IMPROVING LCCA THROUGH THE DEVELOPMENT OF COST MODELS USING THE ALBERTA ROADWAY MAINTENANCE AND REHABILITATION ANALYSIS APPLICATION (ROMARA)

Transcription

1 IMPROVING LCCA THROUGH THE DEVELOPMENT OF COST MODELS USING THE ALBERTA ROADWAY MAINTENANCE AND REHABILITATION ANALYSIS APPLICATION (ROMARA) Lynne Cowe Falls, M.A.Sc. Assistant Professor Department of Civil Engineering, University of Calgary Calgary, Alberta T2N 1N4 Tel.: Fax: Susan Tighe, PhD, P.Eng Assistant Professor Department of Civil Engineering University of Waterloo Waterloo, Ontario N2L 3G1 Tel.: , Ext Fax: Paper offered for 82 nd Annual Meeting of the Transportation Research Board January 2003, Washington, D.C.

2 Lynne Cowe Falls and Tighe Page 2 of ABSTRACT Maintenance management systems provide various benefits to the owners of road networks and the managers and technical staff on their behalf. In effect, the most cost-effective maintenance treatments at the most appropriate time in the pavement life cycle can be identified. Pavement maintenance models can be developed so that the management team can determine the impact of the treatments and the cost of the treatments and how they impact the pavement serviceability. With an increased emphasis on rehabilitation and preservation of pavements, as the highway network ages and expansion slows, agencies are looking more closely at the cost effectiveness of maintenance and its role in life cycle cost analysis (LCCA). Also, with the move to privatization of highway networks and, in particular the privatization of maintenance activities, maintenance has become more of a contracted item and therefore a line item in the operating budget. Not only has this put the spotlight on maintenance costs, but privatization has also provided a mechanism for tracking of activities, resulting in activity and in some cases, location based cost data. The implementation of the Infrastructure Management System (IMS) and Roadway Maintenance and Rehabilitation Application (RoMaRa) in Alberta has resulted in surface condition data and maintenance work orders which detail the location. This information will provide an improved system for monitoring and tracking costs. This paper presents the results from a workshop held at the 5 th International Conference on Managing Pavements and highlights the engineering applications of using PMS data to improve maintenance and construction. The summary presented focuses on the need for improved uses of maintenance data for technical and engineering purposes. The second part of the paper builds on this idea through the development of cost models based on Alberta data. This preliminary investigation of maintenance costs is to determine if there is potential for modeling of maintenance costs as a function of a pavement performance index. Within RoMaRa there exists a maintenance cost model, which can be used to calculate maintenance costs as part of the Life Cycle Costs of competing rehabilitation alternatives, however, until recently, no location based data was available. Various trends in performance will be presented based on an examination of data from the RoMaRa database and the Infrastructure Management System. It is intended that the methodology could be applied to other areas in Canada that are considering the use of calibrated maintenance cost models as a function of pavement performance indices.

3 Lynne Cowe Falls and Tighe Page 3 of INTRODUCTION Life Cycle Cost Analysis (LCCA) is an economic analysis tool used to evaluate the economic worth of infrastructure projects. Originally developed for investment analysis of building, it has become recognized as an integral requirement of investment-related decisions as defined in the National Highway System NHS Designation Act of 1995 and modified in the Transportation Efficiency Act 21; a process for evaluating the total economic worth of a usable project segment by analyzing initial costs and discounted future cost, such as maintenance, user, reconstruction, rehabilitation, restoring and resurfacing costs, over the life the project [1]. Within the definition above, are two distinct categories of costs: agency costs (specifically, initial construction, maintenance, reconstruction, rehabilitation, and resurfacing) and user costs (specifically, vehicle operating costs, crash costs and user delay costs). Traditionally, LCCA has only included the initial construction and larger rehabilitation costs, primarily because these components of agency costs were administered by contract and therefore, accurate values were available for the analysis. Maintenance costs, which can comprise a large part of the life cycle costs of a pavement, are usually difficult to incorporate in network level decision supports systems using actual data because of two reasons. First, most maintenance management systems are designed to track activity based data on a labour/materials/equipment (LME) basis for accounting and payroll purposes. This data is rarely tied to a specific location on the road. Secondly, LME data is often stored in financial management systems, which are not usually integrated with the engineering decision support systems. The result is that maintenance costs are downplayed in the selection of alternative treatments and strategies, however, the general consensus among highway departments is that routine, and/or reactive maintenance is a major component of the life cycle of a pavement. Much work has yet to be done in the modeling of maintenance costs and the exact impact they have on long-term pavement performance prediction models, but conceptually, maintenance cost models fit into performance models in two areas. Firstly, some surface condition assessment processes, downgrade sealed cracks, that is, if a severe crack (as defined by width of the crack) is sealed, it is either not counted in the crack survey, or is counted but classified as moderate. The detailed distress data, when converted to a surface condition index is impacted and this in turn, has an affect on the overall condition index and performance prediction model. A second area where maintenance can impact performance prediction is in the calculation of effectiveness (as defined by either the area under the curve or remaining service life). If it is assumed that maintenance can impact the surface condition through removal and/or mitigation of surface distress, then application of a maintenance treatment will change the pavement performance, as illustrated in Figure 1. In either case, moderation of the surface condition index or alteration of the pavement performance curve, maintenance can have an effect on the life cycle cost of a pavement and should be included in the agency cost calculations. ENGINEERING PURPOSES OF PMS/MMS DATA There are various data sources that contribute to the information in a PMS. In general historical records, construction, maintenance, material samples and test data, structural information, surface distress and surface texture measurements. Ideally this information is stored in one location or office within an agency for easy access. However, the data is often stored in various files and with different people in the organization which makes it difficult to access this information in a short period of time. Consequently, the real time data is not always incorporated into design and performance models because it is so difficult to locate. Thus, it is difficult to evaluate construction and maintenance methods on an ongoing basis. For example, it is important to examine how effective a maintenance treatment is. The type of treatment, frequency, location, quantity and timing are all factors that need to be evaluated in a technical and economical sense. A PMS can help improve maintenance by assisting in the reanalysis of policy based on the aforementioned factors. It should be used to rerun deterioration and economic models. The challenge presented is that many organizations have a separate Maintenance Management System (MMS) so it is important that the PMS and MMS are consistent in the way the sections are

4 Lynne Cowe Falls and Tighe Page 4 of 23 organized for detailed analysis. GIS are providing better co-ordination in this regard. It is imperative for engineering evaluations that both systems allow that the type of application and the effect or service life and cost can be inputted into the MMS. This information then can provide important feedback to the performance models. A workshop on using PMS data to improve Construction and Maintenance Practices was held at the 5 th International Conference on Management Pavements. There were approximately 80 people that participated in the discussion. It was evident that engineers and technical managers were very interested in using the maintenance data to improve technical and economic effectiveness. [2] Table 1 Summarizes the two primary areas that were identified in the workshop where maintenance data can improve operations, engineering and management. Although it is evident that there are many benefits to using maintenance data for improved operations, a number of barriers were identified. These include [2]: Lack of trust of data Lack of organizational buy in from all units Long distances between highway agency units and central office Lack of communication and education Identification of all stakeholders (equal partners) Good training programs Feedback Better construction data tracking Overall the discussion demonstrated a need to better utilize maintenance data for cost and technical decision making. This paper builds on the ideas presented on the workshop by developing cost models based on maintenance data. 3. BACKGROUND Like many agencies in North America, the Province of Alberta transportation agency (Alberta Transportation) has privatized maintenance along with other functions, such as design and construction management. This change in business required a new approach to data management and access, which has resulted in development of the Infrastructure Management System (IMS), a web-based corporate data repository and analysis system. IMS provides web access to accredited users (department staff, consultants, partners and contractors) who are responsible for delivery of department design, rehabilitation and maintenance work. The architecture of the IMS is presented conceptually in Figure 2. [3] Drawing upon the IMS database are the analysis applications, including pavement preservation, bridge management, appurtenances and network expansion, with the first completed application being the Roadway Maintenance and Rehabilitation Analysis Application (RoMaRa). RoMaRa is a network level pavement management application, which selects user-defined rehabilitation and maintenance treatments using a decision tree approach. Each pavement section in the analysis traverses the tree(s) and treatment alternatives are selected based upon sectional attributes and performance criteria. The process is repeated for every year in the analysis (up to 20 year horizons are possible) and for each alternative creating a matrix of alternatives by year. For each combination of alternative and year the present worth and effectiveness (defined as the area under the future performance curve adjusted for section length) are and converted to a cost-effectiveness measure (defined as the present worth divided by effectiveness). Using cost-effectiveness, the system develops multi-year priority programs through a marginal cost-effectiveness optimization analysis. The present worth calculation can include maintenance costs from actual maintenance records, if available. As with most agencies, maintenance costs and activities have not traditionally been recorded to specific highway locations and as such it has been difficult to incorporate actual maintenance costs. RoMaRa has an elegant solution for incorporation of maintenance costs, through a maintenance cost model. The maintenance cost model is a function of the pavement's age and condition and is used to

5 Lynne Cowe Falls and Tighe Page 5 of 23 estimate the increasing maintenance cost of a pavement as it deteriorates, as illustrated in Figure 3. A separate model is created for each pavement type and functional classification. The model is of the form: MC = C1 * e (C2 * VCI) * area where, MC = one year maintenance cost (i.e. $/sq.m $/sq.km.), VCI = Visual Condition Index, C1 /C2 are the user defined coefficients, area is the pavement area which the maintenance is applied. [4] When developing multi-year programs the user can choose to include maintenance costs in the analysis or not, and if used, to include them in the cost-effectiveness calculation during the optimization analysis. However, as with other agencies, a dearth of data has prevented calibration and/or testing of the coefficients. Alberta, along with many other agencies, privatized maintenance during the past decade. Privatization resulted in a change in the process whereby maintenance work is accomplished. Now, maintenance contract inspectors (MCI) create inspect the roadways and create work orders, which are filled by the maintenance contractor and information about the work (location, type of activity and total cost) i.e. entered into the IMS. Surface Condition Rating (SCR) data has been collected in Alberta for the complete primary highway network for the past five years and, until implementation of RoMaRa has been stored in a proprietary database. SCR data is collected annually for predefined segments, by Maintenance Contract Inspectors, who not only use the SCR to evaluate the pavement condition, but also to develop work orders for the maintenance contractors and to audit the results. All SCR data collection takes place in September October. As part of the implementation of RoMaRa, the SCR data was converted to a Surface Distress Index (SDI) and loaded into the Infrastructure Management System (IMS) from the proprietary database [5]. Five distresses comprise the SDI: longitudinal wheel path cracking, depressed transverse cracking, alligator cracking, other cracking (including non-depressed transverse cracking, meander or block cracking, centerline or center lane cracking and edge cracking) and local defects (such as, raveling). Both the SCR and activity costs are resident in the IMS and for the first time surface condition data and maintenance work orders have been tied directly to the same highway location. 4. PROCEDURE A project was initiated to examine maintenance cost trends for the primary highway network and to determine if the maintenance cost models can be developed for use in the Alberta pavement management application. The major task of the project involved matching the IMS data to the SCR data followed by analysis of the merged data. Merging of the data from the two sources was accomplished as described below. 4.1 Activity Cost Data Extraction and Cleaning Activity cost data for 1996/1997, 1997/1998, and 1999/2000 was extracted from the IMS and culled/cleaned. This included removing intersection specific work orders; all bridge related references and all sections without mileages or locations that could be correlated to a kilometer location (for example, Chipseal in Gleichen, Strathmore, 0.24 E of Hwy 2, start of seal, end of seal, junction of Sibbald Flats Road). All sections that were located to one km only (that is for which there was a start kilometer, but no end kilomtere) were aggregated and located relative to an analysis section. In addition, all activities done within one year of the last rehabilitation were removed from the analysis, as this work would be covered under warranty agreements with the contractor. Work orders in IMS included other right of way related activities and to avoid confusion, only those activities that could be directly applied to the pavement surface were included. This necessitated removal of the following items from the costs: Excavation, as it could be within the right of way and not connected to the pavement Haul, as it could be for material within the right of way and not related to the pavement

6 Lynne Cowe Falls and Tighe Page 6 of 23 Measure cold pour crack sealing Measure linear metres of crack sealing Physical crack seal measure Pit run gravel produce and place Supply of aggregate, as it is unknown where the aggregate is placed/used i.e. purely base course, side slope, chip seal, ACP, etc. Costs were summed for total section and normalized to cost per square meter. 4.2 Surface Condition Data Extraction and Cleaning Data within the IMS is referenced to XY coordinates in normalized ORACLE database tables. RoMaRa uses dynamic sectioning to create analysis sections that aggregate the detailed data according to criteria defined by the analyst and stores them in temporary tables in the IMS. For this analysis, sections were defined by changes in the traffic (AADT) and pavement width characteristics and these were extracted. A total of 1797 analysis sections were created and those with start and end kilometer points that could be matched to the activity cost extraction, retained. Data from the extraction included: section length, width, number of lanes, functional classification (defined as national highway system and non-national highway system), AADT, Annual ESALs, % trucks, Cumulated ESALs since last rehabilitation, SDI for 1997, 1998 and 1999, IRI for 1999, Rut Depth data for 1999 and, overlay requirement as calculated by RoMaRa using the AASHTO method. Two additional fields were calculated from the extracted data: the age since last rehabilitation activity (in all cases, the last rehabilitation was asphalt concrete overlay), and section area. The latter was used to normalize the total section maintenance cost to a cost per square metre. 5. DISCUSSION The original IMS activity cost extraction file included approximately 14,000 kms. After matching of the two files, the total length available for the analysis was 8,884 kms. Summary statistics of the network are presented in Table 2. Figures 4 6 present the average network SDI values by pavement type and functional classification. As seen in Figure 4, the average network condition increased from 1996 to 1997 rising from 8.28 to 8.32, and then decreased to SDI is calculated on a scale of 0 10, with 10 being a pavement with no visible surface distress. Based on the data, the network is in very good condition (assuming that an SDI score greater than 8.0 is very good ) with the GBC pavement having a better average SDI than the CSB pavements in each year of the analysis. When the data is separated into functional class, the non-nhs is in better condition than the NHS, with SDI values of 7.49 to 7.64 for the NHS and for the non-nhs. Higher truck traffic on the NHS may have an effect on the surface condition, however more analysis is required to fully understand this difference. The average cost per square meter by functional class and pavement type is presented in Table 3 and graphically in Figures 7-9 (all costs are presented in Canadian Dollars). The total cost per square meter increases each year in the analysis regardless of pavement type or functional class with the NHS systems having an order of magnitude higher cost than the non-nhs. This is to be expected, given the relative importance of this network and slightly poorer condition as illustrated above. Over the three year period, the NHS network cost per square meter doubled from $ in 1997 to $ in Costs for the non-nhs network increased by 50% in the same period from $ in 1997 to $ During the same period, the non-nhs condition decreased(from 8.44 to 8.35) and the NHS network condition increase (from 7.49 to 7.64). CSB pavements required more maintenance than GBC pavements as seen in Figures 8 and 9. In order to understand more fully, the relationship between SDI and maintenance costs, the SDI values were plotted against the following year maintenance costs. An assumption was made that data collected in the fall was used to order maintenance work during the following year, therefore the 1996 SDI value was plotted against the total cost per square meter for By plotting the two, it was hoped

7 Lynne Cowe Falls and Tighe Page 7 of 23 that a relationship between the condition of the pavement and the amount of work or maintenance cost would be found. The results are presented in Figures No discernible trend is evident in the full network plot (Figure 10), however, when separated by pavement type some trends can be seen. Trend lines were plotted on the graphs for 1996 and 1997 (those years were selected because they had higher number of records), which indicate that as the SDI decreases, the cost per square meter increases as expected. In all cases, the R2 values are very low and it is felt that further investigation (for example, differentiation of the data by subgrade type, ESALs and equivalent granular thickness) may result in more conclusive results. 6. CONCLUSION It is evident that there is a need for improved uses of maintenance data. Based upon this analysis, some trends can be seen which lead to the conclusion that maintenance costs can be modeled as a function of surface condition. In particular, CSB pavements tended to have higher maintenance costs than GBC pavements and the non-nhs network had lower maintenance costs than the NHS network. In all cases, maintenance costs on a square meter basis increased annually with the NHS network costs doubling over three years and the non-nhs network costs increasing by 50%. When compared to network condition, the expenditures resulted in mixed results, the NHS network improved in condition, while the non-nhs declined. While promising, these findings are preliminary. Further analysis of data is ongoing and the inclusion of subgrade soil type, traffic and thickness criteria may result a better understanding of the relationship between maintenance costs and surface condition and ultimately result in development of Alberta specific maintenance cost models, which can be used in Life Cycle Cost Analysis at the network level. 7. ACKNOWLEDGEMENTS This authors wish to express their thanks to Alberta Transportation who graciously provided data for this analysis and Stantec Consulting Ltd. of Calgary who provided the software support. 8. REFERENCES 1. FHWA SA : Life Cycle Cost Analysis in Pavement Design- Interim Technical Bulletin 2. Tighe, Susan, 2001, PMS Results to Improve Construction and Maintenance Practices 5 th International Conference on Managing Pavements, Workshop Summary Notes, Seattle Washington 3. Cowe Falls, Lynne, and Alan Cheetham, 2001 Web Enablement of a Pavement Management System the Alberta Model Paper # 145 Submission for the 5 th International Conference on Managing Pavements, Seattle Washington, August 11 14, 2001, 10pp. 4. Stantec 1999, Engineering Documentation, Highway Pavement Management System 5. Alberta Transportation 2000, Surface Condition Index Model Alberta Transportation Internal document

8 Lynne Cowe Falls and Tighe Page 8 of 23 LIST OF FIGURES FIGURE 1 Impact of maintenance on pavement performance and life. 9 FIGURE 2. IMS application architecture. 11 FIGURE 3 Maintenance cost model. 12 FIGURE 4 Average surface distress index (SDI) of the full network in Alberta by pavement type (cement stabilized base CSB, or granular base course GBC) over a three year period 14 FIGURE 5 Average surface distress index (SDI) of the national highway system in Alberta by pavement type over a three year period. 15 FIGURE 6 Average surface distress index (SDI) of the non-national highway system in Alberta by pavement type over a three year period 16 FIGURE 7 Average Maintenance Costs ($CDN) per square meter of the Full Pavement Network in Alberta over a Three Year Period. 18 FIGURE 8 Average Maintenance Costs ($ CDN) per square meter of pavement in the National Highway System in Alberta over a three year period. 19 FIGURE 9 Average Maintenance Costs ($CDN) of pavements in the Non-National Highway System in Alberta over a three year period. 20 FIGURE 10 Maintenance cost ($ CDN) per square meter as a function of the Surface Distress Index for the Entire Network of pavements in Alberta over a three year period 21 FIGURE 11 Maintenance cost ($ CDN) per square meter as a function of the surface distress index for the cement stabilized base pavements in Alberta over a three year period. 22 FIGURE 12 Maintenance cost ($ CDN) per square meter as a function of the surface distress index for the granular base course pavements in Alberta over a three year period. 23 LIST OF TABLES TABLE 1 PMS/MMS For Improving Maintenance 10 TABLE 2 Summary Statistics for Merged Data Files 13 TABLE 3 Summary Maintenance Costs per Square Meter for

9 Lynne Cowe Falls and Tighe Page 9 of 23 M e a s u r e o f D e te r io r a tio n Past Deterioration Minimum Application of Maintenance Treatments Acceptable Remaining Service Life Construction Now No Rehab Year Pavement Age FIGURE 1 Impact of maintenance on pavement performance and life. With Rehab

10 Lynne Cowe Falls and Tighe Page 10 of 23 TABLE 1 PMS/MMS For Improving Maintenance [2] Functional Improvement Improved Street and Highway Maintenance Improve Design and Performance Area of Improvement Better budget allocation Location references Selection of maintenance strategy Better co-ordination of maintenance schedule with utility sectors Improved scheduling and determination of repair work Assists setting accurate operational budgets Better communication to illustrate successes Maintenance data provides valuable input as historical data for future design Assists with trigger values Maintenance data can be used to direct operations as opposed to just using time scheduled based systems Provides guidance to staff for multi year programs Assists with work planning

11 Lynne Cowe Falls and Tighe Page 11 of 23 FIGURE 2. IMS application architecture.

12 Lynne Cowe Falls and Tighe Page 12 of 23 FIGURE 3 Maintenance cost model [4]. 6 Maintenance Cost Model MC ($) 4 Granular Base Course Cement Stabilized Base Full Depth SDI

13 Lynne Cowe Falls and Tighe Page 13 of 23 TABLE 2 Summary Statistics for Merged Data Files 1) - Pavement type Functional Class Data CSB 2) - GBC 3) - Total National Highway System (NHS) (No. of analysis sections = 141) Non National Highway System (Non-NHS) (No. of analysis sections = 539) Total Network (No. of analysis sections = 680) 1) Note: This does not equal the complete Alberta network 2) CSB Cement Stabilized Base 3) GBC Granular Base Course Mean rut depth Mean AADT Mean %Trucks Total length Mean rut depth Mean AADT Mean %Trucks Total length Mean rut depth Mean AADT Mean %Trucks Total length

14 Lynne Cowe Falls and Tighe Page 14 of 23 FIGURE 4 Average Surface Distress Index (SDI) of the Full Network in Alberta by Pavement Type (Cement Stabilized Base CSB, or Granular Base Course GBC) over a Three Year Period Combined 7.49 Combined 7.98 Surface Distress Index Combined Cement Stabilized Base Granular Base Course

15 Lynne Cowe Falls and Tighe Page 15 of 23 FIGURE 5 Average Surface Distress Index (SDI) of the National Highway System in Alberta by Pavement Type over a Three Year Period Combined 8.28 Combined 8.32 Combined Surface Distress Index Cement Stabilized Base Granular Base Course

16 Lynne Cowe Falls and Tighe Page 16 of 23 FIGURE 6 Average Surface Distress Index (SDI) of the Non-National Highway System in Alberta by Pavement Type over a Three Year Period Combined 8.44 Combined 8.35 Combined Cement Stabilized Base Granular Base Course

17 Lynne Cowe Falls and Tighe Page 17 of 23 TABLE 3 Summary Maintenance Costs ($CDN) per Square Meter for Pavement Type Functional Class Data CSB GBC Total Mean Cost/sqm National Highway System Mean Cost/sqm Mean Cost/sqm Mean Cost/sqm Non National Highway System Mean Cost/sqm Mean Cost/sqm Mean Cost/sqm Total Network Mean Cost/sqm Mean Cost/sqm

18 Lynne Cowe Falls and Tighe Page 18 of 23 FIGURE 7 Average Maintenance Costs ($CDN) per square meter of the Full Pavement Network in Alberta over a Three Year Period Cost per square metre Cement Stabilized Base Granular Base Course.

19 Lynne Cowe Falls and Tighe Page 19 of 23 FIGURE 8 Average Maintenance Costs ($ CDN) per square meter of Pavement in the National Highway System in Alberta over a Three Year Period Cost per square metre Cement Stabilized Base Granular Base Course

20 Lynne Cowe Falls and Tighe Page 20 of 23 FIGURE 9 Average Maintenance Costs ($CDN) of Pavements in the Non-National Highway System in Alberta over a Three Year Period Cost per square metre Cement Stabilized Base Granular Base Course

21 Lynne Cowe Falls and Tighe Page 21 of 23 FIGURE 10 Maintenance Cost per Square Meter as a function of the Surface Distress Index for the Entire Network of Pavements in Alberta over a Three Year Period $/sqm SDI

22 Lynne Cowe Falls and Tighe Page 22 of 23 FIGURE 11 Maintenance Cost per Square Meter as a function of the Surface Distress Index for the cement stabilized base Pavements in Alberta over a Three Year Period R 2 = R 2 = R 2 = Cost per square metre

23 Lynne Cowe Falls and Tighe Page 23 of 23 FIGURE 12 Maintenance Cost per Square Meter as a function of the Surface Distress Index for the granular base course Pavements in Alberta over a Three Year Period R 2 = R 2 = Cost per square metre 0.01 R 2 = SDI