Hamid Hajian, PhD Student, USC, Sonny Astani Department of Civil and Environmental Engineering, Los Angeles, CA,

Transcription

1 A Research Outlook for Real-time Project Information Management by Integrating Advanced Field Data Acquisition Systems and Building Information Modeling Hamid Hajian, PhD Student, USC, Sonny Astani Department of Civil and Environmental Engineering, Los Angeles, CA, Burcin Becerik-Gerber, Assistant Professor, USC, Sonny Astani Department of Civil and Environmental Engineering, Los Angeles, CA, Abstract The paper aims to investigate the current research gaps in the application of advanced field data acquisition technologies (e.g. 3D Laser Scanning and Radio Frequency Identification) in the Architecture, Engineering and Construction (AEC) industry. It starts with an overview of past research done in the area and outlines the processes required to implement these tools. It provides a discussion on related challenges and presents a discussion on potential applications of these tools. The paper suggests an outlook for integrating these systems and Building Information Modeling (BIM) for real-time project information management as a response to the industry s low productivity rates. Keywords: data acquisition, building information modeling, 3D laser scanning, RFID, project information management 1. Introduction Architecture, Engineering, Construction (AEC) industry suffers from low productivity rates, which is greatly related to how project information is managed. (Eastman et al. 2008) In the context of this paper, project information includes, but not limited to, architectural and structural design, contracts, accounting and finance, cost control, submittals, shop drawings, request for information, safety records, job site logs, schedule, progress control, and maintenance records. The process of generating, collecting, analyzing, and utilizing this information is defined as project information management. The AEC industry has been implementing traditional project information management methods, which are mostly inefficient due to timely and costly information gathering and processing, information inaccuracy, lack of information reusability and availability. Due to the fact that the AEC industry is the biggest industry in the world (Department of Commerce website 2008) (1.1 trillion in 2002), a small change in the resource allocation and consumption could have a great impact on the supporting industries and general economy as well. The paper aims to provide an outlook on how the AEC industry can improve current project information management practices with the aid of field data acquisition systems and building information modeling (BIM) to increase the industry s dropping productivity rates (Teicholz et al. 2001) and reduce the waste of resources. In addition, it provides a roadmap for research in the area of construction automation and includes a discussion on how this area of research could be applied to our industry and what the potential benefits would be. 1

2 2. Field Data Acquisition Systems Recent technological advancements in field data acquisition technologies such as 3D Laser Scanning, Photogrammetry, Sensors, Radio Frequency Identification (RFID), and Tablet PCs have offered opportunities to address some of the disadvantages of current data collection practices. 3D laser scanners (or laser radars, LADARs: laser detection and ranging, LIDARs: light detection and ranging) capture geospatial information of the scanned environment and deliver the cartesian or spherical coordinates of thousands of points in the scene called point cloud. 3D laser scanners are applied in surveying and mapping, reverse engineering, quality control, productivity monitoring, condition assessment, and also historic preservation (GSA Building Information Modeling Guide Series 2007). Depending on the purpose of scanning, the deliverable can be registered point cloud, and point clouds can be processed to 2D AutoCAD files (elevations, floor plans, building sections), 3D AutoCAD model of façade, Ductwork, and MEP, 3D model of artifacts (historic preservation), and Building Information Model (BIM) of the scanned facility. In order to have CAD or BIM outputs, point cloud containing huge amount of information needs to be taken through a series of process including data editing, registration, and modeling, which will be discussed in detail in the next sections. Some studies have been done on integrating photogrammetry and 3D laser scanning to alleviate some difficulties in point data processing like edge detection and object recognition. (El-Omari et al. 2008) Since digital image processing is a well-established research field in computer vision area, it can be used to overcome some disadvantages of 3D laser scanning. It would be essentially beneficial in reducing the cost and time of high quality scanning since it takes relatively long time and also incurs high expenses while digital images can be integrated into the laser scanned data captured with lower quality which requires less cost and time to achieve the same quality. RFID is also a commonly used data acquisition system that works with radio frequencies. RFID technology was first used in World War II to detect enemy s aircrafts in a system called IFF (Identity Friend or Foe) and now has several applications in the manufacturing, automotive, aeronautics, oil, and construction industry (Domdouzis et al. 2007). An RFID system consists of a transponder (tag), transceiver and antenna or coil. The tag is composed of a computer chip with an internal memory in which a limited amount of information can be stored. The set of transceiver and antenna is called reader and it can read and may write data from and on the tag holding information about the object attached to it by generating an electromagnetic field. This technology aims to decentralize objects information by making it available wherever the object exists. Sensors have various applications in different industries especially electronics, robotics, and communication. Although their application in the AEC industry has been limited so far, recent advancements in structural engineering require implementing electronic products for structural health monitoring such as embedded sensors. Sensors are currently used in the building industry mainly in the form of strain and temperature gauges or accelerometers to provide engineers with data needed to analyze and monitor the structural health of infrastructures and buildings. According to Kiziltas et al. (2008) embedded sensor systems consist of sensor, data logger, transceiver, and power source. This system is capable of capturing, recording, transferring, and processing data such as temperature, strain, and acceleration. Akinci, et al (2006) did a case 2

3 study on using sensors to record the concrete temperature to monitor the structural strength of cast-in-place concrete. The main problem with the sensors seems to be limited size of memory to record the captured data which may be resolved in the future by advancements in this field. Tablet PCs are portable computers that are designed for field use and are able to accommodate several kinds of information such as project drawings and specifications. They can connect to the project central database through Ethernet or Internet and some of them also have a port for an RFID reader. These mobile devices can transfer the acquired information in the field to a central database and also provide project engineers and managers with the project information needed at the construction site. There has been a great progress in the development of tablet PCs and their application to the AEC industry during recent years and they have become popular among project stakeholders. Many of the introduced field data acquisition systems can be utilized in a more automated fashion by using Robotics as a platform to automate the data collection process and reduce the need for human labor. A set of machines embedded together, capable of performing specific autonomous or semi autonomous operations intelligently in the field like moving is referred to as Robotics. Gramegna (2006) shows how to implement robotics, laser scanning, and photogrammetry together for automatic 2D mapping and 3D model reconstruction of indoor environments. The concept of project central digital database, which can accommodate different types of information within itself and is accessible to project stakeholders, can complement the advantages of utilizing field data acquisition systems mentioned above. 3. Building Information Modeling Developments in information technology have provided numerous opportunities for the AEC industry, one of which is Building Information Modeling (BIM). BIM is a central data repository to store and recall different kinds of information about projects by creating a 3D model and adding intelligent data to it. It is often known as new generation of 3D CAD; however, BIM has a lot to offer than simple 3D models that are mostly used just for visualization. There are various implementation examples of BIM such as model-based quantity take off, construction simulation and coordination, change management, design alternatives exploration, energy simulation, etc. Although the AEC industry is adopting BIM rapidly, it is commonly used for MEP coordination and clash detection and 3D visualization during design phase. Employing BIM, as a real-time data-rich model will expand its application from design to construction to occupancy period dramatically to monitor and manage project information. However, this requires reliable data acquisition sources for updating BIM frequently based on the needs. The field data acquisition technologies are potentially able to play this role of feeding into a central database with real-time project information. In this regard, Application Programming Interface (API) is a great tool to author in or update BIM with the real-time captured data in an automated fashion through the aid of computer programming techniques. API enables the user to reach and manipulate the data in a programming environment without any need to use the software s user interface. For instance, a CAD object can be drafted by inputting the object s coordinates into the AutoCAD through its API. BIM coupled with these technologies provides project managers a new way of capturing and storing a wide variety of real-time digitally formed information. Imagine all changes occurred during construction could be automatically captured and the BIM is updated concurrently as changes happen and facility management critical information is added to the 3

4 data-rich model simultaneously. This process would provide accurate as-built BIMs at the end of closeout that can be used for facilities management immediately. This combination extends the application of BIM to construction and then occupancy stages of project life cycle. Having a real-time as-built BIM technology (i.e. AutoDesk Revit, ArchiCAD, Digital Project) tied to a central management database (i.e. SQL) that is capable of mutual data communication with Computerized Maintenance Management Systems (CMMS) (i.e. Famis, Maximo), can be used to link the acquired data to numerous applications. Project information may be collected by different sources and then processed through separate processes, some of which are depicted in figure 1 and explained further sections of this paper. As shown, in order to create an as-built BIM for the project, the as-planned data, if available, can be used for initialization. Laser scanning, RFID, and photogrammetry are introduced as sources of new data acquisition and the corresponding processes are depicted in this chart; however, there can be other scenarios, which are not discussed in this paper. Integrating field data acquisition systems and BIM for real-time project information management process offers outstanding opportunities to the AEC industry. Applications, processes, challenges, and future research questions of these technologies, especially those surrounded by dashed line, are discussed more in the next sections. Figure 1. Proposed process for data acquisition, processing and validation 4

5 4. Data Acquisition, Processing and Validation Process 4.1 3D Laser Scanning Scanning 3D laser scanning is a relatively new technology first developed for surveying and mapping. The scanner emits a laser beam and calculates its distance to the scanned object based on the reflected beam s phase difference with that of the original beam (phased-based) or round trip laser time of flight (time-of-flight). New scanners also add color and return pulse intensity to the output data. The output of the scanning process is a complex set of points known as point clouds containing geo-spatial information of the scanned environment in a Cartesian (X-Y-Z) or Spherical (Φ-θ-R) coordinate system. Depending on the density of scanned points, it is called sparse or dense point cloud requiring different data processing respectively. 3D laser scanners can be considered as an advanced form of total station surveying equipments with faster data acquisition speed. Total stations emit laser beam to a reflector placed on a point whose coordinate is to be determined and calculates and stores its geo-spatial information. However, Kiziltas et al (2008) showed that total stations give more single point distance and angular accuracy in the delivered sparse point data versus those of laser scanner s dense point cloud. Phased-based and time-offlight are two main category of laser scanners. Phased-based scanners which estimate the distance based on the phase difference between the signal sent out and the received signal deliver more accurate data than time-of-flight ones which determine the distance based on the laser beam round trip time of flight. Further, phased-based scanners are also faster than time-of-flight counterparts. Scan time is a function of field of regard, point density, and instrument s speed. (GSA Building Information Modeling Guide Series 2007) Field of regard is defined as angular (horizontal and vertical) coverage of the scene and angular increment or point spacing at a given distance is known as point density. Noise removal is the first step in the modeling process. Some invalid data may be generated during the scanning process because of shadows or movement of objects in the scene, which are known as noise and should be removed from the point cloud. Removing noisy data may have some undesirable impacts such as unintentional deletion of some valid data Registration Both types of 3D laser scanners are line-of-sight instruments, capturing visible parts of object. Thus, casting shades on the invisible parts. In order to solve this problem, scanning from different locations may be required to overcome occlusion and to cover areas of interest in the scene as much as possible. Due to multiple scanning, there will be common points in different scans with different coordinate frames. The process of transforming two or more scans of the same object from different locations to a single point cloud to have a common reference frame is called registration. (GSA Building Information Modeling Guide Series 2007) There are two major registration methods practiced in the industry. The first method uses at-least 3 sphere or planar targets (5 is recommended by GSA BIM Guide) that may be captured in two scan shots whose point clouds are transformed into a single reference frame. In the second method, the scanner is set over a point with known location with respect to the previous scan shot reference frame. Therefore, that new point cloud s coordinates can be transformed into the first (unique) coordinate frame. 5

6 4.1.3 Modeling After having point clouds registered, the next step is constructing a 3D model of the scanned environment. Current modeling practices involve a series of mostly manual operations. Some of these manual operations are drawing wire frames from the point cloud, creating surfaces with frames, extracting solid objects, and finally texturing the 3D objects. Many algorithms have been developed to automate this process for edge detection, fitting range data to 3D surfaces to construct 3D model of objects, and generally object recognition. Different algorithms work based on different mechanisms and may be developed to work with sparse or dense point cloud. Kwon et al. (2004) introduces a semi automatic method to match sparse range data to primitives for rapid 3D modeling. In that research, two algorithms are developed to fit point cloud to a cuboid and cylinder respectively. The user selects a patch of points and runs the appropriate algorithm (cuboid or cylinder) to fit the selected set of points to the desired geometric primitive. For instance, in case of cuboid, the algorithms tries to find the bounding planes using least square method and then intersecting lines and vertex points of the cuboid. The next step is merging the detected primitives into each other to get closer to the shape of the real objects, for which many algorithms and techniques have been developed. There are methods developed to work with dense point clouds as well. El-Omari and Moselhi (2008) introduce a method to combine photogrammetry and 3D laser scanning to reduce the need to very dense point cloud with high accuracy. The purpose of this method is to increase the speed of scanning process so that construction progress could be measured File formats To compare the acquired new data representing the as-built condition of the project and the asplanned project information, the files should have a common format. Laser-scanned data is usually provided in point cloud format, whereas, the as-planned project information may be available as 2D drawings, 2D or 3D CAD files and recently as BIM files. Bosche and Haas (2006) have investigated the data formats for integration of sensed and 3D CAD data for improved equipment operation safety. Different formats are available for as-planned documents. Some of them are DGN, DWG, DXF, and IFC for architectural and structural information, DTM (Digital Terrain Modeling) for earth surface modeling, VRML (Virtual Reality Modeling Language) and XML for visualizations capable of rapid traveling through and STL (Stereo- Lithography) for rapid prototyping in manufacturing industry. As construction sites are subject to frequent changes, Bosche and Haas explore data formats that are most suitable for rapid 3D prototyping such as VRML/XML, convex-hull, STL, and point cloud. The main advantage of VRML and XML is the ability to be used on the Internet. In addition these file formats are smaller in size that is essential for easy fly-through. Convex-hull format represents geometric surfaces by a series of polygons that form a mesh or wire-frame. This file format requires less data, which alleviates the data processing difficulties. Both VRML/XML and convex-hull suffer from the same disadvantage of being limited to representing the surfaces by predefined shapes that are geometric primitives in VRML/XML and convexes in the latter one. Similar to VRML and convex-hull, volumes are approximated by a mesh in STL files. However, they do not need to be convex. This feature of STL gives a good modeling flexibility but requires more complex computations. Most CAD software packages can export in STL format (Bosche et al. 2008); another major advantage of STL to other 3D CAD formats. On the other hand, the availability of sensed data in point cloud and the simplicity of comparison between two sets of point cloud 6

7 make it the best format to store and recall spatial information. Bosche and Haas (2008) introduce a method to convert STL to point cloud to be able to compare the as-built and as-planned data Sources of error Calibration errors, environmental conditions like instrument vibration and thermal expansion due to sunshine and wind, surface reflectivity, and dynamic scan scene are sources of error during scanning process. Scanning process requires minimum movement in the scene in order to minimize the amount of noisy data, though construction site is not a static environment. Mixedpixel phenomenon is another problem causing wrong data acquisition. The more the laser beam distances from the scanner, the thicker it becomes. Therefore, the beam may cover two surfaces belonging to different objects at different locations at the same time, especially, when scanning objects located far away from the scanner. A mixed-pixel forms when the laser beam hits two objects with different distances at the same time and two signals are received for the one emitted which means two ranges are recorded for one point. It usually happens when scanning the edges of objects, too thin objects, and the bottom edge of objects resting on the ground (ground interaction). There are some sources of error in the post scanning processes such as registration and modeling. Accuracy of measurement of targets and the uniformity of targets distribution throughout the work volume are the main sources of error in the first registration method (using targets). According to GSA BIM Guide (2007), accuracy of positioning scanner over the control point also determines the accuracy of the registration in the second registration method. In addition, the errors of range data fitting algorithms affect the accuracy of the modeling process. 4.2 RFID RFID technology is widely used in the manufacturing industry for supply chain management and it is becoming more popular in the building industry. Applications of RFID in construction industry includes but not limited to supply chain management (Tserng et al. 2005), automated material and asset tracking, tracking activities of labors to improve safety, locating buried assets, and on-site inspection using RFID and PDAs (Wang et al. 2007). Unlike laser scanner, RFID is not a line-of-sight instrument; it works with electromagnetic waves. Another major advantage of this technology is the ability to be encapsulated to work under harsh conditions (i.e. construction sites). These features make RFID technology a strong and effective data acquisition system to be used in the AEC industry. Kiziltas et al. (2008) showed the satisfactory results of implementing RFID to track the pipe spools delivered to site, locating pre-cast concrete components in the storage yard, and monitoring quality information of asphalt during road pavement. Using RFID to improve labor safety is also another potential application of RFID to the AEC industry and it is currently under research. Data processing for RFID technology is simpler than 3D laser scanning. The new data captured by the reader need to be stored to update the database (i.e. the new location of a component on site). In an RFID system, a tag including an integrated processor and memory is attached to the object and specific information is stored in it. Stored data can be as simple as an ID number or can be more complex. In order to read the data inside tag, the reader needs to be in less than a certain distance to the tag referred to as read range. While old RFID tags had small memory capacities, the advanced ones can hold up to 64 KB data. RFID tags are produced in two types: active and passive. Active tags have an internal battery, which may work about 5 to 10 years and have more read ranges whereas passive tags need to be activated by the electromagnetic field 7

8 produced by the reader with much less read ranges. Moreover, active tags can hold more information compared with the passive ones. The rate of data transfer between the tag and reader depends on the frequency used to communicate information. (Domdouzis et al. 2007) The UHF waves ( MHZ) are the most efficient frequencies since they give more read range (less than 100 m) and higher data transfer rate (100 KB/sec) than other ranges. According to Ergen and Akinci (2007), active UHF tags are effective systems to have long read ranges. The disadvantage of RFID technology is its performance reduction in presence of massive objects, metals, and liquors because of absorption or reflection of radio wave that may cause tags not detected by the reader. Kiziltas et al. (2008) suggest encapsulating tags, using insulator between tag and attached surface, and using multiple antennae in highly metallic and congested environment as solutions to this problem. 5. Challenges and Research Opportunities Although advanced use of field technologies holds a great promise in assisting, enhancing and automating project information acquisition and information processing, there are still challenges to overcome and gaps to be researched. These research opportunities are summarized below in two categories: data acquisition and data processing. Data Acquisition: Reducing Data Uncertainty: There is information that has not been currently captured on surfaces through scanning such as materials and dimensions. By introducing a multisensor solution, we can make determinations about surface materials and dimensions, for example, by, analyzing absorption of light and reflectance. Adding smart RFID tags to components could help with automatic object recognition process as discussed in the following section. Scanning by itself cannot identify what the purpose of a box is or what it has been made of. If we have smart tags on components, we can identify the object, its pose and orientation. Global Positioning Systems (GPS) are widely used with field data technologies. By sending packets through Ultra Wideband 1 (UWB) to take GPS coordinates into the building, we can stay in the same coordinate system from outside of the building to the inside. If RFID tags are tied to UWB, they can give us information about their orientation to each other. This is called time domain; a term used to describe the analysis of mathematical functions, or physical signals, with respect to time. Imaging Beyond Surface: Current systems don t collect data beyond the surface and they don t look into the building structure. Putting all building information together can provide immediate value in renovation projects and facilities management. Information collected can also create a baseline for change detection. Autonomous Systems: There is a need for data acquisition systems to be more selfsufficient during the data acquisition process. Creating a multi-sensor environment as discussed above would provide the opportunity for a more autonomous data acquisition process, which is more robotic and intelligent. This can be achieved by having verification from independent tools in the system - laser scanner, RFID tags, total stations- where these tools work robotically and compare results and monitor instrument 1 Ultra Wideband (UWB) is a radio technology that can be used at very low energy levels for short-range highbandwidth communications by using a large portion of the radio spectrum. ( 8

9 drift. By enabling tools to report on the proper range accuracy and geometric control, a more accurate budget of uncertainty can be created. Data Processing: Point Clouds to BIM: Manual reconstruction of 3D model from point cloud involves manual processes, which incurs high costs and requires relatively long time contradicting the whole purpose of having a real-time model representing current status of the project. In the current practice, captured point cloud is manually overlaid on the as-planned 3D CAD model or BIM to detect the deviations from as-planned and this process relies on human judgment. This is where computer programming could be utilized to perform massive computations to compare the acquired spatial data with existing data in 2D or 3D CAD files, or BIM. Although some techniques have been developed to convert 2D to 3D CAD files, how existing information can be used to instantiate BIM is a question to be explored. BIM is currently used limitedly but it has a huge potential to be used as a central interactive database capable of communicating information with other systems for facility management or urban planning. At present, linking BIM to field data acquisition and database management systems are missing in the real-time project information management processes. Improved Fitting Algorithms: Fitting algorithms are used to leverage the point cloud engine. These algorithms are generally strong but can be improved. Update of BIM Where Legacy Data Exists: If as-built 2D drawings or 3D models exist, the first step would be converting geometry-based legacy data to BIM, where more intelligence could be added. Then geometric and spatial validation is done using the scanned data, however, this process is currently manual and labor intensive. If algorithms can be developed to automatically detect the deviations between what is documented and what is built, it will drive down the cost of validating as-planned information tremendously. Creating of BIM Where Legacy Data Don t Exist: In cases, where there is no legacy information (for example, historic preservation projects), smart algorithms could be developed for automatic object recognition and creation. However, this is a very ambitious goal, but if achieved, it would be revolutionary. 6. Potential Applications If these systems and associated processes can be improved and in some cases developed, there are numerous potential application areas these technologies could be applied (such as management of cities, emergency & disaster management, energy simulations, policy decisions, facilities management, construction progress measurement, change detection). Some of these application areas are discussed in detail. - Search & Rescue: Coupled with robotics, field data acquisition systems could be used for search and rescue. Search and rescue teams during natural or manmade disasters (fires, earthquakes, tornados, terrorist attacks) could use these systems for understanding what is beyond objects/obstacles or for human detection. - Security: Some combination of these tools could be used for observing type spaces, for flight monitoring/public transportation security and for diplomatic security. 9

10 - Renovation: There are more buildings that need to be renovated than to be built. Today, trades spend substantial amount of time for mapping existing conditions. (i.e. mapping ceiling tiles for routing new equipment for hospitals) These tools could be used for periodical orientation and update of existing conditions. For example, seeing what is above the ceiling without disturbing it has potentially huge application. - Inspection: If one can look beyond the surfaces (almost take an MRI of a building, i.e. looking at the pipes in buried or tensioning cables in the concrete) these systems could be used for inspection in construction or occupancy. - Discovery: these tools can be used for discovery of things we currently don t know like 3D mapping of an old mine by sending a robot equipped with a laser scanner inside the cave done at Robotics Institute at Carnegie Mellon University. (Robotics Institute website 2008) - Construction Progress Monitoring: If implemented properly, one can use these solutions for validating real time construction sequencing, solving construction layout coordination, and real-time progress measurement. - Facilities Management: As as-planned models updated simultaneously with real construction progress, at the granularity that is required for operations, these models could be used during occupancy stage for facility management. Then accurate cost projections, simulations from the model, space studies could be done without interrupting the activity in the space. - LEED Accreditation: The whole effort of creating existing buildings models can be used for LEED accreditation for LEED Existing Buildings. One can identify recyclable materials, which might provide tax credits to the owner. - Change Detection: If these tools can identify and visualize change detection through the use of robotics and multi sensors, they can be used for updating awareness about what is out there and if anything is of a concern. Change detection can also be used for maintenance issues such as detecting broken windows, water stain, or missing equipment. - Entertainment: The fusion between the real world and virtual world has all sorts of value, such as value of awareness, ability to visualize interferences, entertainment value as well as commercial value (application of this technology in gaming or real estate: walking onto buildings and houses for virtual house hunting) 7. References Akinci B., Boukamp F., Gordon C., Huber D., Lyons C., and Park K. (2006). A formalism for utilization of sensor systems and integrated project models for active construction quality control. Automation in Construction, 15(2), Bosche F., Haas C.T., (2008) Automated retrieval of 3D CAD model objects in construction range images, Automation in Construction 17 (2008) Bosche F., Haas C.T., (2006) Investigation of Data Formats for the Integration of 3D Sensed and 3D CAD Data for Improved Equipment Operation Safety, International Construction Specialty Conference, Calgary, Alberta, Canada, May 23-26, 2006 Department of Commerce, Bureau of Economic Analysis website: accessed on 12/16/

11 Domdouzis K., Kumar B., Anumba C.,(2007) Radio-Frequency Identification (RFID) applications: A brief introduction, Advanced Engineering Informatics 21 (2007) Eastman C., Teicholz P., Sacks R., and Liston K.(2008), BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors. Wiley, El-Omari S., Moselhi O.(2008), Integrating 3D laser scanning and photogrammetry for progress measurement of construction work, Automation in Construction 18 (2008) 1-9 Ergen E., Akinci B.(2007), An Overview of Approaches for Utilizing RFID in Construction Industry, st Annual RFID Eurasia, Sep 2007, Istanbul, Turkey Gramegna T.(2006), Automatic construction of 2D and 3D models during robot inspection, Industrial Robot: An International Journal 33/5 (2006) GSA Building Information Modeling Guide Series: 03 (May 2007), accessed on 10,10, 2008 at 12:11 pm. Kiziltas S., Akinci B., Ergen E., Tang P., Gordon C.(2008), Technological assessment and process implications of field data capture technologies for construction and facility/infrastructure management, ITCon Vol.13 (2008), pg. 134 Kwon S., Bosche F., Kim C., Haas C.T., Liapi K.A.(2004), Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds, Automation in Construction 13 (2004) Robotics Institute, Carnegie Mellon University website: accessed on 12,19,2008 at 12:13 am Teicholz, Paul (2001), U.S. Construction Labor Productivity Trends, , Paul M Goodrum and Carl T. Haas, Journal of Construction Engineering and Management, Volume 27, Issue 5, pp , September / October Tserng H.P., Dzeng R.J., Lin Y.C. & Lin S.T. (2005), Mobile Construction Supply Chain Management Using PDA and Bar Codes, Computer-Aided Civil and Infrastructure Engineering 20 (2005) Wang L.C., Lin Y.C., Lin P.H. (2007), Dynamic mobile RFID-based supply chain control and management system in construction, Advanced Engineering Informatics 21 (2007)