Computing in Civil Engineering (2009)

Individual appliances' electricity consumption is automatically disaggregated from a single custom metering system on the main feed to an occupied residential building. A data acquisition system samples voltage and current at 100 kHz, then calculates real and reactive power, harmonics, and other features at 20Hz. A probabilistic eventdetector using the generalized likelihood ratio (GLR) matches human-labeled events to the time-series of features. Machine-learning classification was most successful with the 1-nearest-neighbor algorithm, correctly identifying 90% of the laboratory-generated training events and 79% of validation examples. The challenge of obtaining adequate training data for the real-world home leads to the development of the Wire Spy, a wirelessly-networked event detector with an inductive sensor which clamps to the cable of an appliance.

In the near future mobile robots accomplishing such tasks as delivery of goods, assistance for handicapped people and surveillance will become increasingly available. The aim of this paper is to show the disparity that exists at present in modeling buildings for the purposes of two domains: Architecture, Engineering & Construction (AEC) and Mobile Robotics (MR). Digital product and process models developed in the AEC-domain give the user a comprehensive insight into the components and functions of the building but they neglect the needs of mobile robot operating autonomously inside the building. On the other hand, researchers in the MR-domain seem to be unaware of the results achieved in the AEC area. During the last decades the main effort in MR was devoted to the autonomous navigation. This resulted in efficient and robust procedures of acquiring floor maps from sensor readings. Further progress requires the robot to understand semantics of the surrounding world. Based on our previous research in the graph-oriented support of the AEC-design, we propose a digital model of the building that will enable the robot to recognize the functionality of particular spaces, to transform CAD-based floor plans into graphs reflecting topology and function of the building, to plan the execution of tasks posed by the human operator and to navigate inside the building.

There is a growing realization that the construction project delivery process can be significantly improved through the adoption of intelligent construction systems. These systems seek to embed intelligence in the construction process such that, it is easier to track and make informed decisions at all stages of the process. In adopting the concept of Intelligent Construction, it is imperative that a System of Systems (SoS) approach is explored so as to maximize the synergies inherent in the underlying sub-systems. This paper describes the adoption of a SoS approach to Intelligent Construction Systems (IConS). A methodology that includes comprehensive literature review and case study analysis is followed to identify the characteristics of the SoS approach and to learn from its applications in other domains. In the light of this analysis, a general SoS framework has been developed for IConS. For the findings, five main characteristics of SoS are identified. These characteristics are later mapped to the properties of the AEC domain to reveal that the AEC domain is inherently complex and can be considered to comprise SoSs.

The development of new sensing technology is able to collect more data at a higher precision and enables rapid inspection and monitoring of construction sites. Since the metadata is generally not structured, it is difficult for engineers to choose a proper monitoring method that satisfies their needs for decision making. Monitoring systems that function as a real-time data collection, storage, and data analysis system would be far more efficient than performing these tasks manually. Due to illogic data structure, information losses commonly occur when data is transmitted from one source to another. Minimizing such information losses and enhancing information interchange over the internet requires specific markup language to perform rapid automated feedback. The presented research developed a conceptual and logical model for monitoring systems. Unified Modeling Language (UML) was applied in the conceptual model design for a particular construction work task. Finally, in the logical model design phase, the object model is mapped into XML.

This paper presents the design of a system that is being developed to utilize wireless local databases for making necessary local information readily available to search and rescue teams following a natural disaster. The Search and Rescue Data Access Point (SR-DAP) system is designed for local data storage and retrieval using sensor nodes deployed on the exterior sides of buildings. The paper reports on the results of the interviews conducted with experts to identify types of information items that are needed during search and rescue operations. Also, memory requirements for identified information groups are given. The implementation details of the developed system are provided and initial findings of data reading/writing tests performed in a laboratory environment are presented. The technical feasibility of using wireless sensors for local data storage and retrieval is discussed in the light of the initial findings.

Early detection of actual or potential schedule delay in field construction activities is vital to project management. This entails project managers to design, implement, and maintain a systematic approach for construction progress monitoring to identify, process and communicate discrepancies between actual and as-planned performances as soon as possible. To achieve this goal, this research focuses on exploring application of unordered daily progress photograph logs — available on any construction site — as a data collection technique. Our approach is based on computing- from the images themselves- the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built site using daily progress photographs and superimposition of the reconstructed scene over as-planned 4D models. Within such an environment, progress photographs are registered in the virtual as-planned environment allowing a large unstructured collection of daily construction images to be sorted, interactively browsed and explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, location-based image processing technique to be implemented and progress data to be extracted automatically. The results of progress comparison between as-planned and as-built performances are visualized in a 4D Augmented Reality environment (D⁴AR) using a traffic light metaphor. We present our results on two ongoing construction projects and discuss implementation and future potential enhancement of this new technology in construction.

Gathering data for improving on-site operations is an essential and difficult task in construction. Among other methods, videotaping has long been used in construction to analyze construction operation. However, in the absence of an efficient video interpretation method, tedious manual reviewing is currently still required to extract productivity information from these videos. This paper presents a study on developing an intelligent video computing method to interpret videos of cyclic construction operations automatically into productivity information. More specifically, this research focuses on designing a mechanism for furthering the crosstalk between the prior knowledge of construction operations and computer vision techniques. It uses this mechanism to guide the detection and tracking of project resources as well as work state classifications and abnormal production scenario identifications. Experimental results from preliminary studies have shown the potential of the proposed video interpretation method as an improved productivity data analysis method.

This paper presents a method for measurement-system design through criteria related to model based structural identification. Using a multi-model approach and results from previous research carried out at EPFL, an improved algorithm is proposed. The algorithm accounts for various types of sensors having different accuracies and taking different kinds of measurements. The algorithm selects sensor types and locations that minimise the number of non-identified candidate models. The results show that the approach provides an alternative to selecting and placing sensors using engineering experience alone, and that a scientific approach based on sensor characteristics and modelling error is feasible. A single span composite bridge is used to illustrate the algorithm. It is shown that adding more than 9 sensors, from a possible set of 34, will not provide further useful information for structural identification.

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.

Nowadays, many buildings are equipped by Building Automation Systems, which utilize numerous sensors acquiring large amounts of data. Such data is useful to assess the performance of existing building systems and hence to support a variety of decisions made during facility operations and maintenance. However, sensor data alone typically does not support analyses of conditions of a facility since there is a need to understand the context under which data is collected. Therefore, data about sensors, and data collected by sensors and the corresponding spatial contextual information from a building information model are required to be analyzed in an integrated way. Although existing standards provide specifications to represent some aspects of sensor metadata (e.g. SensorML) and building information (e.g. IFC), an approach, which integrates information about sensors and building elements, is still needed. In relation to this, the authors first discuss requirements for a model integrating both sensor metadata and building information and then present possible approaches for developing such a model. The discussions include a synthesis and analyses of existing standards for supporting representation of sensor metadata and building information models, and overview of a prototype implemented to generate integrated models of sensors and building information.

In project type product production the need for feedback creates the task of collecting field progress measurements. This task may be completed by experienced engineers or assigned to interns and field hands. Whichever the case, the process of field production feedback is inherently inaccurate and may lead to unforeseen project events. It is possible that other industry divisions have similar issues and used solutions applicable to construction. This paper summarizes a case-based analysis of responses from a focus group. The conclusions drawn from the focus group are then further defined through a questionnaire survey. A majority (55% to 75% @95% confidence level (CL)) of construction professionals (i.e., salaried) do not use software tools beyond Microsoft office, which indicates software tools (initial/reorganization/training/support) costs are more than the perceived benefits provided. The Programmable Logic Controller (PLC) system collects measurements from sensors and links these measurements to an activity's programmable recipe formula and monitoring report. While, this system may not be applicable to most construction sites, components and concepts may be.

Indoor localization, a process of determining the location of a user or an object, is important for different types of applications, such as localization of first responders inside a building, ubiquitous computing and tracking of materials in an enclosed warehouse. Different types of technologies based on ultrasound waves, infrared waves and radio signals are currently used for indoor localization. The technology based on radio signal strength is widely used as a mechanism for localization. Currently existing technologies are not able to provide localization accurately within a few meters as they are prone to different types of signal propagation problems, and they often rely on prior data on localization. To improve localization accuracy and avoid collecting prior localization data, the authors investigated an alternative technology which combines an inertial measurement system and correction points for indoor localization. The authors have developed and tested an approach based on a Kaiman filter to fuse location data from correction points and an inertial measurement system for outdoor localization. The outdoor test results of the presented approach showed that accuracy up to a few meters (3 meters) can be achieved. The same approach can be used for indoor localization with minor changes.

In Northerly climates snowstorms are common occurrences that pose severe hazards to drivers. Thousands of lives and property worth of millions of dollars are lost annually. This paper presents a novel method of identifying road segments that pose special hazards to drivers during and after snowstorms by using Light Detection and Ranging (LiDAR) data and a Geographic Information System (GIS). The methodology presented herein can be used for prioritizing roadway snow clearing efforts by identifying the most hazardous road segments. The proposed methodology uses the spatial analysis functionality provided by GIS software and three-dimensional (3D) data obtained from an airborne LiDAR scanning system. The computing focus of this paper is on spatial data analysis. The data include road centerline 3D coordinates (to identify roadway crests and troughs), aspect data (to determine ground surface slope), and sunlight data (to determine ground surface illumination values and identify shaded areas). Combining and analyzing these data sets identify potentially dangerous north facing, shaded, steep slopes over large geographic areas.

After earthquakes, licensed inspectors use the established codes to assess the impact of damage on structural elements. It always takes them days to weeks. However, emergency responders (e.g. firefighters) must act within hours of a disaster event to enter damaged structures to save lives, and therefore cannot wait till an official assessment completes. This is a risk that firefighters have to take. Although Search and Rescue Organizations offer training seminars to familiarize firefighters with structural damage assessment, its effectiveness is hard to guarantee when firefighters perform life rescue and damage assessment operations together. Also, the training is not available to every firefighter. The authors therefore proposed a novel framework that can provide firefighters with a quick but crude assessment of damaged buildings through evaluating the visible damage on their critical structural elements (i.e. concrete columns in the study). This paper presents the first step of the framework. It aims to automate the detection of concrete columns from visual data. To achieve this, the typical shape of columns (long vertical lines) is recognized using edge detection and the Hough transform. The bounding rectangle for each pair of long vertical lines is then formed. When the resulting rectangle resembles a column and the material contained in the region of two long vertical lines is recognized as concrete, the region is marked as a concrete column surface. Real video/image data are used to test the method. The preliminary results indicate that concrete columns can be detected when they are not distant and have at least one surface visible.

Efficient allocation of resources is critical to disaster response operations. Difficulties within resource allocation operations for disaster response have been identified. There is lack of detail information, including geospatial attributes, of available resources. Moreover, current resource request procedures are not systematically organized with automation. In addition, there is no automated decision model that takes input demand from real-time resource requests and input supply from geospatial resource databases. A prototype platform for resource allocation using Geographic Information System is developed which includes 1) a prototype geospatial database which serves as a resource repository 2) client-server architecture software for resource request through mobile devices, and 3) a decision model for allocation of available resources to demand locations. The supply and demand information will be automatically fetched as inputs for the decision model, and result decisions will be produced and shown through the GIS platform to logistics managers, decision makers, and first responders.

The Last Planner System (LPS) has since its introduction in 1994 become a widely used method of AEC practitioners for improvement of planning reliability and tracking and monitoring of project progress. However, the observations presented in this paper indicate that the last planners and coordinators are in need of a new system that integrates the existing LPS with Virtual Design and Construction (VDC), Enterprise Resource Planning (ERP) systems, and automatic object identification by means of Radio Frequency Identification (RFID) technology. This is because current practice of the LPS implementations is guesswork-driven, textual report-generated, hand-updated, and even interpersonal trust-oriented, resulting in less accurate and reliable plans. This research introduces a prototype development of the VREL (VDC + RFID + ERP + LPS) integration to generate a real-time updated cost + physical % complete based weekly work plan and look ahead plan with product progress visualization based on programming intelligence. VREL allows LPS users to enable real-time information generating/sharing, more accurate and reliable planning, immediate proactive action execution, machine-to-machine process, and clear progress understanding by product visualization.

The high cost of skilled workers in labour-intensive production industries has motivated senior production managers to identify the best allocation strategy of crews of workers to appropriate processes. The aim of this paper is to develop a crew allocation system using Genetic Algorithms-based simulation modeling. The objective is to optimally allocate crews of workers to labour-intensive production industries to minimise labour costs. In this paper, a simulation-based Genetic Algorithm (GA) system dubbed "SIM_Crew" is developed to simulate the physical processes of a labour-driven facility. The GA is tailored to be embedded with the developed simulation model for improved solution searching. A chromosome structure is designed to apply such problems and a probabilistic selection of promising chromosomes is applied as a selection strategy, n-points crossover and mutation strategies are designed to add more randomness to the searching process. A case study in the precast industry is presented to demonstrate and validate the model.

There has been a recent surge in the use of IT Applications in the Construction Industry; however, with the introduction of Smartphones these IT applications can be brought directly into the hands of construction personnel on the job site. This paper describes the development of two such applications, namely "Construction Equipment Finder" (CEF) and "Be Safe" on the iPhone development platform. While CEF focuses on enabling project personnel to find the equipment rental location closest to the jobsite without having to leave the work area, Be Safe's focus is to provide access to safety-related information to the on-site construction personnel. The paper describes the design and development of the above applications using the iPhone OS Development Platform. Initial results show that the development platform has the potential to improve information access on jobsites by facilitating the development of application that provide enhanced information retrieval in multiple formats.

Calibration of a camera system is a necessary step in any stereo metric process. It correlates all cameras to a common coordinate system by measuring the intrinsic and extrinsic parameters of each camera. Currently, manual calibration of a camera system is the only way to achieve calibration in civil engineering operations that require stereo metric processes (photogrammetry, videogrammetry, vision based asset tracking, etc). This type of calibration however is time-consuming and labor-intensive. Furthermore, in civil engineering operations, camera systems are exposed to open, busy sites. In these conditions, the position of presumably stationary cameras can easily be changed due to external factors such as wind, vibrations or due to an unintentional push/touch from personnel on site. In such cases manual calibration must be repeated. In order to address this issue, several self-calibration algorithms have been proposed. These algorithms use Projective Geometry, Absolute Conic and Kruppa Equations and variations of these to produce processes that achieve calibration. However, most of these methods do not consider all constraints of a camera system such as camera intrinsic constraints, scene constraints, camera motion or varying camera intrinsic properties. This paper presents a novel method that takes all constraints into consideration to auto-calibrate cameras using an image alignment algorithm originally meant for vision based tracking. In this method, image frames are taken from cameras. These frames are used to calculate the fundamental matrix that gives epipolar constraints. Intrinsic and extrinsic properties of cameras are acquired from this calculation. Test results are presented in this paper with recommendations for further improvement.

For the last several years, many research efforts have focused on enabling and advancing information technology to enhance work efficiency and collaboration among field workers by providing mechanisms to deliver pertinent information required for decision-making in a timely manner (Halfawy et al. 2001). Evolving technologies such as location-aware computing and interoperable product models and project databases offer significant potential of improving traditional, time-consuming, manual information retrieval processes, and supporting important decision-making tasks in the field. Field construction tasks such as progress monitoring, inspection and others require access to a wealth of textual and graphical project information. Rapid and convenient access to such project information, through continuous position tracking of mobile users (i.e. engineers, managers, and inspectors) can lead to significant cost and time savings due to the accuracy and immediacy with which relevant project information can be made available to field personnel. This paper describes the results of experiments conducted at the National Institute of Standards and Technology (NIST) to evaluate the capability of interoperable product models and project databases, such as the CIMsteel Integration Standards (CIS/2) product model, for automated context-aware information access and retrieval in construction and other engineering environments.

Domain ontology, encompassing both concepts and entities, along with their relations and properties, is a new medium for the storage and propagation of domain specific knowledge. For all of its associated advantages, a great deal of effort must still be expended during ontology construction, including collecting the domain-related vocabularies, developing the domain concept hierarchy, defining the properties of each concept and relationships between concepts. In recent years, several engineering handbooks have described detailed domain knowledge via organization into categories, sections, and chapters with indices in the appendix. In this paper, an approach is proposed with a view to extracting concepts, entities, properties and relationships from a handbook of a specific domain. The extracted information can then be reorganized and converted into OWL (Web Ontology Language) format to represent the domain ontology. The generation of an earthquake engineering ontology from an Earthquake Engineering Handbook is used to illustrate the proposed approach.

The complexity of planning after a catastrophic incident is significantly influenced by disruption of critical infrastructure like transportation and telecommunication systems, making it very difficult to achieve a credible common operating picture to respond to the disaster. To increase situational awareness, it is important for first responders to acquire and share building information like structural damage and probability of further collapse of buildings at the disaster site. Current paper-based communications take 24 to 48 hours (or longer) to reach command centers causing response activities to begin without an assessment of critical needs. This paper proposes a framework that allows structural engineers to seamlessly exchange critical information about affected buildings with first responders based on establishment of Mobile Ad Hoc Network (MANET). In a mobile wireless ad hoc network, computers (nodes) in the network cooperate to forward packets for each other, due to the limited wireless transmission range of each individual node. Since MANET do not require any communication infrastructure, they are ideal for communicating during disasters. Adopting such a framework will help planners prioritize search and rescue operations more effectively.

This paper presents AsphaltOpen, a visualization tool for Hot Mix Asphalt (HMA) paving operations. AsphaltOpen visualizes site-specific GIS data and GPS path tracing of equipments' motions with the HMA's physical properties, such as the cooling of the asphalt layer and its compaction. AsphaltOpen stores all data in a central database to allow the company wide access on previous asphalting operations. AsphaltOpen's interface is based on action research that we conducted with HMA paving operators to ensure the application's usability. AsphaltOpen offers an easy-to-use tool for HMA paving companies to visualize previous asphalting operations. In this way, it opens up new ways for HMA paving contractors to improve and professionalize their paving operations.

There is a lack of intelligent digital data for effective support of improvements and/or maintenance of existing United States Army facilities. The move by the Corps of Engineers in mandating the use of Building Information Modeling (BIM) is the first step towards getting intelligent as-built engineering data into the hands of the installation's Directorate of Public Work (DPW). However, this only addresses new facilities, which represent a very small portion of the DPW's facility information needs. The real need is to find the most efficient method of capturing existing facility as-built information so that the DPW can maintain its daily operations and maintenance as well as real property asset management activities. This paper presents a comparative analysis of alternative technologies for capturing as-built information for existing facilities at Fort Lewis in Washington State. Field surveys were performed on four buildings over a period of four weeks to capture facilities data based on the COBIE (Construction Operations Building Information Exchange) standard. Data was captured using (1) paper forms and computer data entry, (2) laptop computers, (3) digital pens, and (4) handheld computers. Hand-held computers were determined to be the most efficient technology (cost and time) under the experimental conditions of the study. However, the most important findings of this study do not reside in the specific issues related to each technology, but in the lessons learned regarding three major challenges that uncovered during the experimental process: logistical issues, operational issues, and user interface issues.

This paper presents a new application of stiffness corrector in predicting the behaviour of vertically loaded masonry wall panels more accurately. Corrector factor values are produced from the comparison between the experimental and theoretical displacements at individual points on the wall panel, and model the variation in masonry properties at local regions within the wall panel and the effect of panel boundaries on the regions. For the application of corrector factors from the base wall panel (a full-scale experimental panel) to an unseen panel, the corresponding rule is applied to match similar zones between the base panel and the unseen panel whose configuration is different from the base panel. It verifies that the application of the stiffness corrector factor can make an average 21% improvement in the prediction of the magnitude of displacements of the wall panel using the finite element analysis (FEA) method, when compared with the FEA results without using the corrector. This study further indicates that the experimental information of the full-scale structural model can be fed back the analysis of the new structure by a proper technique and this could open a way for the application of the existing experimental records in structural analysis.

The appropriate selection of materials and systems to reduce environmental impact, minimize harmful emissions and prevent the depletion of nonrenewable resources is one way of alleviating the impact of building construction or retrofitting. The construction industry and the community in general will benefit from an integrated tool that will help optimize the process of material, equipment and systems selection at every stage of the construction project life cycle, making it useful to designers, engineers, contractors, and facility managers (A/E/C/FM). This paper describes the developmen of the tool, and how it would assist project stakeholders in making these selection decisions for new and existing buildings considering the best value with regard to the applicable green building rating system score. Using Building Information Modeling (BIM) in this approach will allow owners, designers and contractors to choose the item on the spot from a 4-D representation. This way, decisions can be made timely and with effective communication to cut down on additional costs that are inevitable in large-scale construction projects.

Incorrect depth to bedrock (DTB) estimations can result in a layer moduli backcalculation error of more than 100%. It would be the easiest and least expensive approach to determine the DTB from the falling weight deflectometer (FWD) data itself. This paper demonstrates how to develop a DTB prediction model by use of the group method of data handling (GMDH). Records from 84 locations at 10 sites in Texas were employed to develop the DTB prediction model (70 records for model development and 14 records for model verification). No previous study had collected such a large body of data. The findings show that the R² of the proposed model for the verification data is 0.6635. Given the complexity of geotechnical structures, the model developed in this study provides reasonable DTB prediction. The model and algorithms proposed in this study provide a good foundation for further refinement when additional data becomes available.

Critical infrastructures are important national assets for producing or distributing continuous flows of essential goods or services. When one critical infrastructure shuts down due to an external disruption, it can be expected that other critical infrastructures that need goods or services provided by the discontinued critical infrastructure will stop shortly, exacerbating the damage caused by the external disruption. Past research has categorized critical infrastructure interdependency (CII) into four relationship types and has proposed several methods to model CII and its effects. This paper presents a knowledge discovery process for CII that can be used to extract frequent patterns of critical infrastructure failure sequences triggered by external disruptions and/or CII. The knowledge discovery process, including integration of critical infrastructure failure records and transformation into the data format needed by the data mining algorithm, is described. The analysis results of real critical infrastructure failure records in certain areas of Taipei city are addressed, followed by discussion of a mitigation approach to stopping the possible future failure sequences of CII. Disaster mitigation officials can employ the proposed approach to explore CII and to design countermeasures when a disaster hits certain areas.

This paper sets out to develop and test a structural equation model of passenger continuance behavior for a high-speed rail (HSR) service. A passenger satisfaction index (PSI) is developed to serve as a reference for continuous quality improvement. This study empirically examines the interactions and hypothesized relationships among service Quality, passenger Satisfaction, and Loyalty (QSL) using the mediator constructs of corporate image and passenger complaints. A survey was administered for the Taiwan High Speed Rail (THSR) and Korea Train eXpress (KTX) to understand the perceptions of the passengers with regard to quality and service performance. Both of the surveyed services are build-operate-transfer (BOT) projects currently in the franchised operation stage. The empirical results verified that the QSL chain is significantly influenced by the introduction of mediating constructs. The proposed PSI can help the corporations deploy an effective marketing strategy, achieve continuous quality improvement, and efficiently manage customer relationships to improve passenger satisfaction and increase confidence in the HSR systems.

Sustainability and environmental protection are among the emerging policies within construction industry. Technologies that facilitate assessment, modeling and communication of sustainability metrics are promising to help managers understand the dynamics of the driving factors affecting the environment. These technologies also can facilitate decision making in order to minimize impacts of those processes. However, the accuracy and efficiency of the models that are used still need significant improvements. In infrastructure projects, transportation has a major role in the impact to the environment, specifically air quality. Current technologies that are used to model transportation are inherently spatially dependent, and since such information could be used to analyze air quality and gas emissions, these technologies could act as proper baselines for visualizing sustainable construction. Among these technologies, Geo-spatial Information Systems (GIS) have shown their efficiencies in visualizing, manipulating, and analyzing such spatial data. In this research, we present a GIS-based model that helps visualize the distribution and dynamic variations of green house gas emissions during the construction phase of an infrastructure project.

The focus of this paper is the highway maintenance budget allocation decision-making process needed to achieve expected levels of service or performance targets. Establishing these performance targets must take into account the impact that such target levels can have on the financial resources required to achieve them. This paper reports on the sensitivity of the Cost/Benefit (C/B) ratio as a function of the pavement performance targets set by State DOTs. The C/B ratio is in itself a function of the yearly highway maintenance expenditures and the achieved Level of Service Index (LOSI). The LOSI has been modeled using three different functions, namely: step-wise, linear, and quadratic. The sensitivity of the C/B ratio to the performance targets is compared across the different functions used to model the LOSI. The model in this paper builds on the engine reported at the ASCE's International Workshop on Computing in Civil Engineering in 2007 and the findings augment those reported at the European Group's Intelligent Computing in Engineering Conference in 2008 where the sensitivity of the C/B ratio was shown as a function of the budget size. In addition, it builds on the findings reported at the TRB's Highway Economic Requirements Modeling and Data Integration Conference in 2008 where the sensitivity of the C/B ratio was shown as a function of the budget size, the budget profile, and the budget allocation strategy. The paper describes the results of six cases, namely; one step-wise, one linear and 4 quadratic. Our findings show that the best C/B ratios are achieved when quadratic functions are used to model the LOSI, which are fast in rewarding good performance.

Sustainability and building operation cost are the main drivers in attempts to reduce building energy consumption. According to DEFRA (2004) the construction industry is one of the major contributors of CO2 emissions and carbon reduction becomes a global target and a goal has been set by the UK government to reduce carbon emission by 60% by year 2050. In this research, a decision making tool for outline design process called 3D-Environmental Assessment Trade-off Tool (3D-EATT) is creating for building facility managers and environmental consultants, and will be utilised as a platform to allow materials trade-off with the Environmental Impact Assessment, Life Cycle Assessment and Life Cycle Cost Assessment to compare the cost benefit of both sustainable and ordinary materials over a normal building life cycle of 60 years. These assessments are currently fragmented and do not comply with the idea of holistic sustainable assessment as suggested in Agenda 21. The algorithm will be developed in this research for optimisation within the 3D-EATT tool. This paper will focus on the development of the trade-off algorithm of 3D-EATT and to demonstrate the trade-off process. An overview of how the 3D-EATT result to be delivered to the stakeholders via Building Information Modelling software will also be discussed.

Commercially available blinds are occasionally part of the building's thermal and lighting environmental control systems. Inappropriate application of blinds increases the use of electrical lighting, cooling loads for a building and consequently, the total energy consumption. The purpose of this research was to develop a new, automated, split control system for adjusting blinds. This system adjusts the blinds for different tilt angles at a particular point in time depending on the position of the blinds within the height of the window. The research objectives were: (1) developing a framework for the automated split control of blinds, (2) developing a control algorithm for a split system, (3) validating the split control system by conducting a case study for two automated blind control systems (conventional and split). Computer simulations were performed to calculate the illuminance levels in a building and energy consumption as a result of the application of these two blind systems. The research findings showed that the split controlled blinds provided higher illuminance in the back of the room, lower energy consumption for electrical lighting and thus, energy savings for a building compared to conventionally controlled blinds.

In this paper, we consider drinking water pipe breakage as a process in space and aim to identify patterns that can subsequently be used to improve the understanding and modeling of the breakage process. For instance, patterns obtained by spatial clustering algorithms might indicate spatial correlation or lead to the identification of good predictors of breakage within clusters. More specifically, our goal is to assess the efficacy of applying spatial clustering methods to the exploratory analysis of water distribution pipe breakage. This study indicates that the DBSCAN algorithm, which is a density based clustering method, when adapted to consider network shortest path distances, is suitable for identifying spatial clusters of breaks, based on the density of the spatial distribution of break events. An analysis of the limitations created by the subjective nature of parameter choice, and the potential usefulness of the clustering results, is provided along with future work directions.

Infrastructure systems are becoming more interconnected and complex, increasing their vulnerability to different types of disruptors. A tendency affecting the performance of infrastructures in developing countries is the rapid urban growth which produces a concentration of population in areas with existing infrastructures. This concentration of population leads to additional traffic on roadways and additional use of power, which puts intense pressure on the transportation and electric systems, respectively. This study first demonstrates the use of a system dynamics model to evaluate the effect of urban growth on the electric system. A user equilibrium approach is then used to assess the effect of disruptions on the electric system on the performance of the transportation infrastructure. The models developed are verified and validated using data from a developing country.

Product data management (PDM) systems are well established in the mechanical engineering industry. Here, they form the standard solution for the central storage of all data concerning a product and the processes involved in its fabrication. Especially the consistent management of CAD models including sophisticated versioning techniques and access rights management as well as the integrated workflow management are attractive features for using PDM systems also for civil engineering projects. The paper investigates the technical concepts behind PDM systems and discusses in detail the potentials and obstacles for using them in civil engineering projects.

A cellular automation (CA) model of pedestrian movements with 3D visualization is implemented for facility planning. The CA methodology is efficient for large-scale and repeated simulations and describes pedestrian behaviors adequately. Different from the previous studies that attempt to calibrate their models at microscopic level or ignore model calibration completely, this paper calibrates the model with data at macroscopic level (flow, density, and speed). The calibrated pedestrian model is useful for optimal facility design because it reflects the complicated interactions between pedestrians and the facility. After candidate facility designs are evaluated with the pedestrian models, a two-stage procedure is adopted for the statistical selection of the alternatives. The numerical example of a transportation terminal shows that the best design can be selected with a pre-determined probability of correct selection and targeted difference between the best and the second best systems. Moreover, required numbers of repetitions are calculated and conducted to avoid incorrect selection or unnecessary repetitions. Therefore, the procedure proposed is an effective and efficient tool for facility design.

People and knowledge represent the corporation's strategic asset. Engaging mobile knowledge workers, managing and reusing corporate knowledge is still challenging. Nevertheless, it can lead to greater competitive advantage and higher quality products. This paper presents a working software environment that demonstrates how BIM (building information model) can be expanded to become a rich multimedia BKM (building knowledge model). We present the theoretical and practical points of departure that include: (1) knowledge management and re-use focusing on how a corporation can capitalize on its core competence by turning its knowledge into working knowledge? (2) Interaction design focusing on what are the cognitive interactions when creating new knowledge, augmenting and sharing building models? We demonstrate and discuss the benefits of the software environment with an industry project case study.

Parametric studies in parallel-computing is often considered as an "embarrassingly parallel" computation task that can be trivially parallelized and yield good parallel efficiency. However, this paper shows the parallel efficiency of such analyses can be further improved by the proposed scheme in typical cluster setups using commodity PC hardware. Numerical experiments show that the parallel efficiency of such computation task in finite element analyses can be improved by 81.6% and the peak disk usage can be reduced by 83.1%. The proposed scheme for enhancing parallel performance can be readily extended to design optimization problems, which has similar work patterns in parallel processing. Therefore, more efficient engineering analyses and designs can be achieved by employing schemes described in this work.

Recent data driven methods such as genetic programming (GP), model tree (MT), support vector regression (SVR), and locally weighted projection regression (LWPR) look attractive in carrying out a variety of tasks in coastal and ocean engineering. This paper provides an exposure to their applications made by authors to hindcast significant wave heights, to estimate wave surface spectral density and to inversely evaluate wind from waves. The field measurement used for this purpose mainly belonged to two selected locations in Arabian Sea along the west coast of India. The studies indicated that the various data driven methods can be effectively used to evaluate causal, temporal as well as inverse relationships between various wave and wind parameters and also for resolution of average wave parameters into a set of spectral densities. It is expected that the studies reported in this paper would pave the way for solutions to more complex and difficult tasks in coastal and ocean engineering such as estimation of uncertain design parameters and spatio-temporal evaluation of ocean environment.

This paper reports on research of utilizing ontological consistency checking in the design process for identifying potential conflicts and improving coordination and communication. Ontological consistency checking is based on the notion that an ontology could be used to conceptualize and represent the constraints and requirements of the building design. The paper describes how potential conflicts might be detected through an ontological consistency checking system and repair or modification of the inconsistencies can be accomplished. The implementation of the case study revealed guidelines for modeling an ontological representation and automatically identifying problems and conflicts in the design process of BIM (Building Information Modeling).

Different experience levels of estimators and whether a company is self-performing a work or subcontracting it out result in differences in the levels of details in which estimators would like to interact with historical production data. Different interaction patterns of estimators need to be understood when designing information systems that leverage historical production rates. This paper overviews an approach and a corresponding prototype system, which can store production data and contextual information items in a way that supports interaction with them in multiple levels of details (Kiziltas and Akinci 2008). The prototype system was used to conduct think-aloud walkthrough usability tests, within which users performed a specified task while talking about what they are doing during each step. This paper provides the results of these think-aloud walkthrough usability tests conducted with general contractors from heavy/civil and building construction industry. Analysis of the test results showed that estimators interact with historical production and contextual data at different levels of details depending on their experience levels and whether most of the work done by the company is subcontracted out or self-performed. It was observed that senior and lead estimators, who work in companies that self-perform, look into historical information in more detailed levels as compared to estimators, who work in general contracting companies that subcontract their work.

This paper documents the collaborative creation and utilization processes of a BIM model for a $320M public sector building construction project. What makes this implementation unique is the near non-participation in the modeling by the designer of record. Instead, a collection of other project participants (contractor, subcontractors, and construction manager) developed individual model elements and then worked collaboratively to develop a complete model. Project performance enhancements resulting from the completed model are also discussed, specifically trade coordination, construction sequencing, and clash detection results. Also of interest is how the model was able to assist in the dissemination and understanding of over 2500 completed contractor requests for information and 70 architectural bulletins generated in the first 25% of construction of the project. Conclusions documents lessons learned in the effort and compare the effectiveness and performance of this hybrid development modeling approach to more typical BIM model implementations.

Construction projects involve many uncertainties and risks in all phases. As a result, all types of construction projects, including transportation projects, have historically experienced significant cost increases. Project contingencies are important items in the cost estimate for compensating invisible and unforeseen risks against underestimating project costs and overrunning budgets. Generally, a contingency is represented as a fixed percentage of project cost (e.g., 5% of the contract value). However, it is not appropriate to apply this deterministic method to all construction projects because it provides an arbitrary percentage value based on only project costs. In this paper, a method for predicting the owner's contingency for transportation construction projects is proposed by identifying factors that affect the owner's contingency and using artificial neural network (ANN) techniques to predict these values. Transportation projects sponsored by the FDOT (Florida Department of Transportation) and completed from 2004 to 2006 are used for this study. The finding shows the viability of the artificial neural network approach to predict the owner's contingency for transportation projects. These predictions in turn can be used to better manage project contingency requirements and allow for additional projects to be brought online at a faster pace.

Accurate modelling of masonry wall panels has proved to be extremely difficult. One reason for this is the complex and highly composite nature of the material constituents that the masonry is composed of. In the previous research by the authors `corrector factors' were introduced to model variation in the properties of the masonry wall panels subjected to lateral loads. The concept of corrector factors was based on a numerical model updating approach, which was aimed at minimising the error between the measurement and analytical load deflection values over the entire surface of the panel. Due to the inverse problem nature of the model updating process, a number of scenarios could result in similar error. In other research by the authors, multi-objective evolutionary computing (EC) methods were used to identify best fit solution(s). One of the common approaches in EC is to combine objectives using a weighted sum of objectives. This approach needs careful selection of weights for various objectives which is an iterative approach and may add to uncertainties inherent to model updating techniques. This paper uses an interactive visualisation clustering genetic algorithm tool (the IVCGA) that identifies clusters of good solutions that best suit the problem. This tool allows users to interact with the system to explore the suitability of solutions within these clusters using their expert knowledge of the problem. This paper used individual runs of the IVCGA for each objective independently and identifies a `mutually inclusive region' for all objectives. The paper demonstrates that further exploration of solutions inside this mutually inclusive region can lead to identification of solutions that perform much better than the weighted sum approach and this could result in to finding the correct model for the problem.

Automated control of building systems is gaining increasing attention due to rising energy costs and enhanced environmental consciousness. Automated window blinds have the potential to save energy through striking a balance between daylighting levels and air conditioning load. Currently, most control systems for window blinds operate by setting a limit on the maximum direct solar radiation coming through the window (typically 100 W/m²) or on estimated glare discomfort. Trade-off between the energy required for artificial lighting and air conditioning is not currently evaluated through a global optimization framework. This paper presents a methodology for the control of window blinds using global optimization involving lighting and energy simulations making use of minimum number of sensors. Since these simulations take a lot of time, it is not recommended for real-time control. Instead, machine learning techniques are used to speed up the computations of optimal control actions. A combination of hierarchical clustering and perceptron networks is used to develop a generalized representation of potential solutions which are evaluated off-line. A case-study of an office building is used to evaluate the advantages of the approach. It is shown that significant improvement in efficiency is possible. The average prediction error was less than 15% for the selected case study.

Timely maintenance of highways is very essential to create uninterrupted traffic flow that results in reduced traffic delays and operating costs. Federal as well as State governments are struggling to maintain highways due to budget limitations and lack of decision making tools that help to prioritize the highways that require immediate maintenance. A large number of software has been recently developed based on Geographical Information System (GIS) for the management of traffic, road conditions, and safety data. The majority of the software provides an interface between data and maps. This paper describes the process of developing a GIS-based maintenance management tool that assists in making a reliable and accurate decision regarding road maintenance and management. Factors like traffic counts, pavement conditions, maintenance costs, and alternative roadways available were taken into account in this tool to make a decision regarding prioritization of road maintenance. Synthetic data was used to test and verify the tool. The importance of the tool is discussed and recommendations regarding future research are provided.

While the U.S. Army Corps of Engineers (USACE) conducted two pilot Building Information Modeling (BIM) designs in 2005, published their BIM Roadmap in 2006, and hopes to be "fully BIM capable" by 2012, the U.S. Air Force (USAF) has only recently (Fall 2008) embarked on their BIM journey. Indeed, the USAF was much later than the USACE in promulgating their support of BIM, but their delay has the benefit of increased industry BIM knowledge and awareness. Consequently, the USAF initiative has the possibility of greater immediate Return on Investment (ROI) than the Army's BIM initiative. This research assessed BIM's impact on the design and construction phase of the two Army Corps of Engineers pilot construction projects and compared that to the USAF BIM approach based on Dynamic Prototyping. By establishing baseline data according to the Unified Facility Criteria (UFC) standard use category codes used across the Department of Defense, these BIM-based projects were evaluated for statistically significant performance differences compared to recent projects of similar use according to three metrics. These three metrics were cost growth, schedule growth, and construction timeline growth. After evaluating the data it was determined that all the BIM-based projects analyzed experienced statistically significant differences in their productivity metrics compared to recently constructed projects of the same type that have similar use. However, there was no significant trend in either positive or negative performance in the construction phase.

To solve recent urban problems such as decline of downtown areas, aged society, congested roads, etc., light rail transit (LRT) is gaining much attention among municipal agencies and urban planners. For building consensus among stakeholders in the process of design and construction of LRT, a virtual reality (VR) system is often used. Usually, as the data size of a virtual urban model increases and more mobile objects are in the model, its presentation gives more realistic impressions. However, at the same time, this causes longer data loading time and low rate of frames per second (fps), which may hinder the actual use of VR for workshops and meetings. In this research, thus, requirements of VR systems for LRT projects were studied, and a methodology for development of VR model was proposed from the perspective of traffic, landscapes, and urban planning. This methodology was applied to an actual LRT project in Japan and showed its feasibility and practicality.

Building Information Modeling (BIM) is described by many as a solution to a number of inefficiencies in the construction industry. BIM is considered a new IT tool that implements new processes based collaboration between stakeholders. However, BIM's adoption has been slow due to the ambiguity surrounding its definition, purpose and business value. A survey of 202 Architecture, Engineering and Construction (AEC) professionals was administered to gain an understanding of the prevalent definition of BIM and to identify BIM's perceived impact on the success measures of construction projects. Overall, the survey results showed that BIM was most frequently perceived of as a tool for visualizing and coordinating AEC work and avoiding errors and omissions and improving the productivity, schedule, safety, cost and quality of construction projects. The results of chi-square tests showed that the combination of an individual's professional affiliation (defined by the survey group) and specific aspects of the individual's age, education and organizational role had a statistically significant relationship. Additional chi-square test showed that the perception of BIM's impact on one success measure (i.e. productivity, schedule, safety, cost or quality) was significantly associated with the perceptions of the other success measures. In particular, BIM was found to have the strongest perceived positive impact on the quality, cost and schedule of construction projects. The study's findings assist in clarifying the ambiguity surrounding BIM and identifying the mutual benefits from adopting BIM. This in turn supports the required stakeholder collaboration essential for BIM's long-term success.

This paper presents the development of a bidding game application that can be used in construction management for training students on bidding strategies and to improve students' decision making skills in preparation for real bidding processes. In the proposed approach, High Level Architecture (HLA) was used to develop a distributed model of the bidding process. This model is composed of different simulation components (federates) that can cooperate in a large simulation model (federation). Each federate represents a different role in the bidding process: general contractors, owners, market, bank, etc. These federates are responsible for simulating different activities in the bidding cycle, such as creating projects, awarding them, and tracking players' performance. In the final outcome, the player who creates the highest value is the winner. The model is developed in the COnstruction SYnthetic Environment (COSYE), which represents the next generation of integrated construction simulation models.

Facilities are dynamic in nature and as-built conditions of a facility frequently change during construction and operations. Correspondingly, building information models (BIMs) representing the facilities need to be updated. As a first step in identifying computational support for updating BIM, it is necessary to understand what types of changes occur during the life-cycle of a facility. With that goal in mind, the authors performed two construction case studies and observed work orders from daily maintenance and emergency response recovery activities during facility operations. This paper presents a synthesis and cross-comparison of these studies and highlights some initial patterns of changes occurring throughout the life-cycle of a facility. It also discusses some challenges associated with managing such changes and updating building information models accordingly, as well as how well commercially available systems address these challenges. This analysis emphasizes a need for computational support for change management.

Procurement of long lead engineered equipment is a complex process which requires the evaluation of several suppliers and project targets. This analysis is usually performed manually, takes long time and certain tradeoffs may be overlooked. This paper describes the development of a decision support tool which aims to integrate market forecasting or historical data on supplier performance with procurement targets. A supplier selection module uses the Aspiration Interactive Method (AIM) for providing rapid tradeoff analysis and points how each supplier is ranked in relation to the expected targets. The tool has been developed in Visual C#, in the form of simple and intuitive forms, with Microsoft Access as the back-end database. According to experienced managers, this tool brings intelligence to the traditional selection process. The ability to quickly generate what-if scenarios based on real data improves the quality of decision making and supports the selection of suppliers in the early phases of capital projects.

Safety training and management are among the constant tasks of project management on any construction site. A review of literature on the causation model and improvement factors for safety confirms that construction accidents can be preventable with consistent safety management and effective communication between managers and workers. One limitation of traditional safety management, however, is that workers may not be efficiently informed of hazardous locations and safety-related issues. In that respect, any technology that facilitates the analysis of safety and communication would have potential benefits in improving safety management. In this paper, application of the D⁴AR model (4-dimensional augmented reality) model, wherein a 4D as-planned model is superimposed on safety site photographs, and a visualization framework for site safety management are introduced. Visualization of safety acts as an effective communication tool to facilitate interactions between workers and managers and brings beneficial spatial information on and cognition of safety education and training. The proposed model is applied to an ongoing building project and the preliminary results are presented.

In this paper, we present the results of a four month action research study with a small builder that focused on exploring and improving the builder's financial decision making processes with IT solutions. The paper shows that, while existing IT systems, in general, did not integrate well with the financial decision making processes of the builder, it was possible to pragmatically develop a custom tailored system that only and specifically catered to the builder's needs. The action research study also shows that the AJAX (Asynchronous Java and XML) architecture of the cost control application supported the development of such a pragmatic system well. Overall, the study shows the technical feasibility of pragmatically developing custom tailored software systems closely together with practitioners using AJAX architectures. Overall, the paper provides a first indicator that such pragmatic development can overcome the problems that small construction companies have with supporting their financial decision making processes with IT systems.

Managing time and human resources efficiently in a project can lead to greater competitive advantage, improved product quality, less waste during the process, and team well-being. It is critical to use a decision support system to manage global teams to enable prediction and identification of potential problems. This paper presents a study how the use of a predictive simulation tool that models organizations, called SimVision, can improve the team process and performance in global project teams by (1) making the workload and backlogs of each team member visible, (2) exploring through what-if scenarios possible interventions to address current team challenges. We simulated in SimVision swift A/E/C project teams in the context of the A/E/C Global Teamwork course offered at Stanford in collaboration with university partners worldwide. We modelled three intervention scenarios to increase: (1) the frequency of the project meetings, (2) centralization of the decision making process, and (3) formalization of the communication among team members. We compared their impact on team performance. The simulations showed that a higher formalization, where ICT can capture the context of the meetings and allow reuse of knowledge, facilitates more effective communication, reduces rework and the risk to increase time to market, improves team well-being and satisfaction. The results of the simulation are validated through comparison with the actual outcome of the project.

Designing structures involves the fulfillment of topological properties. Based on these requirements a new technique is outlined which enables the direct modeling of the desired topology rather than wrapping the topological properties into a geometric model. This topological model will then be utilized in building up a consistent geometric model. The geometric model is stored as an extension of the topological model. Applying this technique results in a cellular decomposition of a 3-dimensional space. Given such a cellular decomposition, a method to consistently identifying building components and rooms is outlined. The completeness and validity of the assembled components and rooms is checked based on set theory in combination with the topological model. Trees are used to structure components and rooms. These trees are handled as extensions of components and rooms. The relations of components and rooms to the topological model allow an efficient navigation on every hierarchical level. The modeling technique described in this paper is still in a research phase. Basic concepts and preliminary results are presented.

Three case studies carried out on two types of building emergencies, flood and power outage, indicated that first responders need improved support to quickly identify which areas to protect in a facility during emergencies. Observations from these cases indicated that only information about vulnerable contents in a building and their locations was available, while actors also needed information on contents' vulnerability to different threats, since it is common that one threat will lead to another one (e.g., flood followed by power outage). Such information is needed since different types of contents and building systems are affected. Moreover, contents affected by a current threat (e.g., flood) might generate a new threat (e.g., power outage). Hence, there is a need for a vulnerability representation that can support such scenarios, minimizing information overload and time spent in gathering information from different sources. This paper presents the development of a representation schema that captures to what threats specific contents are vulnerable, as well as impact of a content malfunctioning during an emergency and location of contents in a facility. In order to develop this representation, we evaluated various subject-based classification techniques and implemented a faceted classification based representation, using data collected in the case studies. Results show that a faceted classification enables the representation of contents and their respective vulnerabilities as well as reduction of the search space from 8.88 to 99.41% (mean = 90.81%), depending on the number of facets selected for a given query.

This paper presents a vision on the development of a sustainable e-learning model (SELM) for promoting the education about a discipline, such as construction safety and health (S&H). The model is designed to capture the process knowledge individuals obtain from the learning activities so that the knowledge can be preserved, shared and reused. An example of such knowledge is the cognitive acknowledgements of useful educational resources (CAUER). Important model components include discipline and learner analysis, e-learning, search and social bookmarking technologies, knowledge codification, and discipline-specific resource repository. The socialization network, the discipline concepts, and the resource repository together present a unique source of domain knowledge which could have positive impacts upon the utilized search technology. This would generate a cyclic effect within SELM and enable the model's sustainability fundamentally. The paper explains the model design and reports some preliminary findings from a case study.

Construction visualization provides ample benefits to planning, scheduling, and forensic analysis of repetitive construction activities. Efforts have been directed toward using state-of-the-art technologies for providing a virtual realism to the visualization of construction processes. This paper addresses the benefits that can be provided by two-dimensional animation sequences on a widely used programming environment; Excel. Examples are presented. The system uses discrete event simulation and Visual Basic for Applications (VBA). The visualization is presented in an Excel worksheet. The work performed can be visualized dynamically during the simulation period. The first example presented illustrates the actual construction of a residential development where the lots were made available on time to the contractor. The second example is a simulation of this same project assuming that the residential lots were not available on time.

This research effort focused on the development of an automatic road incident detection and characterization system which will lead to the reduction of non-recurrent traffic congestion on freeways. An algorithmic methodology was developed for incorporation and enhancement of the existing South Carolina Department of Transportation (SCDOT) evacuation analysis system. Analysis of the existing system's input data helped determine the specific model needed. Investigation included a series of traffic analysis algorithms that consider: 1) identification of prevalent traffic flow conditions during a predetermined time window, 2) recognition of incident occurrence, 3) incident characterization, and 4) subsequent routing. The algorithmic methodology initially developed by Sheu (2002) was considerably expanded and adapted to the available SCDOT traffic data. This modified approach was then incorporated into a general system design document to establish definitions and descriptions of the proposed enhanced system. This initiative will provide traffic officials with relevant insights and expertise to detect and characterize emergency situations. It represents a significant improvement over the labor-intensive traffic monitoring systems currently used by many state DOTs, which lack automated capabilities for incident detection and characterization.

The establishments of standards to exchange, share, and integrate information result from the full consensus within the construction industry and their enforcement by government institutions. However, even if these conditions are met, the unique features and the dynamic social network of a construction project hinder the standard's success in developing interoperability, and results in different actors' developing different views to represent the information in projects. Alternatively, other efforts have relied on mapping the actor's representations within an individual project, limiting their application to that particular project or only to projects where the same actors are involved. Construction project actors constitute social networks at different scales in time and in space that require a dynamic information workflow for exchanging, sharing, or integrating information. The actors' achievement of these inter-operations is critical in making the communication of information possible to, for example, coordinate processes and exchange resource constraints. The complexity of social network hinders the deliverance of the construction products and processes information. Semantic tags for interoperable construction workflows represent a novel approach to support the exchanging, sharing, and integrating of information. Semantic tags leverage the communication of information within the dynamic social network grid. This research proposes a semantic main form of representation of construction concepts to assist in the communication between actors. This semantic approach represents construction concepts within documents by employing a concept-description that holds metadata. The semantic tag, therefore, has the ability to represent the required information for particular interpreters because of its incorporation of categories of concepts employed in the construction domain.

Students involved in the architecture, engineering and construction disciplines are often faced with the challenge of visualizing and understanding the complex spatial and temporal relationships involved in designing three-dimensional structures. A difficulty inherent in engineering education lies in teaching students decision-making skills, the logic of project management concepts, and the related risks involved in executing complex engineering projects. An evolving body of research applies the use of educational computer simulations to enhance student learning experiences through testing real-world scenarios to aid the development of student decision-making skills. This paper introduces ongoing research at The Pennsylvania State University aimed at improving engineering education in building and construction through interactive construction project learning applications in an immersive virtual reality environment. This research focuses on challenges in understanding and creating construction schedules. The construction industry increasingly employs 4D CAD models for detailed schedule reviews, but commercial applications currently used for creating these 4D models are often inadequate for construction engineering education due to their inability to concurrently create and review schedules. This paper describes the development of a first and second generation Virtual Construction Simulator (VCS), a tool that enables students to create and review construction schedules simultaneously through 3D model interaction. The educational value and utility of the VCS was assessed through surveys, focused group interviews, and a student exercise conducted in a construction management class at Penn State. The results revealed the overall value of the VCS as an effective 4D model creation and schedule review application that fosters collaborative work and greater task focus. In addition, this paper also discusses further development of the VCS educational simulation tool and future research.

For the Texas Department of Transportation (TxDOT), accurately predicting the duration of Right-of-Way (R/W) acquisition and utility adjustment processes in highway projects has been deemed as one of the principal capabilities that regional districts should possess. TxDOT has sought to establish an effective methodology for predicting the durations of these two processes. The "Right-of-Way (R/W) Acquisition and Utility Adjustment Process Duration Information (RUDI) tool" was developed and has been analyzed for effectiveness in implementation on projects. RUDI is an Excel-based tool that takes into consideration user inputs regarding project circumstances, such as schedule urgency and levels of uncertainty. The accuracy of RUDI and the key drivers that affect the durations of R/W acquisition and utility adjustment have been examined in order to identify future enhancements of the RUDI tool. The drivers identified by the experts with the most predictive accuracy in estimating durations for R/W acquisition include "TxDOT Project Type," "District R/W Annual Budget," "Dedication of Funds to the Project," "Funding Limitations for the Project," "Level of Political Pressure," "Need for Residential Relocation," "Level of Local Availability of Replacement Housing Facilities," and "Likelihood of Title Curative Actions." Likewise, for the duration of the utility adjustment process, the drivers deemed by the ones with the most accurate predictions include "Dedication of Funds to the Project," "Funding Limitations for the Project," "Have Subsurface Utility Engineering (SUE) Investigations been Performed?" and "Adjustment is Reimbursable Utility or Non-Reimbursabe Utility." In addition to the identification of these critical drivers, the RUDI tool proved helpful in predicting durations with the better accuracy in spite of limited data availability. Specifically, RUDI provided experts with reasonable duration ranges that can be used in better forecasting the durations of utility adjustment. The study revealed that further research is needed to maximize the benefits of the RUDI tool, although validating the study findings was restricted due to a lack of data.

Alternative dispute resolution (ADR) techniques are being widely adopted in construction projects to help handle disputes in an effective way. However, there are costs related to ADR implementation as it requires expenditures to cover the expenses incurred by the owner's/contractor's employees and third party neutrals. The exact amount of these costs won't be known until the actual occurrence of disputes during the construction phase. Rather than setting aside a certain percentage of contingency fee from the beginning of a project to deal with potential disputes, pre-arranging ADR implementation cost through a fixed cost investment in an insurance-like product may relieve project participants from constant anxiety over what will happen in the future, enable more efficient use of funds, and compensate unknown ADR implementation cost that may be incurred (perhaps up to a specified limit). The fundamental idea of this paper is to compare the known cost of purchasing insurance for ADR implementation versus the unknown cost of bearing the risk of uncertain numbers and costs of disputes. Mathematical calculations are presented in an illustrative construction project to perform the decision making process of risk management, and to show project participants the trade-offs of investing a certain amount of premium in exchange for compensation from the insurance company in the uncertain event of an unknown ADR implementation cost that may be incurred during the construction phase.

Processes in civil engineering consist of a huge amount of different tasks with complex dependencies between them. Currently, the specification and adaption of construction schedules are supported insufficiently. To provide a more efficient and comprehensible planning support, within a research project several methods and algorithms have been developed to allow semi-automatic generation of construction schedules including different construction alternatives. This allows handling and comparing construction alternatives as well as the reuse of schedules created previously. This paper introduces a novel approach for the generation of construction schedules using Industry Foundation Classes. It facilitates project managers to extract information from a building information model for scheduling and leads to a quick visualization via 4D simulations without further user interaction.

The Occupational Safety and Health Administration (OSHA) recommends performing Job Hazard Analysis (JHA) for construction activities to highlight and react to potential hazards. JHA commonly requires having brainstorming sessions to identify steps and associated hazards within construction activities. A company's personnel involved relies on their experience — and often also on the company's internal knowledge represented in the form of safety rules. The complexity and time consuming nature of JHA prevent safety personnel to react quickly to changes in the construction and the schedule. This paper presents a framework aiming to improve access to a company's JHA knowledge. The framework uses ontologies for structuring knowledge about jobs, job steps and hazards. It also includes an ontological reasoning mechanism for identifying safety rules applicable to a given activity. The framework has been tested using a test case. The results of this test case are discussed in this paper and conclusions for future research are drawn.