Age = years since last resurfacing Seal _ life = number of years until commencement of cracking, see Equation 15 n = crxage modifier = for sprayed/chip seal surfaces = for asphalt surfaces TI = as defined in Section (Thornthwaite Moisture Index). The cracking age is estimated assuming that all cracking is initiated by binder hardening which depends on the annual average daily air temperature. The expression for cumulative cracking, crx, in Equation 18 is dependent on the Thornthwaite Moisture Index, TI, which is a i function of rainfall and temperature (Austroads 2010a). Cumulative cracking model predictions Figure 4.9 and Figure 4.10 each show the cumulative cracking predictions for sprayed seal and asphalt surfacing respectively, based on Equation 18 for different values of Thornthwaite Moisture Index, TI. As TI increases the predicted cumulative cracking also increases. For sprayed seals at low values of TI (= -40) cumulative cracking is predicted to reach 20% cracking (a reasonable upper limit for rural roads) after 70 years while at high TI values (= 50) it is predicted that cracking will reach 20% after 18 years September 2012

30 Cracking (%) Cracking (%) dtims Asset Management Tool User Documentation Version TI = 12 TI = 50 TI = crxage (years) Figure 4.9: Cumulative cracking prediction for sprayed seals with changes in climate, TI TI = 12 TI = 50 TI = crxage (years) Figure 4.10: Cumulative cracking prediction for asphalt surfacing with changes in climate, TI September 2012

31 4.2.4 Cumulative Pavement Structural Deterioration, STRUC The pavement structural condition component estimates the change in structural condition of the pavement and subgrade represented by the initial modified structural number, SNC 0 under traffic loading over time. The expression for this is shown below: STRUC = exp m AGE MESA 1 SNC 0. crx B S 5 where i cum i m = environmental coefficient (Paterson 1987) = TI (Martin 1996) 21 TI = as defined in Section (Thornthwaite Moisture Index) AGE = age of the pavement at time i since construction or last rehabilitation i (years) SNC = as defined in Section (Initial pavement strength, SNC 0 0 ) MESA = cum cumulative annual traffic loading, MESA i up to time i crx i = cumulative cracking component at time i (%). (refer to Section 4.2.3) B = field layer thickness factor for bitumen binder = 0.6 for single seals = 0.9 for double seals S = nominal maximum size of seal aggregate (mm). The field layer thickness factor, B, will be determined by the attribute surf_material, which describes the type of surfacing used. Table 4.2 below shows what values are used for the different pavement types. The nominal maximum seal aggregate size, S, is determined the same as seal aggregate size in the seal life calculation in Section (Maintenance expenditure). Table 4.2: Field layer thickness factor, B surf_material Description Field layer thickness factor, B UNSL Unsealed N/A PSEAL Primer seal 0.6 1CHIP Single chip seal 0.6 2CHIP Double chip seal 0.9 ASPH Asphalt September 2012

32 Structural deterioration dtims Asset Management Tool User Documentation Version 1.1 Cumulative pavement structural model predictions Figure 4.11 and Figure 4.12 each show the cumulative structural deterioration, STRUC, predictions using Equation 20 or separate changes in the independent variables for traffic load, MESA and initial pavement/subgrade strength, SNC 0. The mean values used are SNC 0 = 4.5 and MESA i = An increase in traffic loading, MESA i will increase the predicted cumulative structural deterioration as shown in Figure While an increase in pavement/subgrade strength, SNC 0, will decrease the predicted cumulative rutting (Figure 4.11) Equation Mean MESA & SNCo Equation Mean MESA & SNCo = 6.5 Equation Mean MESA & SNCo = AGE (years) Figure 4.11: Cumulative structural deterioration prediction with changes in initial pavement strength, SNCo September 2012

33 Structural deterioration dtims Asset Management Tool User Documentation Version Equation Mean SNCo & MESA = Equation Mean SNCo & MESA = Equation Mean SNCo & MESA = AGE (years) Figure 4.12: Cumulative structural deterioration prediction with changes in traffic loading, MESA Cumulative Environmental Effect on Roughness, ENVIR The cumulative roughness deterioration of the pavement caused by environmental effects is estimated based on the Thornthwaite Moisture Index, pavement age and the pavement roughness at the time of construction as follows: ENVIR = m IRI 0 AGE 22 where: AGE = age of the pavement at time i since construction or last rehabilitation (years) m = environmental coefficient = TI 23 IRI = initial roughness, IRI at zero pavement age (m/km) 0 all other variables are previously defined. Back calculating IRI 0 Most pavements are constructed prior to the analysis period and as-built pavement condition data is usually not available. In order to determine environmental component of the roughness progression model, a back calculation of the initial roughness of the pavement at the time of September 2012

34 construction, IRI 0, using current available pavement condition data is needed. The expression to calculate IRI 0 is a transformation of Equation 1 as follows: where IRI = 0 IRI i IRI i 24 IRI = last recorded roughness value at year i (m/km) i IRI i = cumulative increase in roughness from initial roughness after i years (m/km) = STRUC crx 0.25 rut ENVIR = refer to Section The expression is then transformed to: where IRI = 0 i STRUC crx 0.25 rut m AGE 1 IRI 25 IRI = last recorded roughness value at year i (m/km) i AGE = pavement age at year i i m = environmental coefficient refer to Equation 23 rut = cumulative rut depth (mm) in since construction/rehabilitation to year i (Section 4.2.2) STRUC = cumulative structural deterioration since construction/rehabilitation to year i (Section 4.2.4) crx = cumulative cracking (%) since construction to year i (Section 4.2.3). i Incremental Roughness Deterioration The cumulative roughness deterioration model of Equation 2 does not account for annual traffic load growth. In order to include traffic growth as a factor in predicting future road conditions, the cumulative roughness deterioration model can be converted into an annual incremental form as shown below. where IRI ti = STRUC 0.016crx 0.25rut ENVIR 26 ti IRI ti = incremental change in roughness, IRI (m/km), for each year i after the ti ti ti September 2012

35 initial roughness, IRI 0, at zero pavement age, AGE 0 STRUC = 5 ti EXP[ m AGEi ] MESA ti [1 ( SNC crx ti B S)] 27 EXP[ m AGE 5 i 1 ] MESA ti1 [1 ( SNC crx ti1 B S)] crx ti = incremental change in percentage (%) area of surface cracking (0 to 100%) contribution to roughness deterioration each year i = exp n crxage / 200 TI K i exp n crxagei / 25 1 / 200 TI / rut ti = incremental change in rut depth (mm) each year i after initial densification at AGE i = 1 = k AGEi TI AGE TI / / SNC MESA me SNC MESA ti me i ti 29 ENVIR = m IRI ti AGE = number of years i since construction or last rehabilitation i AGE = number of years i 1 since construction or last rehabilitation i1 crxage = number of years i after commencement of cracking i crxage = number of years i-1 after commencement of cracking i1 MESA = annual traffic load per lane in millions of ESAs each year i ti MESA = annual traffic load per lane in millions of ESAs each year i 1 ti1 k = calibration coefficient for roughness (default = 1.0). In order to estimate the total roughness, IRI i, at any time i, Equation 26, estimating ΔIRI Δti for each year is substituted into Equation 3 (refer to Section 4.2.1). Incremental roughness model predictions Figure 4.13 shows the total roughness predictions using Equation 26 to predict annual incremental roughness for various values of traffic loading, MESA i. Figure 4.13 shows the roughness increase as MESA i increases with a traffic growth of 1% September 2012

36 Roughness, IRI (m/km) dtims Asset Management Tool User Documentation Version MESA = and traffic growth of 1% MESA = and traffic growth of 1% MESA = and traffic growth of 1% AGE (years) 4.3 Unsealed Roads Figure 4.13: Incremental roughness prediction for changes in traffic loading, MESA The deterioration models for unsealed roads cover two important aspects of deterioration, namely the loss of surface gravel (gravel loss) due to displacement of stones by traffic and environmental effects and the roughness deterioration between routine gradings (routine maintenance) by graders. These two models operate independently and neither will affect the rate of deterioration or outcome predicted by the other model. The roughness is reduced upon completion of each surface grading. The effect of grading can be predicted and accounted for in the works effects model (refer to Section 5.1.6). The unsealed road deterioration models are currently based on a functional form derived from international studies which has been adapted and calibrated to Western Australian conditions. These models were chosen because they included all the independent variables considered relevant for the prediction of these two critical aspects of unsealed road deterioration Gravel Loss The gravel loss model (Paige-Green 1988) takes into account the type of traffic loading on the road, weather and geotechnical properties of the surface material. A road that experiences a high amount of heavy vehicle traffic is expected to deteriorate faster. The evaporation and rainfall precipitation values will determine how dry a road surface is. A dryer road will be more susceptible to gravel loss as it has less surface cohesion. However, at the other extreme where there is high rainfall. Gravel loss could also increase due to surface erosion from rainfall. The particle size distribution of the material used also affects the rate of gravel loss, as larger stone distributions will be more stable than finer materials. The gravel loss, GL, expression is as defined below: September 2012

37 GL = K g { D( axles /1.4 ( N P26) N PF P26) } 31 where GL = depth of gravel loss after time, D, where D is hundreds of days (days/100) after routine maintenance (grading) (mm) K g = calibration factor for gravel loss to suit local conditions (default = 1.0) D = hundreds of days between routine maintenance (days/100) axles = number of axles of light and heavy vehicles (both ways) = ADT ( PctHeavy /1005 (1 PctHeavy /100) 2) 32 ADT = average daily traffic (both ways) PctHeavy = proportion of heavy vehicles (%) N = Weinert value = 12 EJ / Pa (N=1 wet; N=10+ dry) 2 33 EJ = evaporation in the warmest month (mm) Pa = annual precipitation (mm) P 26 = percentage of particles passing 26.5 mm sieve (%) PF = Plasticity Index ( PI ) P75 34 P 75 = percentage of particles passing mm sieve (%). Gravel loss prediction Figure 4.14 and Figure 4.15 each show the gravel loss predictions for unsealed roads, using Equation 31 for separate changes in the independent variables precipitation, Pa and traffic loading, ADT. Equation 31 predicts that as precipitation increases the predicted gravel loss also increases. Predicted gravel loss also increases as the traffic loading increases, there is no traffic growth factored into unsealed roads as it is assumed that traffic does not grow at constant rate in rural unsealed roads. 2 Weinert value is based on evaporation and precipitation. This is closely related to Thornthwaite value which is based on temperature and precipitation, as temperature increases, Thornthwaite index decreases (dryer). Higher temperatures means higher evaporation, as evaporation increases, Weinert value increases (dryer) September 2012

38 Gravel loss (mm) Gravel loss (mm) dtims Asset Management Tool User Documentation Version Percipitation = 100 mm Percipitation = 1000 mm Percipitation = 40 mm AGE (years) Figure 4.14: Gravel loss prediction for changes in precipitation, Pa Traffic ADT = 200 veh/day with 20% HV Traffic ADT = 500 veh/day with 20% HV Traffic ADT = 50 veh/day with 20% HV AGE (years) Figure 4.15: Gravel loss prediction for changes in traffic loading, ADT September 2012

39 4.3.2 Roughness Progression between Routine Maintenance The roughness progression model is based on a model developed from studies in Brazil undertaken by the National Research Council (Visser & Hudson 1983). The model estimates roughness deterioration in the same time frame as the gravel loss model so both road conditions can be measured at the same time. The roughness deterioration model also uses the same types of parameters for its calculation as the gravel loss model. The roughness model is defined below: IRI = u Kr [ 1/ EXP( DIRI ( T NC NT S1 ( P PI NC)))] 35 where D = hundreds of days between routine maintenance (days/100) IRI K = calibration factor for roughness progression to suit local conditions r T = 1.0 (PI >= 7) or 0 (PI < 7) 2 NC = average daily light vehicle traffic (both ways) = ADT ( 1 PctHeavy /100) NT = average daily heavy vehicle traffic (both ways) = ADT PctHeavy / 100 PI = Plasticity Index S 1 = 0 (Thornthwaite Moisture Index < 20) or 1 (Thornthwaite Moisture Index >= 20) Model Parameters and Lookup Values The time between grading or reconstruction, D, and, D IRI, is calculated based on the number of gradings performed on the road each year, which is obtained from the attribute gradings_per_year, divided by 365 days. This calculation will provide the number of days in hundreds between each grading. The traffic loading of unsealed roads will be obtained from the attribute ADT as provided by the LGA through the RAMM databases. This attribute represents the average daily traffic that the road is subjected to. The attribute PctHeavy will be used to represent the percentage of heavy vehicles travelling on the road. These two attributes will be used to calculate the traffic variables NC and NT. The annual precipitation, Pa, and evaporation, EJ, values were obtained for each LGA using the climate tool with values from the BOM 2007 database. These values are stored in a lookup table within the dtims asset management tool and can be modified and updated with new values as more recent information is available. For the full list of values refer to Appendix B September 2012

40 Roughness, IRI (m/km) dtims Asset Management Tool User Documentation Version 1.1 As the particle distribution and geotechnical properties of the materials used for most unsealed roads are not readily available and could not be obtained from LGA RAMM databases, default values were taken from the main report for the Road Base Test kit (Giummarra et al. 2009). These values represents the typical gradation requirements for crushed rock and natural gravels which are the most commonly used materials for unsealed roads. Table 4.3 shows these default values for 20 mm, 30 mm and 40 mm crushed rock and gravel. Table 4.3: Default particle distribution values and plastic index for different size crushed rock and gravel Sieve Size (mm) 40 mm crushed rock 40 mm gravel 30 mm crushed rock 30 mm gravel 20 mm crushed rock 20 mm gravel % passing 26.5 mm % passing mm Plasticity Index 0 This table is stored in dtims as a look up table and can be modified by each LGA to represent the particle distribution and geotechnical properties of the local materials they use to construct their unsealed roads. Roughness predictions for unsealed roads The following graphs assume that four grading is performed each year; hence hundreds of days between gradings, D iri will be days. The roughness value represents the roughness of the road after grading. Figure 4.16 and Figure 4.17 each show the roughness predictions for unsealed roads, using Equation 35 for separate changes in the independent variables S1 which is determined by Thornthwaite Moisture Index, TI and traffic loading, ADT. Equation 35 predicts that as traffic loading increases the predicted roughness also increases. However, roughness increases when S1 equals 0 (TI is less than 20), meaning that areas with higher rainfall will have lower rate of roughness deterioration ADT = 300 veh/day ADT = 100 veh/day ADT = 500 veh/day AGE (years) Figure 4.16: Roughness prediction for changes in traffic volume, ADT September 2012

41 Roughness, IRI (m/km) dtims Asset Management Tool User Documentation Version TI < 20 TI >= AGE (years) Figure 4.17: Roughness prediction for changes in climate, S September 2012

42 5 ROAD TREATMENTS AND WORK EFFECTS Regular treatments, or works effects (WE), need to be applied to road sections in order to maintain an adequate level of service for road users. Currently RAMM has a set of default treatments that are applicable to the types of road surfaces analysed by dtims. Table 5.1 below describes the treatments currently stored in RAMM. Table 5.1: RAMM default treatments RAMM default treatments Chip Seal Asphalt Overlay Milling and Asphalt Overlay Reconstruction Unsealed road gravel sheeting Unsealed road maintenance grading Description Utilised to return the seal to an as new condition to provide a waterproofing function as well as provide a suitably textured surface for vehicle traction. A single layer of asphaltic concrete to provide a minor shape correction and waterproofing function as well as provide a suitably textured surface for vehicle traction. Most commonly used in metropolitan areas or areas with access to an asphalt batching plant or in small quantities such as at intersections to give a longer surface lifespan. The removal of a significant depth of the original asphalt surface prior to the application of an asphalt overlay. Used to prevent the need to replace kerbs due to loss of height following an asphalt overlay. Commonly used in metropolitan areas. Removal and reinstatement of existing pavement structure and surfacing. The application of additional gravel to an existing unsealed gravel road to reinstate the gravel level. Grading of an existing unsealed gravel road to recover stones that have been expelled by traffic and to reinstate adequate shape to the road for drainage. In order to simulate the types of treatments currently used by local governments to maintain a sustainable level of service from their roads, three generic treatment types are used within the dtims asset management tool for both sealed and unsealed roads. Table 5.2 shows the treatment types and their associated treatments currently stored in dtims. Treatment types and treatments Table 5.2: dtims treatment types Resurfacing Chip reseal Thin asphalt overlay Granular re-sheeting for unsealed roads Rehabilitation Granular re-sheeting for spray/chip seal roads Asphalt overlay Mill and replace asphalt Description Reconstruction Reconstruction of asphalt and chip seal roads, restoring pavement condition to initial values Reconstruction of unsealed roads, restoring gravel thickness and roughness to initial values These treatments were selected for each of the different treatment types in order to align with the default treatments as prescribed in RAMM. 5.1 Treatments and Work Effects Modelling The WE models for sealed roads documented are based on the Austroads research report on interim works effects models (Austroads 2007). Thebv models were developed based on information provided by participating state road authorities (SRA). This information consisted of September 2012

43 before and after measurements of road conditions when specified works effects were applied to defined sections of road. This information allowed the quantification of the influence of the specified works effects when sufficient samples of each treatment type were available. Only roughness and rutting correction models are provided based on this research, whereas other pavement conditions will be reset to either default values or previously predicted initial values as defined in Section 4. The following sections describe the WE relationships and intervention rules employed for each of the different treatments, as follows: WE the effect that each specific treatment will have on pavement condition e.g. reducing roughness and rutting intervention values the level of pavement condition at which a treatment will be required e.g. chip reseal treatment will be required when surface cracking has reached 15 % intervention limits limit of pavement condition for which a particular treatment is no longer appropriate or adequate, e.g. when roughness is too high, or resurfacing of the road is no longer sufficient to maintain an appropriate level of service. The intervention values and limits for each of the treatments listed below are stored in their specific look up tables within the dtims asset management tool, with a set of default values defined for each class of road and the region they are applicable to, meaning that each LGA will have their own set of intervention values and limits. Users from individual LGAs with the appropriate access levels will be able to review and modify these values to meet their requirements and to allow for local conditions and practices Chip Reseal The chip reseal treatment is used for chip and sprayed seal granular pavements to rectify or improve pavement surface conditions. It will be applied once surface cracking and seal age has reached an unacceptable level. It is classified as a resurfacing treatment and will be linked to the resurfacing budget as described in Section 5.2. Intervention criteria Table 5.3 shows the suggested default intervention values and intervention limits currently stored for all LGAs for each of the different road classes as defined by the attribute cway_hierarchy. Users with the appropriate access levels can modify these values through the look up table xt_trig_resurf_cs in Cross Tab Transformations under the Work With Data section within dtims. In each case, appropriate preparatory work, including surface patching of badly distressed areas, should be carried out before treatment. cway_hierarchy Table 5.3: Chip reseal default intervention values and limits Intervention values Intervention limits Cracking (% area) Seal age (years) Maximum IRI (m/km) Maximum rutting (mm) Primary Distributor Regional Distributor Distributor A Distributor B Local Distributor Access Road September 2012

44 Work effects The sprayed reseal treatment will reset pavement cracking and seal age back to zero. This will also reduce the cumulative roughness deterioration of the road as cracking is a contributing component in roughness deterioration modelling. Once cracking has been reset to zero, and the appropriate preparatory works undertaken, the same cracking initiation and progression model as defined in Section and Section will apply. Sprayed resealing also improves surface texture. However, texture is not a measured pavement condition within the models described in Section 4, therefore no work effects have been created surface texture condition Thin Asphalt Overlay A thin asphalt overlay treatment will only be applied to asphalt pavements to rectify or improve pavement surface conditions. It will be applied once surface cracking and seal age has reached an unacceptable level. It is classified as a resurfacing treatment and will be linked to the resurfacing budget as described in Section 5.2. Intervention Criteria Table 5.4 shows the suggested default intervention values and intervention limits currently stored for all LGAs for each of the different road classes as defined by the attribute cway_hierarchy. Users with the appropriate access levels can modify these values through the look up table xt_trig_resurf_thin_ol in Cross Tab Transformations under the Work With Data section within dtims. In each case, appropriate preparatory work, including surface patching of badly distressed areas should be carried out before treatment. cway_hierarchy Table 5.4: Thin asphalt overlay default intervention values and limits Intervention values Intervention limits Cracking (% area) Seal age (years) Maximum IRI (m/km) Maximum rutting (mm) Primary Distributor Regional Distributor Distributor A Distributor B Local Distributor Access Road Work effects A thin asphalt overlay provides a minor shape correction to the pavement, slightly improving roughness and rutting. It also resets cracking and inhibits initiation, provided adequate preparatory works are applied. This improves waterproofing of the pavement surface, as well as providing a suitably textured surface for vehicle traction. Equation 37 and Equation 38 below respectively predict the roughness, IRI a, and rutting, Rut a of pavement after a thin asphalt overlay is applied. These equations were based on New South Wales combined urban/rural arterial road data that gave a WE model with adequate explanatory independent variables and statistical significance from the regression analysis (Austroads 2007). The work effect models are defined as follows: Roughness September 2012

45 where Rutting where IRI = IRI b thick 37 a IRI = roughness of pavement after treatment (m/km) a IRI = roughness of pavement before treatment (m/km) b thick = nominal thickness of overlay (mm). Rut = Rut b 0.013thick a Rut = rutting after treatment (mm) a Rut = rutting before treatment (mm) b thick = nominal thickness of overlay (mm). The nominal thickness value is currently set to a default value of 40 mm and may be modified in dtims by users with the appropriate access level to reflect local practices. Note that the rutting model was based on rutting data on roads with high rutting prior to treatment, and may exaggerate rutting reduction on pavements with low rutting. A reduction limit of 2 mm (i.e. the treatment will not reduce the rutting to below 2 mm) is programmed into dtims Granular Re-sheeting Granular re-sheeting will be applied only to sprayed seal pavements in inter-urban or rural areas to rectify or improve pavement conditions. It will be applied once rutting, roughness and pavement age has reached an unacceptable level. It is classified as a rehabilitation treatment and will be linked to the rehabilitation budget as described in Section 5.2. Intervention criteria Table 5.5 shows the suggested default intervention values and intervention limits currently stored for all LGAs for each of the different road classes as defined by the attribute cway_hierarchy. Users with the appropriate access levels can modify these values through the look up table xt_trig_rehab_cs in Cross Tab Transformations under the Work With Data section within dtims September 2012

46 Cway_hierarchy Table 5.5: Granular re-sheeting default intervention values and limits Rutting (mm) Intervention values Roughness (m/km) Pavement age (years) Maximum IRI (m/km) Intervention limits Maximum rutting (mm) Primary Distributor Regional Distributor Distributor A Distributor B Local Distributor Access Road Work effects Granular re-sheeting rehabilitates the base and sub-base of the pavement providing it with a major shape correction, improving roughness and rutting. As it is a rehabilitation treatment it will remove all existing cracking and surface defects. Equation 39 and Equation 40 below respectively predict the roughness, IRI a, and rutting, Rut a, of a pavement after a granular re-sheet is applied, which includes surface sealing. Equation 39 was based on South Australian rural arterial road data that gave a WE model for roughness improvement with minimum explanatory independent variables and acceptable statistical significance from the regression analysis (Austroads 2007). Equation 40 was based on Victorian rural arterial road data that gave a WE model for rut depth reduction with minimum explanatory independent variables and acceptable statistical significance from the regression analysis (Austroads 2007). The WE models for granular re-sheeting roughness and rutting corrections are defined as follows: Roughness IRI = a IRI b 39 Where IRI = a IRI = b roughness of pavement after treatment (m/km) roughness of pavement before treatment (m/km). Rutting Rut = a Rut b 40 where Rut = a Rut = b rutting after treatment (mm) rutting before treatment (mm) September 2012

47 Note that the rutting model was based on rutting data on roads with high rutting prior to treatment which may result in the reset value being greater than the rutting value prior to treatment. dtims is programmed to take this into account where if the reset is greater than the rutting prior to treatment no effect on rutting will be applied Asphalt Overlay Asphalt overlay treatments will only be applied to asphalt pavements in rural areas, to rectify and reinstate the pavement to a serviceable level. In appropriate circumstances, it provides an alternative to a granular re-sheeting. The treatment is triggered when roughness and rutting levels exceed acceptable limits or a minimum pavement age is exceeded. It is classified as a rehabilitation treatment and will be linked to the rehabilitation budget as described in Section 5.2. Intervention criteria Table 5.6 shows the suggested default intervention values and intervention limits currently stored for all LGAs for each of the different road classes as defined by the attribute cway_hierarchy. Users with the appropriate access levels can modify these values through the look up table xt_trig_rehab_ac_ol in Cross Tab Transformations under the Work With Data section within dtims. cway_hierarchy Table 5.6: Asphalt overlay default intervention values and limits Rutting (mm) Intervention values Roughness (m/km) Pavement age (years) Maximum IRI (m/km) Intervention limits Maximum rutting (mm) Primary Distributor Regional Distributor Distributor A Distributor B Local Distributor Access Road Work effects An asphalt overlay rehabilitates the asphalt pavement by adding extra layers of asphalt on top of the existing pavement, providing it with a major shape correction, improving both roughness and rutting. As it is a rehabilitation treatment it will remove all existing cracking and surface defects. Equation 41 and Equation 42 respectively predict the roughness, IRI a, and rutting, Rut a, after asphalt overlay is applied. Equation 41 and Equation 42 were based on South Australian combined urban/rural arterial road data that gave WE models with acceptable explanatory independent variables and statistical significance from the regression analysis (Austroads 2007). The work effect models for roughness and rutting corrections are defined below: Roughness where IRI = IRI b thick 41 a September 2012

48 Rutting IRI = roughness of pavement after treatment (m/km) a IRI = roughness of pavement before treatment (m/km) b thick = nominal asphalt overlay thickness (mm). Rut = a Rut b 42 where Rut = rutting after treatment (mm) a Rut = rutting before treatment (mm). b The nominal thickness value is currently set to a default value of 60 mm and may be modified in dtims by users with the appropriate access level to reflect local practices. Note that the rutting model was based on rutting data on roads with high rutting prior to treatment which may result in the reset value being greater than the rutting value prior to treatment. dtims is programmed to take this into account where if the reset is greater than the rutting prior to treatment no effect on rutting will be applied Mill and Replace Mill and replace treatment will be applied only to asphalt pavements in urban areas, to rectify and reinstate the pavement to a serviceable level. This was done due to the fact that in most urban roads the level of kerb and channelling are set and asphalt must be removed first before a new layer can be placed so that it doesn t change the level of the surface of the asphalt overlay. The treatment is triggered when roughness and rutting levels exceed acceptable limits, or when the pavement has reached a certain age. It is classified as a rehabilitation treatment and will be linked to the rehabilitation budget as described in Section 5.2. Intervention criteria Table 5.7 shows the suggested default intervention values and intervention limits currently stored for all LGAs for each of the different road classes as defined by the attribute cway_hierarchy. Users with the appropriate access levels can modify these values through the look up table xt_trig_rehab_ac_mill in Cross Tab Transformations under the Work With Data section within dtims. Cway_hierarchy Table 5.7: Mill and replace treatment default intervention values and limits Rutting (mm) Intervention values Roughness (m/km) Pavement age (years) Maximum IRI (m/km) Intervention limits Maximum rutting (mm) Primary Distributor Regional Distributor September 2012

49 Cway_hierarchy Intervention values Intervention limits Distributor A Distributor B Local Distributor Access Road Work effects Mill and replace rehabilitates the asphalt pavement by removing the top layer of the existing asphalt and replacing it with a new asphalt layer. This will remove some of the existing pavement defects and provide a correction to the roughness and rutting of the pavement. As it is a rehabilitation treatment it will remove all existing cracking and surface defects provided adequate preparatory works are applied. The mill and replace works effects are defined by Equation 43 and Equation 44 which respectively predict the roughness, IRI a, and rutting, Rut a, after a mill and replacement is completed. Equation 43 and Equation 44 were based on the South Australian combined urban/rural arterial road data that gave WE models with acceptable explanatory independent variables and acceptable statistical significance from the regression analysis (Austroads 2007). Roughness IRI = a IRI b 43 where IRI = roughness of pavement after treatment (m/km) a IRI = roughness of pavement before treatment (m/km). b Rutting: Rut = a Rut b 44 where: Rut = rutting after treatment (mm) a Rut = rutting before treatment (mm). b The same nominal thickness value of 60 mm for asphalt overlays will be used Unsealed Road Routine Maintenance (grading) Routine grading of an unsealed road is not trigger based, but is determined by each LGA based on how many gradings each unsealed road section receives each year. dtims will automatically model the effects of grading based on the number of gradings per year. Routine grading will September 2012

50 reduce the rate of roughness deterioration because the effect of grading is an immediate reduction in roughness. Grading is not predicted to reduce gravel loss, although there may be an apparent immediate increase in gravel thickness which would be quickly reduced by traffic. The WE model for predicting roughness after routine grading, IRI a, is defined by Equation 45, based on a model documented by Patterson (1987) for unsealed roads. This model was chosen because of its relative simplicity and adaptability to WA conditions. Roughness where IRI = IRI qiri IRI a MIN 45 b IRI = roughness of unsealed road after treatment (m/km) a IRI = roughness of unsealed road before treatment (m/km) b IRI = minimum roughness of unsealed roads (m/km) MIN = 3 m/km (default) MIN q = MGD MGD = 1 for dust ratio. The minimum roughness that an unsealed road can achieve after construction needs to be specified by the user performing the analysis. The roughness after grading takes into account the roughness of the road before treatment and the dust ratio of the material. This model is suitable for WE modelling of unsealed road as it is aligned with the deterioration model with the variables used. Table 5.8 shows typical grading frequencies for unsealed roads at different traffic levels (TRL 2003), in the event that the grading frequency is not specified by the LGA, the figures below will be used. Traffic level (AADT) Table 5.8: Grading frequencies Frequency (Gradings per year) < > Pavement Reconstruction A pavement reconstruction treatment will be applicable to all roads within the network. The treatment is triggered by roughness, rutting and pavement age. Pavement reconstruction will be split into two separate components, one for asphalt and chip seal roads, and one for unsealed September 2012

51 roads, as the different types of pavements require different treatments. The cost of reconstruction will be taken from the reconstruction budget as described in Section 5.2. Intervention criteria for asphalt and chip seal pavements The suggested default intervention values for asphalt and chip seal pavement reconstruction are listed in Table 5.9. Reconstruction treatment will not have any intervention limits as no further types of treatments will be used. These values are currently stored for all LGAs for each of the different road classes as defined by the attribute cway_hierarchy. Users with the appropriate access levels can modify these values through the look up table xt_trig_recon_ac for asphalt roads and xt_trig_recon_cs for chip seal roads, found in Cross Tab Transformations under the Work With Data section within dtims. Table 5.9: Pavement reconstruction default intervention values Cway_hierarchy Rutting (mm) Intervention values Roughness (m/km) Pavement age (years) Primary Distributor Regional Distributor Distributor A Distributor B Local Distributor Access Road Work effects for reconstruction of asphalt and chip seal pavements Pavement reconstruction will fully restore all pavement conditions back to initial values. The dtims asset management tool will apply this treatment once the pavement has reached any of the above limit values. The treatment will reset the following pavement conditions: pavement age 0 years cracking, cracking age and seal age 0 years rutting 0 mm structural deterioration - 0 initial modified structural number, SNC 0, reset back to the default SNC 0 value as defined in Section This value was used as it does not require a current value of SNC i and it is based on current traffic volumes roughness will be reset to the initial roughness value, IRI 0, previously calculated Section (Back calculating IRI 0 ). Intervention criteria for unsealed roads The suggested default intervention values for unsealed roads reconstruction are listed in Table The reconstruction treatment will not have any intervention limits as no further types of treatments will be used. These values are currently stored for all LGAs for each of the different road classes as defined by the attribute cway_hierarchy. Users with the appropriate access levels can modify these values through the look up table xt_trig_recon_us found in Cross Tab Transformations under the Work With Data section within dtims September 2012

52 Table 5.10: Pavement reconstruction default intervention values for unsealed roads NA not applicable. Cway_hierarchy Intervention values Gravel loss (mm) Roughness (m/km) Primary Distributor 100 NA Regional Distributor 100 N/A Distributor A 100 NA Distributor A 100 NA Local Distributor 100 NA Access Road 100 NA Work effects for the reconstruction of unsealed roads The reconstruction treatment for unsealed roads will rebuild the pavement back to its original level, resetting gravel loss back to zero. It will also reset the roughness of the unsealed road to its minimum value as defined in Section This value is stored in dtims in the expression US_IRImin and maybe modified by users of the appropriate access level. 5.2 Budget and Treatment Costs Treatment Costs The costs of each treatment are located in the look up table xt_unit_rates, located in the Cross Tab Transformations section. These unit rates are calculated as dollars per square metre of road on which the treatment was performed. The area of road is determined by multiplying the treatment width, tl_width and treatment length, Length. The unit rates for each treatment maybe modified and updated to reflect local practices by users with the appropriate access levels to the dtims asset management tool. Table 5.11 shows the default values currently loaded within dtims, where the road class numbers corresponds to the each of the values represented in the attribute cway_hierarchy : Table 5.11: Unit rates for all treatments Road class Chip resealing Thin asphalt overlay Granular resheeting Asphalt overlay Milling and replace Reconstruction Chip seal Asphalt Unsealed Budget Allocation The dtims asset management tool allows users perform analysis of the road network using different budget constraints. LGA s can decide to perform an analysis using either: unlimited budget where all treatments will be performed with no budget constraints September 2012

53 limited total budget using a total budget constraint for the year to perform all types of treatments separate budget using a separate budget allocated specifically for the different treatment types. There are three budget categories currently specified in the dtims for each of the treatment types listed in Table Users may not alter these budget categories. When performing an analysis using separate budget allocations, however, users will be able to modify the amount of budget allocated to each of the categories for every year within the analysis period by changing the properties of the budget scenario (refer to Section 5.2.2). An example of budget allocation for an 8 year analysis is shown below. Table 5.12: Budget categories Budget ($) Year Resurfacing Rehabilitation Reconstruction 1 $1,000,000 $2,000,000 $3,000,000 2 $1,250,000 $2,250,000 $3,250,000 3 $1,250,000 $2,250,000 $3,250l,000 4 $1,500,000 $2,500,000 $3,500,000 5 $1,750,000 $3,000,000 $3,750,000 6 $1,750,000 $3,250,000 $4,000,000 7 $2,000,000 $3,250,000 $4,250,000 8 $2,000,000 $3,500,000 $4,500, Benefit and cost of road treatments In the default setup of dtims, strategies generated for each pavement may contain multiple treatments. Each strategy will then be optimised based on the strategy s cost/benefit ratio. These are defined below: Cost total cost of all the treatments performed within the strategy Benefit As the deterioration model for roughness, IRI, as defined in Section 4, already comprises all of the other distress conditions (i.e. cracking, rutting, pavement strength and environmental) within its calculation. The roughness, IRI, of the pavement will represent the pavement condition index (PCI) as a measure of the pavements performance. The benefit of any strategy will be measured as the change in pavement roughness compared to if no works were done on the pavement September 2012

54 6 DTIMS SETUP AND USER PERMISSIONS There is no installation process or disk required to install the dtims asset management tool. Users must first obtain a valid ROMAN 2 Citrix account (obtained through ARRB administrator) to access the tool through ROMAN 2 online applications portal or alternatively through the dtims Citrix online hosting website. The user must note that the Citrix login is separate to the dtims database login described in Section 6.1. Upon entering the website the user will be presented with the login screen as shown in Figure 6.1, enter in the valid credentials and click Log On. Figure 6.1: Citrix login screen Once logged in the user will be prompted to download the client, a pop up will also appear at the top of the browser (if using windows internet explorer, other web browsers may differ). Figure 6.2: Download and installation screen To allow the installation to begin, right click on the pop-up and click on Run Add-on, the browser will then ask the user if they want to run the Active X control for Citrix Helper Control, click on Run. On completion users will be able to see the dtims access screen as shown in Figure 6.5, click on the dtims icon to open the application. Figure 6.3: dtims access screen September 2012

55 6.1 dtims User Access Levels and Login Once opened the main menu will be displayed, click on either recently accessed databases or open a new one by clicking on the More option. Figure 6.4: Main menu The user will be asked to give permission to allow dtims to access the user s local network drives as shown in Figure 6.6, always click yes to allow the program access. Figure 6.5: File security prompt Each LGA will be provided with their own copy of the default ARRB dtims database which will be named ROMANII dtims [yyyymmdd]. This default database can be found in Z: drive labelled Databases and is contained within the folder named after the user s LGA name. Each LGA will September 2012

56 only be able see their own folder, any modifications they make on their own copy of the default database will not affect other LGAs, however other users within the same LGA will be able to see those modifications. Open the default ARRB dtims database (.udl file) contained in the folder. The database path will now appear at the bottom of the main menu under Secure Login (Figure 6.6). Figure 6.6: dtims login menu A user must have a valid dtims login to access their database. On roll out of the software each LGA will be given one dtims login to the user who will be managing dtims within their organisation. That user will be designated programmer level and will have the ability to create and manage new dtims logins for other users within the organisation. Unlike the Citrix login LGAs may freely create new dtims logins without notifying ARRB. Once a user has received their login details, enter those details into the appropriate fields and click Login to enter the database. Note that since all users within the one organisation will be accessing the same database it is wise to leave the Remember user name and password box unchecked so that the next user will not be able to login with the previous user s credentials. When creating dtims logins, LGAs should separate the account into three different access levels. The different access levels allow the LGA to control and manage the different users who may be using the dtims tool within their organisation and to provide security and preservation of the contents and work done by each user. The three access levels and their functionality are described below Viewer Access Level 3 Users with viewer access level are limited examining and producing reports. Viewers will not have the ability to modify any models stored within dtims whether they are created by ARRB or future programmers. The user with this access level is able to import, run analysis and create budget scenarios, produce reports and export results Analyst Access Level 5 Analysts may perform all the tasks of the viewer. In addition, analysts will have the ability to modify regional factors such as rainfall, treatment triggers and limits, treatment unit rates, etc, which are stored in their respective cross tables. Analysts may also modify any model calibration factors and analysis parameters. However, like the viewer, an analyst will not be able to view or modify the default ARRB model or any models created by programmers September 2012

57 6.1.3 Programmer Access Level 7 Programmers will have the ability to view but not modify the default ARRB models (All ARRB model components will be assigned an access level of 8) as it should always remain available to all levels of users. However, they will be able to create their own models and any components required for the models to function, such as new input parameters and expressions. A programmer may also restrict other user levels from viewing or modifying their models. The programmer will also have the ability to create dtims user accounts Creating dtims Logins Once logged in the user may create new dtims login by clicking on Tools, Security and Users as shown in Figure 6.7 below. Figure 6.7: User settings The dtims CT v8 Users screen will now appear. The user may now create other logins with access levels that are either of the same level or lower (e.g. if the user is access level 7, he/she may create other accounts that are of access level 7 or lower). Create a login by simply typing in the desired user name and password and click on Add. The user may also change his/her own password in this menu. Simply select your own name, modify the password and click on Update. Users of level 7 or lower will not be able to see other users that are currently stored in the system, so that they may not be able to modify other user s passwords. However, they will be able to create them. In the event that a user has forgotten the user name or password, ARRB reserves an account that is of level 8 (highest level), which has access to all dtims logins and will be used to reset user names and passwords. Figure 6.8: Users creation/modification screen September 2012

58 7 DTIMS-RAMM INTEGRATION 7.1 Exporting RAMM data to dtims To prepare a dtims analysis the user needs to have checked and verified their data and its completeness in RAMM prior to undertaking these stapes, else the analysis may result in inaccurate or incomplete results. For a detailed account of the dtims export process, refer to the RAMM Work Selection Tool documentation. To import data to dtims: Log onto the online RAMM service on (ramm.roman2.com.au) using a valid RAMM login Select the appropriate database at the top of the screen as shown in Figure 7.1 (Each LGA should only have their own database available) Figure 7.1: RAMM database selection In the main menu click on the icon labelled Works Selection A new window should pop up for the works selection navigator Under the dtims section click on Export to dtims (Figure 7.2) Figure 7.2: Works selection navigator dtims menu September 2012

59 The user will be taken to the export menu Select the Attribute and Condition default that is to be used in the export process Select whether these defaults are to be locked to prevent changes being made after the export Select whether a network filter is to be applied. Figure 7.3: dtims export menu Commence the export process by pressing Export Select a location and name for the exported database and press Save If the treatment length summarise function in RAMM has not be run, the user will be presented with the following warning (Figure 7.4), press Continue to proceed Figure 7.4: Treatment summarise warning The process will continue and the file saved to the specified location Once completed the user will be requested to Confirm and finalise the export by pressing the Export Results as shown in Figure September 2012

60 Figure 7.5: dtims export confirmation Following the completion of the export, the user will be shown a report outlining the defaults used in the export process The user should then open the exported database to check that the export contains the following tables so that it is ready to import into dtims: o o treatment treatment_length 7.2 Loading Road Section Data into dtims Once the user has logged in, the user is required to load into dtims the road section data obtained from RAMM exports if they are either using dtims for the first time or updating an existing database with new attribute values / treatment lengths. To import road section data, click on Manage Database Structures in the navigation panel and select the Perspectives icon, as shown in Figure 7.6. Figure 7.6: Navigation panel The available perspectives will now be shown in the workspace window. Select the LCC perspective, under LCC tasks located in the tasks panel click on data sheet view to view what is currently contained in the LCC database September 2012

61 Figure 7.7: Perspectives common tasks panel A new tab will appear in the workspace window as shown in Figure 7.8, where users can navigate through the different tabs currently opened. Notice that the tasks available in the tasks panel have now changed. The new tasks available allow the user to import new database or add and delete elements from the selected database. Figure 7.8: Work space tabs To import a fresh database, simply click on the Import button, the user maybe asked to allow Citrix permission to access their local network drives, if so, click on yes and navigate to the target database and open it. The user will be prompted to select which table they wish to import as shown in Figure 7.9. Figure 7.9: Table import Each LGA will have exports of their road section data from their RAMM databases. RAMM exports contains two separate tables, the treatment table which contains the committed or scheduled treatments currently assigned to each particular treatment length and the treatment_length table which contains all the relevant road section data. The user will be required to import both of these tables separately into the LCC database. The user must always import the treatment_length table before the treatment table September 2012

62 When importing a new database, both the Elements and Attribute values need to be imported. Do this by checking the boxes in the Data Sheet Import Options menu as shown in Figure The New Attributes option is only used when importing a database which contains additional attributes that are currently not stored in dtims, hence, this option is greyed out as dtims already contains all the attributes required. Click Import Now to begin the import process. Figure 7.10: Data sheet import options menu The imported elements will be stored in the BASE perspective. An element represents each of the separate roads that are contained within the database, where each road may contain multiple treatment lengths. The imported treatment lengths will be stored in the LCC perspective, which contains all the attributes and road data relating to each of the treatment lengths. Repeat this process to import the treatments table, by selecting it in the table selection screen. Select to import only the Attribute Values in data sheet import options, as the elements will be the same as those that were imported from the treatment_length table Updating Existing Databases When updating an existing database where all elements are the same, the user is only required to import the attribute values. These will automatically overwrite the data within the database. However, when importing a new database which either has additional elements or a different database all together, the new elements cannot be imported if an existing element occupies the same location or part of it. If this is the case, the user must delete all existing entries contained in the BASE and LCC perspectives and import both the elements and attribute values before importing the new database. To delete all entries enter into datasheet view for each of the perspectives, go to Edit->Remove all elements. 7.3 Exporting Analysis Results for RAMM To export the results into RAMM the user must export the analysis sets and budget scenario into Microsoft Access database format. Each database should contain results from only one budget scenario. Exports of analysis sets contain all of the analysis set properties, which includes the variables used, treatments applied and filters applied. Only one analysis set should be exported for the output database. To export: September 2012

63 select the appropriate analysis set and click on the Export function under the Analysis Set tasks panel browse to the target database or create a new database and click on save the user will be asked to enter in a table name, use the table name provided for the export, it is essential that the default table name is used so that it can be for imported back into RAMM later click on OK to begin exporting Budget scenarios export contains all the generated strategies, analysis variable values, treatments applied, treatment costs and analysis segments processed during the analysis. Only one budget scenario can be exported at a time. dtims will not export multiple budget scenarios to the same database. To export: select the appropriate budget scenario and click on the Export Strategies function under the Budget Scenario tasks panel in the pop up screen click on Browse to select the output database to store the data. (Note: Exporting times may vary depending on where the output database is located, exporting time may increase if the output database is located on the user s local drive, to improve exporting time it is recommended that users export their databases into the folders allocated to them on the Citrix server in the Z: drive labelled databases) click on save click on the Add all button to include all the calculated analysis variables in the export file (Figure 7.11) in the options uncheck the box to export after analysis variables only (Figure 7.11) uncheck the box to export selected strategies only (Figure 7.11) click on OK to begin exporting September 2012

64 Figure 7.11: Budget scenario export options Open the exported database to check that the export contains the following tables so that it is ready to import into RAMM: Analysis_Segments Contains all of the treatment lengths that was analysed Analysis_Sets Contains the analysis set name and details Analysis_Sets_TRT Treatments used within the analysis set Analysis_Sets_VARS variables used within the analysis set Analysis_Sets_YEARS number of years projected during the analysis Ancillary_Treatments ancillary treatments applied over each analysis year Major_Treatments major treatments applied over each analysis year Minor_Treatments minor treatments applied over each analysis year Strategy all generated strategies Treatments lists all treatments performed and associated costs for each strategy Variables tables shows the before and after values of analysis variables for each analysis year. 7.4 Importing dtims Data into RAMM Once the user has verified that the output database contains all of the required tables, the user may import the maintenance strategies back into RAMM. To import: Log onto the online RAMM service on (ramm.roman2.com.au) using a valid RAMM login September 2012

65 Select the appropriate database at the top of the screen as shown in Figure 7.12 (Each LGA should only have their own database available) Figure 7.12: RAMM database selection In the main menu click on the icon labelled Works Selection A new window should pop up for the works selection navigator Under the dtims section click on Import from dtims (Figure 7.13) Figure 7.13: Works selection navigator dtims menu The user will be taken to the Import menu Enter in an import description and click on Import (Figure 7.14) Navigate to the desired database located on your local drives and click on Open September 2012

66 Figure 7.14: dtims import menu Navigate back to dtims menu and the user will be able to see the newly imported scenario The use may now review the scenario and commit treatments by double clicking on the scenario For instructions on committing works refer to the accompanying Work Selection Tool documentation Figure 7.15: Selecting imported scenarios September 2012

67 8 USER CREATED MODELS AND UPDATING ARRB DEFAULT MODELS 8.1 User Created Models Users of all access levels may create new models (i.e. attributes, expressions, analysis variables etc). However, users may only lock their models (read and write access) to a maximum level equal to their own access level, i.e. users with access level 5 may lock their models to a maximum of level 5. This means that other users with a higher access level will be able to modify them. Hence it is advised that all new models should only be created by users with programmer access (level 7). When creating new models, users should name all their items accordingly so that they are easily distinguishable from the default ARRB model components. It is important to note that due to users with access level 5 or above being given access to the default ARRB analysis set, when creating new models users should not alter the default ARRB analysis set. Any new variables, expressions, or attributes created should be stored in separate analysis sets. 8.2 Updating ARRB Default Models When a new release of the default ARRB model is distributed to LGAs (due to updates to the current model or replacing existing model), LGAs will be given ten working days (two weeks) to export their own created items or modified items and import into the new release of ARRB default dtims database before the obsolete database is moved out of the folder and will no longer be accessible. The user should only export and import items that are created by them. Importing items that are created by ARRB may clash with items contained in the updated database Exporting/Importing dtims Items Any items within dtims may be exported and imported into another database. To export an item: Select the item(s). Right click on the selected item(s) and click on export or click on export under the common tasks panel, Figure 8.1 is an example of exporting budget scenarios. Figure 8.1: Exporting items File directory screen will appear, if prompted to allow access to user s local drives, click on yes September 2012

68 Save the item(s) into a new Microsoft Access database file (.mdb file) or select an existing Access database. If the user has selected an existing database where other dtims items have been stored, click on yes when prompted to replace the existing file. Click on save. The user will then be prompted to enter a table name. For most items a default table name will be provided as shown in Figure 8.2 for budget scenarios. In the case where a default table name is not provided, the table name should represent what type of item it is so it can be distinguished when importing tables back into dtims, i.e. analysis expressions should be stored in the AnalysisExpressions table. Figure 8.2: Entering table name Navigate to the target Access database to check whether the export was successful. The user will notice that two tables have been exported. Some items may require extra tables to store additional information. In this case the first table contains the budget scenario properties and the table with the extension ADD stores the yearly budgets for each scenario. Figure 8.3: Access database tables Repeat the steps above to export other items. When exporting other items it is recommended that they are exported into the same Access database for ease of importing later. Once all user created items have been exported, it is ready for import into a new database. Each type of item must be imported separately as dtims does not have a mass import function. To import items: Navigate to the desired item type where the item is to be imported (i.e. go to budget scenarios to import budget scenarios) and right click on the empty workspace and click on import as shown in Figure 8.4, or alternatively click on import under the common tasks panel. The file directory page will now appear. Navigate to the Access database previously used to store exported items. The user will be prompted to select a table to import September 2012

69 Click on the drop down menu and the user will see that the two tables shown in Figure 8.3 will be available. Figure 8.4: Importing items It is important to note that when importing items never select tables with extensions (e.g. tables with ADD at the end), as these tables only contain additional information, and do not contain the main properties of the items. The data within these tables are automatically imported when the main table (table with no extensions at the end) is selected. Select the table BudgetScenario and click OK. Figure 8.5: Table selection for importing Once the items have been imported, check that all the properties are correct Importing User Logins There is currently no functionality to export/import user login details. All user logins must be reentered into the database manually. Only users with programmer access (level 7) should re-enter user logins as users may only create logins with an access level equal to or lower than themselves September 2012