Computing in Civil Engineering (2012)

Advanced data acquisition (DAQ) technologies, such as smart tags, laser scanners and embedded sensors promise to collect accurate, complete, and timely field data, which is essential to increase the control of construction projects. However, since different DAQ technologies have different capabilities, which are meant to collect different types of data that come with different data processing requirements, construction professionals end up making ad-hoc decisions on which technologies to use in order to capture the required field data. To enable the capture of construction field data in a formal way via mapping required field data to applicable DAQ tools, we identified an initial set of representation requirements for modeling DAQ tools. Based on these requirements, we evaluated and compared relevant sensor ontologies. The limitations and capabilities of relevant sensor ontologies are described in this paper. This evaluation revealed that for the mapping process, there is no single ontology that can be used in its original form to represent DAQ tools. Hence, a representation schema, which builds on various aspects of existing ontologies, has been developed.

The traditional way of construction project claim document production suffers from tediousness and inefficiency. The manual assembly of claim event history leads to poor reliability of the claim report, due to the claim knowledge gap between experts and clerks. Sharing the team's comprehensive and formal claim knowledge with all of the participants in the claim work flow would result in a more effective way of producing claim documents. This study proposes a system for the ontology-based automatic generation of construction claim documents. The system will extract claim knowledge from all the potential sources into ontology. Software agents supported by the ontology will then complete each part of the claim events history data collection and generation process. The system will also support other functions such as information retrieval, natural language processing and database management. Such a system would efficiently facilitate the claim documents work and become an effective assistant to claim experts.

Construction concept details comprise their geometric features, components or parts, additional or assembled items, and functional characteristics. The concept conditions are the situational conditions or state of affairs, which embrace the concept location, position, site, place, and settings; the status condition, which is the stage of the concept (e.g., completed, installed, delayed), and its relations with other products or context descriptions (e.g,. set by, part of). The details and conditions are minimal ontological definitions of the concept that can be formalized by including logical axioms that use the syntax and vocabulary of a language, and additional semantic relations, which help describe several states of affairs. However, the representations are semantically poor which leads to deficient and accurate interpretations. Furthermore, intentionality is not semantically articulated with the representations. To respond to this need, this research proposes intelligent geometric topologies for construction (i-Con). i-Cons are images that have the virtue of being a symbolic vocabulary. These images have an established use, defined according to qualitative spatial relations, parenthood, and connectedness. Through the use of a mediating technology platform (e.g., a computer graphic application), i-Cons are linked to the virtual representations of the components to assist the user in semantically interpreting them and in efficiently arriving at more accurate conclusions.

Heating, ventilation and air-conditioning (HVAC) systems account for about 40% of energy used in buildings in the United States in 2008. Analysis of the energy efficiency of HVAC systems requires building-related information, such as building design, HVAC configurations, indoor environment measurements and load requirements. However, since building projects involve multiple disciplines, the needed information is stored in heterogeneous sources, such as design drawings, equipment manuals and sensor networks. It is hence difficult for system operators to collect and integrate the information required for analyzing the performance of HVAC systems. This paper explores the approaches to overcome this challenge of integrating heterogeneous information sources to support the information required for analyzing the performance of HVAC systems. The discussions include a synthesis of the characteristics of integration approaches from various domains, and an analysis of the relationships between the characteristics and the information sources required for analyzing performance of HVAC systems.

Analyzing community connectivity is necessary for identifying construction sites that have various services (e.g., restaurants, supermarkets) within walking distances, so that occupants of the new facilities would not need to use vehicles for accessing those services and thus reduce the carbon footprints of facility operations. Currently, civil engineers need to manually conduct community connectivity analyses on multiple candidate construction sites and recommend the one with the largest number of nearby services. Manual community connectivity analyses are tedious, especially when the engineers need to consider hundreds of locations across an urban area. While experienced engineers may quickly identify some locations with higher community connectivity, such a process is subjective and may compromise the completeness of the solution. To enhance the efficiency of community connectivity analysis while keeping the completeness level of the analysis, this paper explores a hierarchical sampling approach for quickly identifying all locations with high community connectivity across a medium-size city in Michigan. This approach first sparsely sample the urban area (e.g., two miles apart between evaluated locations), and then increase the sampling densities at locations with higher community connectivity based on sparse sampling results. Sensitivity analysis of sampling step sizes are presented and analyzed.

This paper is part of a larger research project, initiated to leverage the existing NoSQL database storage solutions for creating a platform-independent, fast, scalable and reliable database, capable of efficiently storing and retrieving information for oil & gas/civil engineering projects. This information is usually stored in SQL-based data warehouses to facilitate planning, maintenance or shutdown business processes. The proposed semi-structured database system stores information in JSON format, thus requiring a different approach in structuring, querying and aggregating data. The prototype storage model implemented in MongoDB looks very promising. JSON structure is a convenient way for storing project data, test scenarios show it being faster than a SQL alternative, and flexible schema definition means it is properly aligned with the agile development strategy. This storage approach fulfils the requirements of the project and is expected to be expanded to support ontology-driven storage, knowledge sharing and 3D model integration for a web application.

The project resource leveling problem was proposed to smooth resource usage and reduce resource fluctuation, while the resource-constrained project scheduling problem focuses on minimizing the project duration with limited resources. Solving the two interrelated problems separately is unlikely to result in a globally optimum schedule. The few integrated optimization methods that have been developed favor a predetermined resource profile that may be difficult to achieve. This paper proposes an integrated scheduling method to minimize the project duration and resource fluctuation by using the strength Pareto evolutionary approach II (SPEA II) which outperformed several other multi-objective optimization techniques in solving the resource-constrained project scheduling problem. An innovative chromosome representation scheme for SPEA II was proposed. A set of case studies were tested to compare the optimization performance of the proposed method with the best of the existing techniques. The results showed that the method yields better results than popular methods presented in the literature.

Construction workspace is regarded as one of the main constraints on construction sites. Construction workspaces are generally difficult to proactively plan and manage due to the dynamic nature of a site where workspace requirements keep changing as time evolves. However, project managers are looking for ways to develop proactive site plan for the workspaces required for construction activities as this can impact not only on the cost and project duration, but can also contribute to provide a safer site. This research paper presents an approach for integrating workspace management within the planning process using a serious game engine technology. This paper first illustrates a review of the workspace management practices and advanced visualization techniques in the construction industry. Then, it presents a process framework for an interactive decision support system that integrates workspace planning into 5D planning in order to enable safer, efficient and more productive construction sites. The decision support system will identify schedule conflicts, workspace conflicts and the severity of their conflicts, and workspace congestions on a construction site and allow construction planners to resolve spatial conflicts prior to construction. Finally, the paper shows the current progress in the development of an initial prototype and outlines the future work and research.

About 80-90% of accidents are strongly associated with workers' unsafe behavior. Despite the importance of worker behavior measurement, it has not been applied actively in practice due to its time-consuming and painstaking associated tasks. For measuring and analyzing worker behavior, vision-based motion capture has recently been proposed as an emerging technique that requires no additional time or cost. In line with motion tracking that extracts 3D skeleton motion models from videos, this paper proposes motion classification techniques for automatic detection of workers' actions. The high-dimensional motion models (e.g., 78 variables in this study) that result from tracking are transformed into a 3D latent space to reduce the dimensions for recognition. To recognize motions in the 3D space, this paper applies supervised classification techniques for training the learning algorithms with training datasets where unsafe motions are labeled, and then classifying testing datasets based on the learning. As a case study, motions during ladder-climbing are tested. The results indicate that the proposed approach performs well at automatically recognizing particular motions in datasets. Thus the measured information has great potential to be used for enhancing safety by providing feedback and improving workers' behavior.

Decision-making in fields such as politics, engineering and healthcare, shapes the world and how it evolves. Both public and private organizations face challenges when making decisions. Two examples occurred with the Minnesota Department of Transportation in 2007 and the U.S. Department of Energy in 2008. Losing bidders for a bridge-rebuilding contract and a liquid-waste cleanup project, respectively, protested the agencies' awards on the basis of evaluation methods and selection criteria. Multi-evaluator multi-criterion (MEMC) decision making can be controversial if bias or uncertainty find their way into the final decision. In a previous study, the authors of this paper developed a model that reduces the effect of uncertainty resulting from an evaluator's insufficient expertise in a particular criterion. This paper builds on the previous study by also correcting for potential favoritism or bias. It presents a more comprehensive mathematical model that supports MEMC decisions and protects decision-makers from criticism. The methodology includes: (1) proposing a probabilistic model and its assumptions; (2) developing an iterative algorithm; (3) testing the algorithm and analyzing its convergence; and (4) revising the model based on the results. Tests of the model show it performs better than the simple average method on 100% of the simulations.

Industrial construction projects are typically very complex in nature both technically and from a construction management perspective. These projects typically involve a large number of very specialized, highly skilled, and diverse role players. The skill set requirements for the resources (both manpower and equipment) make it very costly, and in many cases impossible, to accelerate or delay key tasks. The scheduling in this context focuses on achieving a near continuous work flow for key trades. This paper presents the study, from an owner's perspective, of schedule analysis for a highly repetitive industrial project. Due to highly specialized equipment needs, delays or unnecessary accelerations on this project results in huge added costs per day for the owner. The study uses a location based scheduling approach, the Flowline methodology, as a visualization tool to identify scheduling inefficiencies which cannot be identified using the existing CPM based schedule for the project. Although productivity data is considered an essential input for linear scheduling in general, this study demonstrates that the Flowline methodology can be used by an owner even without access to trade productivity data. The results can be used to strategically release work packages to avoid unnecessary added costs.

During the early stage of a construction project, decision-making happens frequently due to the decisive effect to the whole project. It is necessary to compare with actual data of other projects because every construction project is unique. For this, a decision-maker utilizes mountains of solution which can be collected from other projects. Case-based reasoning, which is the process of solving new problems based on the solutions of past experiences, is widely utilized for decision-making of construction project. This research suggests an expert system for decision-making of construction project using case-based reasoning. To verify the method, this research utilized the model which was built and case studied based on Korean military barrack projects.

This paper presents the use of experts' knowledge for design of field management instruction. Learning scenarios and supporting tools are developed through cognitive analysis of construction superintendents. In particular, a safety supervision tool was designed to simulate jobsite conditions and aid learners' decisions. By providing a robust context, higher education construction students are presented with an opportunity to practice decision making and observe the existing relationships of the domain. This helps students to understand how their knowledge fits into the big picture of field work before they step into a jobsite.

Imagine a student in a materials lab pressing a switch to begin a tension test on a steel specimen. For a few moments nothing appears to happen. Suddenly, with a loud bang, the specimen breaks and the test is over. Now imagine the student pressing the backup button and watching as the two halves of the specimen are magically reconnected. The student chooses to repeat the test but this time, knowing where things get interesting, the student chooses to view the test in slow motion so they may observe the transition from elastic deformation to inelastic deformation and the necking down of the specimen before it fails. Clearly, this is not your father's Tinius-Olsen machine. This student is using a simulated testing environment that offers safe and inexpensive learning opportunities. This paper demonstrates the effectiveness of using 3D games-based virtual lab simulations in the Strength of Materials Lab for Civil Engineering students. Virtual lab characteristics such as easy access to the information, visualization, and simulation capabilities allow auditory, visual, and kinesthetic learning environments to emerge. The effectiveness of this approach is measured using quantitative and qualitative analysis and compared to student performance in the traditional physical laboratory.

Construction has been one of the most dangerous industries, with fall being the most common type of hazards. Meanwhile, about one fourth to one third of the industry's taskforce is composed of Hispanic workers, many of whom lack proficiency in English, and/or have limited literacy. Limited literacy and lack of English proficiency are not specific to Hispanic workers and serve as critical barriers in effective safety education and training. The paper presents the developmental efforts and lessons learned of six 3D visualized and scenario-based training case materials that use minimum amount of text descriptions. Common cases that involve construction fall hazards from the National Institute for Occupational Safety and Health (NIOSH) Fatalities Assessment and Control Evaluation (FACE) program were analyzed and modeled in a 3D video game platform referencing the effective training strategies from Occupational Safety and Health Administration (OSHA). Using 3D simulated virtual job sites in safety training is expected to reduce the required level of language proficiency and literacy, and increase the understanding as well as learning interests of those in construction who can't speak or read much English.

The existing deteriorating condition of infrastructure systems in the U.S. needs to be improved by expanding innovative financing options. In this paper, a hybrid agent-based/system dynamics model is created to simulate the micro behaviors of the players (namely, the state Departments of Transportation, private institutional investors, and the general public) in this financing process, and aggregates the effects of these micro behaviors on infrastructure investment at the state and national levels. Results of the simulation model are used in a classification and regression tree meta-model to (1) simulate the landscape of infrastructure financing policies and (2) identify the most significant factors affecting the level of investment in transportation infrastructure. The reliability of the conceptual model, the computer simulation, and the data were verified and validated by subject matter experts from organizations heavily involved in infrastructure financing. The model is further validated through sensitivity analysis and uncertainty propagation analysis. The results of the model include the creation of landscape of policies and the identification significant factors affecting transportation infrastructure financing in the U.S. A scenario analysis is implemented and recommendations, including potential policy paths to close the financing gap, are provided based on the simulation results and the policy landscape.

Serious games provide an avenue for people to experience what occurs on a construction site and develop beneficial skills before they enter a live construction site. These tools have traditionally been designed around the assumption of users engaging with preset construction scenarios with the goal of learning specific skills or behaviours that are important when interacting with the site for real. The construction industry has other tools it can exploit to increase the training value of serious games. 4D and 5D planning and visualisation tools and techniques can help expand the horizons of these types of training games. In particular, by merging virtual training environments with a 4D design ethos, the range of potential knowledge the game can impart would be significantly increased. This research paper is exploring how a cross-pollinisation of these two techniques can yield a richer environment for transferring skills and knowledge. This paper explores the creation of a multi-user virtual environment capable of providing health and safety training and orientation to groups of trainees before they step foot on site.

With the growth of the population worldwide and the increased demand on resources, there has been a growing emphasis on sustainability and awareness of green and responsible use of resources. In response to the need for a sustainable built environment, the United States Green Building Council (USGBC) has established the Leadership in Energy and Environmental Design (LEED) rating system, which includes a set of metrics for defining and measuring "green buildings". Construction professionals and designers are recognized as LEED AP (Accredited Professionals) if they can demonstrate, via the LEED exam, knowledge and understanding of the LEED rating system. In this paper we describe a serious game for learning Green Building design principles and LEED concepts. The game is aimed at undergraduate students enrolled in Civil Engineering, Architecture and Building Construction Management programs, and professionals who are interested in becoming familiar with the LEED system. The game includes five levels that focus on the environmental categories of the LEED rating system for `New Construction and Major Renovations' 2009 version.

Landslides cause significant damages to civil infrastructure. Over the years, methods and technologies have been proposed to determine the risk of landslides and to detect hazardous slope movements. There have been increasing interests in developing and landslide monitoring systems to observe movements using sensors installed on the slope. Although providing accurate data, many landslide monitoring systems are not operating in an automated fashion and lack the ability to analyze the collected data in a timely manner. This paper presents an autonomous landslide monitoring system based on wireless sensor networks. Self-contained, autonomous software programs ("software agents") are embedded into the wireless sensor nodes. In cooperation with each other, the software agents are continuously collecting and analyzing sensor data, such as recorded ground acceleration and the orientations of the sensor nodes along the slope. If movements are observed, the collected data sets are automatically transmitted to a connected server system for further diagnoses. The landslide monitoring system presented in this paper is remotely accessible via Internet and provides real-time information about the current state of the monitored slope. Laboratory tests have been conducted to validate the reliability and the performance of the monitoring system.

The estimation of the impacts an uncertainty might have on a construction project is particularly challenging. The major reason is that construction projects are considered as unique facilities. Therefore, a successful estimation requires from the project manager an evaluation of the different simulation scenarios and alternatives and this apparently demands time and efforts. This paper discusses the huge number of simulation runs that are needed for the evaluation of a simulation study in construction projects, and efficiently reduces the overall simulation time without increasing the required processing needs. Concerning this goal, the combination of a deterministic colored petri-net model with a multi-agents model to build a simulation model is suggested. Furthermore, a simulation framework named MOSAICA is developed in order to properly execute these models. And thus, the results of applying MOSAICA on a real project are encouraging. As a conclusion, the proposed approach made it possible to concurrently run large number of simulation models alternatives of a real building construction project within milliseconds.

Maintaining the infrastructure involves managing structural elements requiring different maintenance strategies. Some elements have to be maintained at regular intervals based on a pre-programmed routine. Other elements are inspected at regular intervals and the specific the requirements for maintenance remain unknown until after the inspection. The latter complicates job scheduling as it disrupts existing programs for routine maintenance. Conventional maintenance planning systems lack dynamism and/ or fail to acknowledge all relations between actors, activities and resources. This paper discusses the potential of addressing these challenges using an organization-centered agent-modeling approach where different scenarios are encapsulated in agent models which captures: 1) global organization strategy and goals, and 2) the objectives and requirements of different stakeholders. Specific challenges of maintenance planning that cannot be fully addressed using conventional tools have been identified. The paper describes implemented of proof-of-concept organization-centered agent models that capture the requirements of maintenance planning.

Most of the existing automated machine vision-based techniques for as-built documentation of civil infrastructure utilize only point features to recover the 3D structure of a scene. However it is often the case in man-made structures that not enough point features can be reliably detected (e.g. buildings and roofs); this can potentially lead to the failure of these techniques. To address the problem, this paper utilizes the prominence of straight lines in infrastructure scenes. It presents a hybrid approach that benefits from both point and line features. A calibrated stereo set of video cameras is used to collect data. Point and line features are then detected and matched across video frames. Finally, the 3D structure of the scene is recovered by finding 3D coordinates of the matched features. The proposed approach has been tested on realistic outdoor environments and preliminary results indicate its capability to deal with a variety of scenes.

The main issues that significantly contribute to problems and delays on construction sites are changing client's requirements, incomplete design information, and poor site monitoring and control. Although experienced designers and construction managers control or minimize such problems during the design stage, the complexity and amount of the information in construction project make such a task very difficult to accomplish effectively. This paper presents an actual case study model for an integrated system which aims at presenting CAD/CAE in 3D using virtual reality. Firstly, the technology enables the designer to walk-through the proposed building perhaps at different construction time intervals-giving a vivid appreciation of the whole situation. Secondly, its enables the users to interrogate the building structural elements to present its details progress thus giving total virtual structural view of the project. Thirdly, the design effect of any changes in the building configuration can be modeled, visualized, and even the cost effect could be calculated. Finally the system enables virtual models to be shared and thus facilitates collaborative global design and construction. A case study involving the design of a 1600 m2 Cathedral with a roof height of 13m and a span of 33m is explored.

Videogrammetry is an inexpensive and easy-to-use technology for 3D scene spatial recovery. When applied to civil infrastructure, the scale of the scene is significantly larger than manufacturing applications. As a result, it usually takes a long time to videotape the scene, leading to a large quantity of frames in the video. In most cases, only few portions of those frames should be processed. In addition, uncontrolled environment will result in motion blur, which significantly affect the performance of the videogrammetric pipeline. To address the mentioned issues, in this paper, a novel method for automating the selection of appropriate frames is presented and validated. In this method, the blurred frames are first removed based on the thresholds determined by experiments and the key frames are then selected from the remaining frames using selection criteria devised by the authors. Experimental results reveal that the proposed method outperforms the existing methods in terms of successful rates of 3D reconstruction while maintaining optimized number of required frames for post processing stages.

Construction equipment generally consists of a set of rigid bodies connected by joints, which is termed as a kinematic chain. The relative motion and constraints between successive bodies of the chain make the real-time 3D visualization a challenge, which requires minimizing the number of sensors as needed by practical applications in construction. In this paper, we investigate a methodology for efficient real-time 3D visualization of articulated construction equipment. The Denavit-Hartenberg (DH) technique which has been widely used in robotics research is introduced and adapted for computing relative motions of various components in the articulated equipment system based on a minimal quantity of input parameters. The 3D position of articulated construction equipment can be analytically fixed by analyzing point coordinates and joint movement parameters. This analytical method is validated by simulating a backhoe excavator in a computer environment, where the absolute location of the backhoe' tracks is computed by tracking three control points, and the relative positioning states of the cabin, boom, stick and bucket are deduced by using the DH technique.

The goal of this research is to gain better understanding of how BIM models can be used to create mobile 3D game environments for some serious purpose other than entertainment. This project was motivated by the perspective that the capability of BIM creating realistic 3D virtual game environments for collaboration, communication and learning (CCL). The wide dissemination of mobile devices is driving many computer applications toward the direction of ubiquitous mobile accessibility. Considering the popularity of the iPad mobile device, a BIM model of an institutional building was created for this 3D mobile game application on iPad. This game application allows users to visualize and interact with the HVAC BIM components. In this paper, we reported our preliminary findings in developing a BIM-based iPad 3D game application for CCL activities in architecture, engineering, and construction (AEC). Technical barriers of using BIM models to create mobile 3D CCL applications were also discussed. The detailed system architecture and implementation framework were presented in this paper. Differences between the developed program and the existing BIM mobile programs were elaborated to distinguish the developed program from the off-the-shelf BIM mobile programs, such as Vella and Autodesk Inventor mobile viewer. The preliminary results indicated that fairly complex BIM models including multiple building systems can be imported and created through 3D virtual-built environments to conducted CCL activities in the AEC domain.

The Industry Foundation Classes (IFC) provides a rich and redundant schema for interoperability. However, IFC lacks semantic clarity in mapping entities and relationships resulting in multiple methods to map the same information. This research explores a software engineering methodology based on engineering ontologies to develop a formal, consistent and machine-readable structure for IFC entities, attributes and relationships. Various issues such as the need for a logical framework, the current semantic approaches in AEC/FM and advantages of ontology for IFC are addressed. Details of the approach are illustrated by building application ontology for precast model exchanges. This research is expected to impact the overall interoperability of BIM tools by providing a formal and consistent taxonomy and classification structure for creating model view definitions (MVD) using IFC.

Industrialization creates new requirements for design. Designers need to consider not only building performance, but also production plan needs. This requires a well-structured Building Information Model (BIM) to support the manufacturing needs for design and drafting. BIM, in combination with CAD tools such as AutoCAD and ArchiCAD, can be used for this purpose. These, however, are not sufficient to support the level of detail needed for the manufacturing process. The proposed research establishes a methodology for the automation of design and drafting for the building manufacturing of residential facilities based on the platform construction framing method. The proposed methodology has been incorporated into a computer model called MCMPro, which was developed using Visual Basic for Applications (VBA) as an add-on to a CAD model. MCMPro incorporates BIM technology based on CAD parametric modelling and manufacturing requirements in a 3D-model, in order to generate sets of shop and fabrication drawings.

Research on information systems development methodologies (ISDM) adoption has been widely undertaken to highlight the experience of developed countries. Studies concerned with the status of ISDM in developing economies including the factors that influence and motivate their use, current trends, difficulties, and barriers to adoption are lacking. This paper explores these issues in a developing economy, namely Oman, and proposes an ISDM adoption decision model using Analytical Hierarchy Process Analysis (AHP) and Delphi consultation. The findings from the survey reveal a lack of knowledge of ISDM amongst Omani Information Systems developers and the reliance on ad-hoc software development methods. Conversely, analyzed data reveals a trend whereby a majority of Omani organizations are gradually moving towards increased ISDM adoption and deployment.

Building commissioning is one of the most effective means to ensure the building systems and equipment will perform as designed in its lifecycle when handed over for operation. Current practice of commissioning is labor intensive and the commissioning deliverables are heavily 2-D documents and images that are inefficient and ineffective to utilize in operation and maintenance of the building. The research thus looked at BIM as a facilitator for a more viable approach for building commissioning. Per its definition, BIM is a lifecycle information repository of a construction project. With a project delivery embedded commissioning scenario, this BIM-enabled commissioning facilitated the commissioning process at each phase of the project lifecycle by accomplishing critical commissioning tasks, empowering enhanced project team collaboration and streamlining commissioning documentation generation. Two case studies were given at the end to demonstrate the best practices in the industry that used BIM for building commissioning and handover.

Three-dimensional (3D) reconstruction of as-built pipelines has been considered for a wide variety of purposes, including plant operation, maintenance, and expansion of existing facilities. However, the current method of generating 3D models of as-built pipelines from laser-scanner data requires costly and labor-intensive manual processes. The objective of this study is to develop an automated as-built pipeline-modeling method. The proposed approach consists of two functional parts. The first is segmentation of cylindrically formed pipelines based on medial axis approximation and curve skeleton extraction. The second is surface-model generation of cylindrically formed pipelines based on a parametric modeling method. The initial experiment was performed at an operating plant facility to evaluate and validate the proposed method. The preliminary experimental results revealed that the proposed method enables reliable 3D as-built models of pipelines, which could be successfully incorporated into the development of an as-built plant information modeling method and utilized to assist maintenance tasks, such as inspection, repair, replacement, or revamping.

As-built BIM is a relatively new concept to the AEC/FM industry and has not been applied to many project elements yet, including structural components. The conventional methods of as-built documentation of structural components, such as rebar cage, do not provide sufficient information and often lead to rework and project delay. A comprehensive dimensional quality control of installation of structural rebar and anchor bolts is very time-consuming and labor intensive with traditional surveying systems. It is impractical to check every rebar and anchor bolt within the structure with sparse data collection. However, laser scanning is capable of bridging this gap by providing dense point cloud data of such components at construction site. This paper introduces the use cases of scanning structural rebar in reinforced concrete construction. A successful experiment of scanning structural rebar was conducted in a construction project. As-built conditions of rebar cages and anchor bolts were documented in BIM environment and compared against as-planned model. The result of the comparison and the benefits to the contractor are explained in the paper.

To improve energy efficiency of existing buildings, various analyses, computational simulations and performance monitoring are conducted throughout the lifecycle of the building, especially during the planning and design phases. One of the most common shortcomings in the current computing infrastructure is that there are bottlenecks in the building processes due to imperfect information exchange, causing frequent and significant delays as, for example, the missing information and data need to be re-produced. This paper discusses the development of a building systems process model being developed as a part of a major energy efficient building initiative that focuses on energy-efficient renovation of medium-sized commercial and multi-family residential buildings. The map aims to depict an integrated building design process flow, especially for analyses, simulations, and information exchanges among the core activities contributing to key decision-making points. In particular, this paper focuses on the development of such a process map through literature review of existing integrated processes and workshops, and defines the requirements and strategies for efficient and seamless information exchange among project participants from different disciplines.

Model View Definition (MVD) procedures are accepted by buildingSMARTTM as the de facto industry standard in AEC/FM for specifying information exchanges between software applications using Industry Foundation Class (IFC) schema. However, current model view development approaches are time consuming, lack semantic clarity, are expensive to test and validate, and result in static model views with limited reuse in industry. As a result, software companies are required to duplicate efforts in preparing export functions that contain the same exchange structures multiple times, for each view. To address these issues a new framework for composing model views based on object-oriented, testable and reusable modules of information called Semantic Exchange Modules (SEM) is under development. We define a set of guidelines for developing SEMs and a methodology for specifying model views based on SEMs. The approach is validated using the precast concrete domain model exchanges as the test case. Comparison of current methods to the new approach using SEMs highlights the impact of this research.

Building Information Modeling (BIM) efficiently integrates environmental analysis into the design and delivery of high-performance buildings. Building Energy Modeling (BEM), a subset of BIM, employs various simulation tools for predicting the environmental performance of buildings. As the demand for high-performance buildings has increased, BEM has facilitated the delivery of buildings that meet expected performance requirements. The research objectives were to: 1) evaluate various BEM tools, and 2) develop guidelines for using BEM tools in design and delivery of high-performance buildings. Twelve BEM tools were evaluated using four criteria: interoperability, user-friendliness, available inputs, and available outputs. The top three programs (Autodesk Ecotect, Autodesk Green Building Studio, and IES <VE>) were selected based on this evaluation. Each of these selected BEM tools was used in the case study to simulate energy consumption, daylighting performance, and natural ventilation for two buildings, one LEED certified and one non-LEED certified. The results of the case study were used to compare the environmental performance of the two buildings and to develop guidelines for using BEM tools to analyze building environmental performance.

Computerization of building codes and standards in connection with Building Information Modeling (BIM) represents a real challenge for the AEC industry. On one hand, building rules and regulations are written by professionals to be read and applied by people since the reasoning and interpretation ability of the human brain is unlike anything implemented in computer systems. On the other hand, a smart phone app can outperform any engineer on solving a set of linear equations. However, many tasks that are easy for engineers or human in general are surprisingly difficult for computers. Examples include interpretation and expression of code and standard provisions that are characterized by subjective and descriptive rules. The perceptual abilities of engineers certainly surpass the fastest supercomputers. This gave motivation to many researches investing the efforts in creating computable representation of the building codes and standards and their link to Building Information Modeling (BIM). This paper provides critical review of the development of computable building codes rules that can be implemented into BIM-based automated rule-checking systems. It also addresses the complexity of these knowledge systems, the problems they pose for engineers and designers, and the methods for managing and advancing them.

With the rapid acceptance and increasing interest in Building Information Modeling (BIM) in the AEC industry, the issues of level of details and interoperability as means to communicate and integrate various model-based application into a high productive workflow has emerged to the forefront of the professional attention. The ultimate success of BIM will in part depend on the ability to capture all relevant data in the BIM model, and to successfully exchange data between the various applications. BIM tools that have matured to some extent for structural steel and concrete construction are not yet satisfactory for modeling wood structures. This study reviews the current state of BIM tools in modeling wood structures and formulates the functional requirements for development of successful BIM models for designing wood structures. This paper focuses on advancing standardization of BIM model for designing and analyzing wood structures. In particular the paper addresses the Information Delivery Manual (IDM) and Model View Definitions (MVDs) as they aim to provide the integrated reference for process and data required by identifying the discrete processes undertaken to design wood structures and to create an efficient method for seamless, reproducible exchange of reliable information that is routinely acknowledged by the industry.

The authors' efforts to improve the quality of Industry Foundation Class (IFC) building information exchanges has highlighted needed for defensible verification methods. The tools and techniques needed to meet these efforts requirements would also improve research that requires building information. This paper announces the open publication of a series of models and tools produced and used by the authors for their research. Widespread use of common models and shared tools are expected to improve the quality of research that requires building information.

The lack of viable methods to map and label existing infrastructure is one of the engineering grand challenges for the 21^st century. For instance, over two thirds of the effort needed to geometrically model even simple infrastructure is spent on manually converting a cloud of points to a 3D model. The result is that few facilities today have a complete record of as-built information and that as-built models are not produced for the vast majority of new construction and retrofit projects. This leads to rework and design changes that can cost up to 10% of the installed costs. Automatically detecting building components could address this challenge. However, existing methods for detecting building components are not view and scale-invariant, or have only been validated in restricted scenarios that require a priori knowledge without considering occlusions. This leads to their constrained applicability in complex civil infrastructure scenes. In this paper, we test a pose-invariant method of labeling existing infrastructure. This method simultaneously detects objects and estimates their poses. It takes advantage of a recent novel formulation for object detection and customizes it to generic civil infrastructure scenes. Our preliminary experiments demonstrate that this method achieves convincing recognition results.

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.

This paper introduces a model-based automated object recognition and registration method, Projection-Recognition-Projection (PRP), to assist heavy equipment operators in rapidly perceiving 3D working environment at dynamic construction sites. It has been a challenging subject to recognize target objects from a scattered work environment because large and complex 3D site data obtained by a laser scanner makes it difficult to process itself in real or near real time. In this study, a CCD camera and a hybrid laser scanner were used to rapidly recognize and register dynamic target objects in a 3D space by separating target object's point cloud data from other background point cloud data for quick process. The experimental results were promising in terms of modeling speed and accuracy. If successful, as an on-going project, the proposed PRP method can significantly improve heavy construction equipment operations and automated equipment control by rapidly modeling dynamic target objects in a 3D view.

Using Building Information Modeling (BIM) for design, detailing, fabrication and erection of concrete reinforcement and automating information exchange throughout AEC projects potentially improves productivity of the reinforcement supply chain. This study first develops an information flow process map documenting the reinforcement supply chain activities, and identifying the contents of the reinforcement models, created and exchanged by different disciplines during various phases of a concrete project. Then the identified information items are used as a basis to assess the functionality of BIM tools for reinforcement. BIM tools are evaluated in four areas: design and modeling, editing, project and construction management, and interoperability. The IFC schema is chosen as the preferred medium to examine the interoperability capabilities of the evaluated BIM tools. Our study identified significant enhancements in 3D parametric modeling and information interoperability of concrete reinforcement. Despite these improvements, notable modeling and interoperability deficiencies in the reviewed BIM tools remain unsolved. These shortcomings impact the modeling outcome and hinder effective use of BIM reinforcing models generated in design during fabrication and construction management. Recommendations to improve the identified shortcomings in all the four reviewed areas are provided.

Buildings cannot be used for their intended purpose without utilities. Maintenance workflows require work to be performed from the inside and outside of the buildings. The inside information can be extracted from a fully built building information model, and the outside information can be extracted from a Geographical Information System (GIS) map. GIS is the platform that integrates maintenance workflows for mechanical, electrical and plumbing systems for facility infrastructure. In order to perform efficient maintenance work, it is necessary to get the related information of the pipe network in advance. The purpose of this paper is to investigate how to use GIS and BIM information to get the detailed information for facility operation and maintenance. Field workers, without strong GIS and CAD background, need to visualize and analyze the subsurface pipelines. This study proposes a method to use data from BIM and GIS for 3D visualization.

Driven by the knowledge that Building Information Modeling (BIM) may result in fewer conflicts and improved coordination, many owners are now mandating that it be utilized on new construction projects. However, they are often unsure of what BIM deliverables and processes to demand and how proficient the stakeholders they choose to contract with are in using such technologies. This research aims to assist facility owners in the adoption and mature implementation of BIM processes by assessing their current state of BIM execution. Using a survey of building owners, it identifies the proportion of owners currently requiring BIM, what requirements are being set forth for BIM deliverables and whether those deliverables are being used post-construction for operations and maintenance.

The construction industry has incurred the most fatalities of any U.S. industry in the private sector in recent years. While many factors may contribute to this statistic, one likely cause is due to designers who often lack design for construction safety knowledge, which results in many safety hazards being built into project models. To improve the current situation, a design for construction worker safety tool based on BIMserver was developed. This tool can automatically conduct compliance checking by using the advanced query functions of the BIMserver. Construction safety best practices were complied into computable rules. These rules were written by adopting Java code with the IFC hierarchy. This tool efficiently makes designing for safety suggestions available to designers and constructors. Particular emphasis was placed on fall accidents since falls are the most frequently occurring cause of fatalities on construction sites.

Energy efficient building design, construction, and operations require the development and sharing of building information among different individuals, organizations, and computer applications. Building Information Modeling (BIM) servers are tools used to enable an effective exchange of data. This paper describes an investigation into the core BIM server requirements needed to effectively support information sharing related to energy efficient retrofit projects. The requirements have been developed through an analysis of existing functional capabilities combined with a case study analysis. The set of requirements identified includes fine-grained queries such as selective model queries, information queries (e.g. weather, building description), and operational information queries (by building parts, proximity, and context). A set of RESTful programming interfaces for building tools to access and exchange data, including security and data privacy issues, is being explored to provide a server-centric building information model exchange and interoperability to facilitate energy efficient retrofit.

With the development of building information modeling, knowledge sharing between facility management and design professionals has become possible. The use of building information modeling (BIM) technology in the design and construction phases of buildings is increasing. There is a need to expand the use of BIM beyond the two to three years design and construction phase into the facility management phase of buildings in order to facilitate tasks such as maintenance and to maximize its value to facility owners. However, the fact of current Architecture, Engineering, Construction and Operations (AECO) industry practice is that the facility management phase seldom uses the BIM models even when they are required by the owner in design and construction phase. This fact results in BIM models being wasted where they have the most value, in the facility management phase. This study is a part of the ongoing research of BIM-assisted Facility Management which is aimed to bridge the communication gap between design and facility management professionals. This paper focuses on automatic bidirectional communications between Computerized Maintenance Management Systems (CMMS) and BIM models on a database level.

To further understanding about how to use building information systems to communicate, this paper uses computer semiotic theory to study how building information systems operate as carrier of meaning by introducing a semiotic framework for the analysis of building information systems. This framework consists of a basic sign typology and describes several mechanisms of how to compose basic signs to convey more complex meaning in three different semiotic domains: the use process domain, the system's content domain, and the system's user expression domain. The paper also provides a first illustrative application of the framework by analyzing the user interface of a construction process visualization tool. This initial analysis already shows that the semiotic framework can improve our understanding of how well meaning can be exchanged between the system and users.

Cloud computing is a hot research topic and is quickly gaining recognition and application in combination with Building Information Modeling (BIM). However, since there is still lack of an authoritative definition of cloud computing, different parties in Architectural / Engineering / Construction (AEC) industry may have very different understanding on what is qualified as BIM cloud computing. This paper proposes a system under which some of the currently available cloud computing frameworks are classified and the adoption of cloud computing in a specific context can be assessed, and reviews and compares several currently available BIM cloud computing frameworks under this system. The BIM cloud computing frameworks compared include dedicated cloud services for AEC industry such as Revit Server, Revit Cloud and STRATUS, and general purpose cloud services such as Advance2000 and Amazon. The proposed system can be used by IT implementers in the AEC industry when making decisions on adopting cloud computing for BIM applications.

Natural disasters can compromise the structural integrity of buildings. Three dimensional post-disaster building models, created using image-based 3D reconstruction techniques, would help early responders assess the extent of the damage. Image-based 3D reconstructions are advantageous as they only require a camera and tens of pictures to create a 3D mesh model. Under some circumstances responders can collect these images; however, if a building is suspected to have sustained significant damage, it may be neither safe nor straightforward to examine its structural integrity. Therefore, an approach is needed which allows responders to remain away from compromised buildings while gathering images for a 3D model. We propose the use of a small ground robot to take pictures of building elements remotely. Once the images are collected, an element model is created using a pipeline of Structure from Motion, dense reconstruction, and surface modeling. This model is compared to a pre-existing CAD model or examined to assess the likelihood of any structural damage. We also demonstrate a new automatic 3D crack-detection algorithm to assist examiners in identifying structural defects. Our preliminary results show that this process can successfully identify numerous cracks in the 3D reconstruction of a sample concrete column. The perceived benefits of the proposed method in a post-disaster situation are also discussed in detail.

The U.S. construction industry suffers from high rates of fatalities, injuries and illnesses. Non-fatal injuries and illnesses also lead to low construction productivity. Among various types of hazards construction workers are exposed to, excessive physiological loads have generated unnoticed but profound negative impacts on construction productivity and safety. This research aims at automatically detecting the physiological load of construction workers by using wearable sensors. The research utilized off-the-shelf bio-sensors that collect heart rate and energy expenditure in real time. Biomedical measurements were carried out during masonry wall construction. The collected data were analyzed statistically and compared with the subjective feedback from test participants. The authors introduced an index to quantify the level of physiological load for construction workers. The ultimate goal is to develop a prevention system that reduces fatalities and injuries, and increases construction productivity on construction sites by automatically monitoring workers' physiological conditions in real time.

This research aims to develop a virtual laser target board methodology for tunnel boring machine (TBM) guidance control during tunneling operations. Current practice for TBM guidance using physical laser targets is evaluated. Coupled with a fully automated TBM tracking system resulted from in-house research, the virtual laser target board program is proposed to provide an effective aid for TBM operators and field managers in making critical decisions for tunnel alignment control. Comprehensive data processing procedures are carried out to determine: (1) TBM's position in the underground space, including any registered points on the TBM, e.g. center of TBM's cutter head; (2) tunneling progress; (3) line and grade deviations of the tunnel alignment; and (4) TBM's three-axis body rotations. Field experiments on a 2.4 m diameter TBM were conducted to collect registration data for on-line processing by the virtual laser target board program.

Vision-based object detection has been introduced in construction for recognizing and locating construction entities in on-site camera views. It can provide spatial locations of a large number of entities, which is beneficial in large-scale, congested construction sites. However, even a few false detections prevent its practical applications. In resolving this issue, this paper presents a novel hybrid method for locating construction equipment that fuses the function of detection and tracking algorithms. This method detects construction equipment in the video view by taking advantage of entities' motion, shape, and color distribution. Background subtraction, Haar-like features, and eigen-images are used for motion, shape, and color information, respectively. A tracking algorithm steps in the process to make up for the false detections. False detections are identified by catching drastic changes in object size and appearance. The identified false detections are replaced with tracking results. Preliminary experiments show that the combination with tracking has the potential to enhance the detection performance.

This paper presents an automated and real-time algorithm for recognition and 2D tracking of construction workers and equipment from site video streams. In recent years, several research studies have proposed semi-automated vision-based methods for tracking of construction workers and equipment. Nonetheless, there is still a need for automated initial recognition and real-time tracking of these resources in video streams. To address these limitations, a new algorithm based on histograms of Oriented Gradients (HOG) is proposed. The method uses HOG features with a new multiple binary Support Vector Machine (SVM) classifier to automatically recognize and differentiate workers and equipment. These resources are tracked in real-time using a new GPU-based implementation of the detector and classifier. Experimental results are presented on a comprehensive set of video streams on excavators, trucks, and workers collected from different projects. Our preliminary results indicate the applicability of the proposed approach for automated recognition and real-time 2D tracking of workers and equipment from a single video camera. Unlike other methods, our algorithm can enable automated and real-time construction performance assessment (including detection of idle resources) and does not need manual or semi-automated initialization of the resources in 2D video frames. The preliminary experimental results and perceived benefits of the proposed method are discussed in detail.

Digitally addressable lighting systems offer tremendous opportunities for performance monitoring and control of individual light fixtures. However, the locations of individual light fixtures within a building are not easily differentiable; as such, facility managers cannot easily distinguish and control each fixture. Virtual models contain semantic representation of objects which enable users to visually identify, distinguish and interactively monitor and control building components. To monitor and control light fixtures from these models, each physical fixture needs to be tightly integrated with its virtual representation such as to enable bi-directional coordination. Bi-directional coordination between virtual models and physical light fixtures offers tremendous opportunities for enhancing progress monitoring during construction, and performance monitoring and control during the operations and maintenance phase. Thus, this paper presents an approach to component-level cyber-physical systems integration using light fixtures as an example. A system architecture describing the enabling technologies and their roles is presented and a practical implementation is also presented to demonstrate the functionality and utility of the proposed approach.

Adequate crane position on construction site required large amount of site data to be collected, prior to any lift operation. Existed permanent/ non-permanent obstructions as well as objects being placed on their final position, must be considered for each lift. This paper presents a methodology for crane selection and on-site utilization evolutionary algorithm for multi-lifts for modular construction. The proposed methodology accounts for cranes capacity limitation as well as evaluates the crane carrier/body position and orientation. State-of-the-art methodology incorporate evolutionary algorithm that reacts to dynamic changing site conditions. This paper introduces a case study where the objectives were to simplify and optimize the field assembly operation for 5- three-storey dormitory buildings including bridges and large roofs in McGregor Village for Muhlenberg College in Allentown, Pennsylvania, USA. Each dormitory contains three types of modules and a total of 18 separate modular units. Fully habitable elements were delivered securely on flatbed trailers to the site in advance. An all-terrain mobile hydraulic crane placed in the center of construction site lift each module and placed like a puzzle floor by floor for each building at predefined position. All 100 lifts were conducted in only 10 working days.

Automated regulatory compliance checking requires automated information extraction (IE) from regulatory textual documents (e.g. building codes). Automated IE is a challenging task that requires complex processing of text. Natural Language Processing (NLP) aims at enabling computers to process natural language text in a human-like manner using a variety of text processing techniques, such as phrase-structure parsing, dependency parsing, etc. This paper proposes a hybrid syntactic (syntax/grammar-related) and semantic (meaning/context-related) NLP approach for automated IE from construction regulatory documents, and explores the use of two techniques (phrase-structure grammar and dependency grammar) for extracting information from complex sentences. IE rules were developed based on Chapter 12 of the 2006 International Building Code; and the approach was tested on Chapter 12 of the 2009 International Fire Code. Initial experimental results are presented, empirically evaluated in terms of precision and recall, and discussed.

This paper discusses concepts and procedures that civil engineers use to handle both generic 3-D spatial data and geospatial data referenced to the Earth. A consequence of the digital revolution is that spatial data are now characterized as digital and 3-D. The principles used by Gerard Mercator (1512-1594) in conformal mapping and René Descartes (1596-1650) in solid geometry are brought forward in the context of information management as envisioned in the 1955 Grinter Report and more recently realized in geographic information systems (GISs) and 3-D applications such as GPS, laser scanning, and LIDAR. The 3-D global spatial data model (GSDM) provides a computational environment that preserves 3-D geometrical integrity, is standard world-wide, supports existing "flat-Earth" applications, and serves as a foundation for the global spatial data infrastructure (GSDI). While spatial data computations are standard in civil engineering practice, the concepts related to geospatial data should also be included in civil engineering practice, education, and curriculum design.

During the construction of new asphalt roads, compaction is the final step. Proper compaction is crucial for the road's lifetime. The temperature of the asphalt mixture directly impacts on the compactability and therefore the construction process strategy. Ideally compaction should be done within a certain in-asphalt temperature window, with lower and higher temperature boundaries, to achieve high quality road surface. But, as there are no available systems to predict in-asphalt temperature, roller operators have to guess the actual temperatures. This paper describes a method and proposes an implementation of this method to predict in-asphalt temperature at any given position. Calculations are based on an initial asphalt mix temperature during paving operations and the automated computing of a cooling function for a specific mix within certain ambient weather conditions. The implementation of the method was tested using position and temperature information collected by following a real paving project. Outcome of the method - the resulted visualization - aims to provide information about in-asphalt temperature to support decisions of machine operators when to start and stop rolling process to obtain the high quality road surface more reliable.

After the devastating earthquake that hit Haiti in January 2010, a number of non-profit organizations started building cost effective replacement homes for the needy using the rubble from collapsed buildings. Rubble-Houses are environmentally friendly structures with walls comprised of recycled loose rubble placed in welded wire baskets. Rubble-Houses are assumed to be earthquake resistant structures due to their improved damping and ductility characteristics arising from the rubble and welded wire basket, respectively. However, the response of such structures under static and dynamic loads has not been studied in detail. In order to have a better understanding of its behavior, a full-scale rubble-house was built in the middle of Southern Polytechnic State University campus and subjected to a series of in-plane and out-of-plane static loads. Wall displacements were recorded using a 3D laser scanning technique in addition to total station and displacement gauge measurements. 3D laser scanning is an effective and efficient approach for precise and dimensionally accurate as-built documentation. This paper presents and discusses the use of 3D laser scanning technique in measuring the displacement response of a rubble-house under static loads.

Research and innovation in the design of sustainable intelligent buildings has gained much interest recently. Among various technologies, wireless sensor network is a promising option that enables real-time building monitoring and control. In addition to utilizing the collected data points for building operation management, it is attractive to analyze the real time building information for hazard detection. In this paper, the authors implemented an indoor wireless sensor network that transmits the sensed nearby air temperature. A machine learning algorithm is developed to analyze the collected data and automatically generate early warning signals for hazards. Experimental results validate the proposed algorithm and the effectiveness of using a wireless sensor network for early detection of building hazards.

Building components are subject to diverse changes throughout their lifecycles. Several tasks, such as construction quality control and structural health monitoring, require accurate information of the existing condition, and they involve comparing the current status of the building with the models to identify possible discrepancies. However, there is a lack of formalisms for representing the identified deviations so that they can be easily understood, evaluated and addressed by engineers and managers. Accurate and complete understanding of how, and the extent to which, buildings deviate from models is necessary for a number of decisions made throughout the building's lifecycle. This paper addresses this need by proposing a taxonomy for depicting geospatial deviations identified through comparison of as-is data (i.e. laser scanned point clouds in this paper) and building models. This taxonomy is an initial step toward formalizing the representation of geospatial deviations and communicating them in a machine-interpretable manner to support data-model comparison, and model evaluation tasks.

Tunnel surveying requires fast and accurate data processing because a tunnel excavation pattern and its work sequences are dependent on the surveying result in the field. While a total station is widely used for measuring reference points in tunnel projects, the data from the total station are too limited in implementing some of the functions required for project control. Recently, 3D laser scanners have been tested in some tunnel projects, because they generate high-density data within several minutes. On the other hand, software development to facilitate processing the resulting large dataset is essential for its wide adoption on tunnel projects. We propose efficient data processing algorithms to accelerate continuous data processing and to facilitate downsizing data points, considering the characteristics of tunnel projects using The New Austrian Tunneling Method.

Benchmarking and monitoring are critical steps toward improving operational efficiency of earthmoving equipment and minimizing their environmental impacts. Despite the importance, the relationship between operational efficiency and total pollutant emissions of these operations has not been fully understood. To establish such relationship and find ways to minimize the excessive environmental impacts due to reduced operational efficiencies, there is a need for an inexpensive and automated benchmarking and monitoring method. This paper presents a novel cost-effective method for monitoring carbon footprint of earthmoving operations using a vision-based equipment action recognition method along with pollutant emission inventories of construction actions. First a site video stream is represented as a collection of spatio-temporal features by extracting space-time interest points and describing each feature with a histogram of oriented gradients. The algorithm automatically learns the probability distributions of the spatio-temporal features and action categories using a multiple binary support vector machine classifier. Next, using a new temporal sliding window model, the equipment action categories are classified over a long sequence of video frames. The recognized time-series of equipment actions are placed in an emission and carbon footprint estimation model where based on the amount of emission for each equipment action, the overall Green House Gas emissions are analyzed. The proposed method is validated for several videos collected on an on-going construction project. The preliminary results with average action recognition accuracy of 85% reflect the promise that the proposed approach can help practitioners understand operational efficiency of their construction activities and minimize excessive environmental impacts due to reduced operational efficiencies.

A hard challenge associated with infrastructure monitoring is to extract useful information from large amounts of measurement data in order to detect changes in structures. This paper presents a hybrid model-free approach that combines two model-free methods - Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA) - to detect damage during continuous monitoring of structures. While a merit of MPCA is the ability to detect small amount of damage, an advantage of RRA is fast damage detection. The objective of this paper is to exploit these two complementary advantages through an appropriate combination. The applicability of this hybrid approach is studied on a railway truss bridge in Zangenberg (Germany). Its performance is compared with that of individual methods in terms of damage detectability and time to detection. Results show that the hybrid approach has higher damage detectability and identifies damage faster than individual applications of MPCA and RRA.

In the U.S., buildings account for 42% of the total energy consumption - half of which is consumed by commercial buildings. Knowledge of electricity use patterns in buildings has several applications in demand side management. In commercial buildings, acquiring energy consumption information with high granularity requires consuming node level sub-metering, which is prohibitively expensive. Accordingly, a cost effective, non-intrusive energy metering is needed to provide room level and personalized energy consumptions. This paper proposes a vision for an alternative non-intrusive load monitoring approach in commercial buildings. As part of the overall vision, the feasibility of non-intrusive lighting load monitoring using single wireless light intensity sensors has been explored. The experimental results show the feasibility of the vision introducing area of the room, the fluctuation amplitude of the captured signal, and the daily variation trend of the signal as some of the parameters and features that could be applied for machine-learning training purposes.

Cracks are important defects that can cause catastrophes if neglected. Visual inspection is currently the predominant method for crack assessment. This approach is labor-intensive, yet highly qualitative. Although several non-destructive evaluation methods have been proposed for inspection purposes, they cannot quantify crack thickness reliably. In this paper, a new contact-less crack quantification methodology, based on computer vision and image processing concepts, is introduced and evaluated. The proposed approach utilizes depth perception to quantify crack thickness.

Improving asset management of infrastructure systems has been an ongoing issue in the United States. Oliveira et al. (2010, 2011) developed several approaches to better understand the nature and location of pipe breaks in a drinking water distribution system. In this paper, we applied these two approaches to another infrastructure system..the pipe network of a sewage collection system. We first applied several classification approaches to analyze factors associated with higher density regions of deteriorating pipes in the sewage collection system. Relevant attributes that cause poorly conditioned sewer pipes could be found using this approach. We then applied the network version of a density-based clustering algorithm created by Oliveira et al. (2010), used to detect clustered regions of pipe breaks in water distribution systems, to detect hierarchically clustered regions in one of the high density regions of pipe deterioration in the same pipe network. This latter approach was found to provide useful information and additional insight about the local attributes that might be related to the high-density of pipe deterioration.

A variety of roadside objects along state highways contribute to a complex infrastructure system that needs to be efficiently managed by state departments of transportation (DOTs). State DOTs routinely collect data on roadside objects using a variety of sensing methods to meet regulation requirements, to evaluate road conditions, and to optimize their operations. Significant challenges exist in collecting and maintaining highway inventory data of sufficient detail and quality. There are thousands of miles of highways in each state. Collecting road inventory data along these highways often incurs significant cost. This research reviews mobile terrestrial laser scanning (MTLS) as an emerging highway inventory data collection method. The objective is to analyze the system components of MTLS systems and to investigate MTLS's data accuracy and data collection effort relative to the need of implementing a cost-effective highway asset management system.

Current pavement condition assessment methods are predominantly manual and time consuming. Existing pothole recognition and assessment methods rely on 3D surface reconstruction that requires high equipment and computational costs or relies on acceleration data which provides preliminary results. This paper presents an inexpensive solution that automatically detects and assesses the severity of potholes using vision-based data for both 2D recognition and for 3D reconstruction. The combination of these two techniques is used to improve recognition results by using visual and spatial characteristics of potholes and measure properties (width, number, and depth) that are used to assess severity of potholes. The number of potholes is deduced with 2D recognition whereas the width and depth of the potholes is obtained with 3D reconstruction. The proposed method is validated on several actual potholes. The results show that the proposed inexpensive and visual method holds promise to improve automated pothole detection and severity assessment.

Context awareness has the potential to improve various work processes in the construction industry, by automating the delivery of location specific information for a wide range of onsite information-intensive tasks, and supporting crucial management and decision making processes. Implementation of context awareness relies on an accurate and cost-efficient localization solution. Current indoor localization solutions require significant investments on hardware, and require users to carry additional beacons. This study addresses these limitations by proposing a solution that is built on the recent popularization of smart phones. The proposed solution collects the received signal strength indication (RSSI) data of all detectable WiFi access points (APs) in an indoor environment using the built-in WiFi sensors in smart phones. The RSSI data is compared to that of a set of known reference locations namely fingerprints, which is measured beforehand and recorded in a database. In this way, the nearness of a user with respect to each fingerprint in terms of the Euclidean distance is determined. A field test that covered three rooms and a corridor was carried out. The results showed that the solution achieved an average coordinate-level accuracy of 2.55 m and a room-level accuracy of 100%. The tests were repeated one and half months later and the results revealed the time invariance of the fingerprints, and the reliability of the solution over time.

Years of research have investigated factors that contribute to high-energy consumption in buildings. These efforts have primarily focused on consumption by building systems such as HVAC systems, lighting systems and appliances. Moreover, research efforts using simulation tools have been directed at influence of occupant behavior and how occupants interact with building systems and other energy consuming devices. Simulation tools can be rigid in depicting occupant behavior and therefore continued research to understand diversity and complexity of occupant behavior and appliance interaction is required. This research, examines occupant behavior in an office environment by sensing their daily activities and their interactions with energy consuming devices. Visual observation is used to detect occupant activities while non-intrusive appliance load monitoring is used for energy monitoring. Results from a five-week long period tracking daily activities of occupants of an office building and their energy consumption patterns are presented. This research has identified and tracked five commonly used office appliances and how they contribute to energy consumption. The objective of this study is to leverage the correlation between consumption and occupant usage data to create occupant awareness of how much energy they waste. The results of this study show that even though occupants seemed oblivious, turning off appliances when not in use can realize 38% energy savings. In addition, findings of this study indicate that there is a need for energy awareness and literacy campaigns to positively modify occupant behavior as a way to reduce energy consumption.

Anecdotal evidence suggests that physical strain may negatively affect workforce productivity and safety performance. Thus, an effective management of construction workforce physical strain could be very beneficial in improving construction productivity and safety. However, clear relationships between physical strain, safety, and productivity have yet to be established because of limitations in data collection procedures and technologies. This research has utilized recent innovations in sensing and communication technology to investigate the physical strain vs. productivity relationship. Data collected by physiological status monitors were analyzed through regression analysis that adopted heart rate as predictor of physical strain. Productivity and heart rate data of seven subjects performing a four-hour, simulated construction task were collected. The analysis showed that heart rate is a significant predictor with a strong parabolic relationship with productivity. Therefore, this research provided evidence of the physical strain vs. productivity relationship and, for the first time, proposed a mathematical formulation of such a relationship.

Following a disaster or an emergency situation (e.g., earthquake,) in a facility, it is crucial for emergency response teams to rapidly access navigation information of the facility, its contents and the final status of the facility (e.g., damages and vulnerable locations). However, in the current practice, accessing these information items is time-consuming and the accessed information is incomplete, since there are multiple sources of information that are mostly disorganized. This study proposes a Building Information Model (BIM) based approach integrated with sensors to provide the damage and vulnerability information of the facility for efficient response and for safe evacuation of the facility. The proposed framework integrates navigation algorithms, a vulnerability assessment approach and the status information obtained from various sensors that are strategically deployed inside the facility. This framework will be used for guiding the occupants and rescue teams through safe locations in a facility during evacuation.

Complex buildings such as hospitals and laboratories require intensive ventilation in order to meet operational demands. This research focuses on reducing cooling and ventilation loads in these types of buildings by incorporating hybrid ventilation in public spaces that do not require intensive air conditioning. Our research departs from previous works by justifying an optimal control strategy through experimentation rather than simulation. Ideally, designers would incorporate modeling to optimize building design and control, but these models are often not economical if created post-construction, and rarely result in accurate implementation of hybrid ventilation strategies. The method establishes a number of generic hybrid ventilation strategies to be tested in the public space of a complex building. The paper describes a multiple regression analysis of data to determine which controls and set-points are best for maintaining comfort while optimizing energy use. These controls and set-points are inputs for a building automation program developed to automatically determine whether a space should be naturally or mechanically cooled and actuate the analogous commands. It also calculates the expected energy savings associated with each strategy. This automated hybrid ventilation control is illustrated through a case study example, which has shown public space ventilation energy reduction of up to 56 percent.

Exposing building occupants to information about their energy use and the energy use of others in their social network through eco-feedback systems has been shown to significantly impact occupant energy consumption. In this paper, we describe the design and development of a web-based visualization system that exposes building occupants to real-time information about their individual energy utilization, the utilization of their peers in the building, and the average energy utilization of all monitored individuals in the building. The system also monitors and records user interaction with the system and other users of the system providing relevant usage data for conducting analysis to quantify network effects on user energy consumption. A description of the system's physical and virtual architecture and how its design enables meaningful analysis, visualization and effective user monitoring is provided. We conclude by presenting the methods and challenges related to how our visualization system was developed to facilitate and monitor social interaction around energy conservation, and how it enables research into the underlying mechanisms that drive these actions.

Construction projects are complex in nature, including multiple and conflicting project objectives and conditions. Although time and cost are traditional objectives that are considered in every project, environmental impact, such as greenhouse gas (GHG) emissions, has often been considered as another important objective recently. The complex construction environment makes it a challenge to determine construction methods that satisfy multiple objectives of a project. The complexity is further compounded by the need to consider the impact of changing project conditions on the time, cost and environmental impact (TCEI) to decide on the suitability of construction methods. In order to fulfill this need, this research discusses using system dynamics modeling to simulate and analyze various construction methods, as well as calculate their corresponding TCEI. The system dynamics model works with a decision-making system to select among construction methods and a project system to evaluate selected methods based on project objectives. The integration of the two systems has been applied to a case study of a highway construction project to determine a construction method that can best satisfy three potentially conflicting objectives as TCEI. The results have shown that the proposed model is effective in evaluating selected methods and suggesting resource utilization plans that can better fulfill the project objectives.

Buildings consume 39% of the total energy in the United States. To address the urgent challenge to improve the building energy efficiency, numerous digital analysis tools have been developed. However, there is a general lack of interoperability among these tools which limits the collaborative potential or creates added work when using BIM on projects. In order for the industry to solve this interoperability issue in a concerted fashion, an overarching process model describing the building process and the information exchanges within is needed. As integrated design makes it easier for project team to deliver high performance buildings, and HVAC systems are critical to building energy performance, this research has begun to map out the HVAC design process in an integrated design environment, in which various digital tools will be used. With the process maps, the information exchanges among various digital analysis and simulation tools can be identified. The process maps developed by this research can serve as a foundation for the industry to overcome the interoperability challenges. For the design teams that are not familiar with integrated design process, this process model provides a generic guide for design planning.

The building sector has consumed a significant portion of energy produced in the United States. In order to achieve Zero-Net-Energy (ZNE) for Buildings in the near future, designers need to consider energy consumption and CO2 emissions during planning and design stages. Benchmarking is the systematic process of measuring performance against best performers to determine best practices leading superior performance. Today, benchmarking, such as the Energy Star designation, is already being applied to measure building energy performance. However, questions still remain as to how effective the benchmarks are. Due to the uniqueness of each building and the dynamic nature of building operations, the value of dynamic and context-driven benchmarking is not fully understood. This paper presents a framework for using automatic data collection techniques, such as sensors, to contextualize and compare the energy consumption and CO2 emissions of a building. The authors also discuss how these benchmark data can be used in planning and design phases.

Daylighting performance is one of the primary metrics used for evaluation and selection of window systems. Radiance is one of the most accurate software tools for lighting simulations of buildings. However, it is difficult to use Radiance in the early stages of the daylighting design because of the complexity of the tool and uncertainty and variability of the design parameters. Repeated modifications of different design parameters are required in order to select appropriate design. As a result, simulation runtime is significantly increased. To solve the conflict between accuracy and length of simulation runtime, this research proposed a two-phase model for selection of window systems that can be used in the early design stages. The model uses Radiance for daylighting simulations and calculates the daylighting metrics by utilizing the optimization methods. A case study was conducted to illustrate the application of the model. This model not only provided relatively accurate daylighting simulation results but also reduced the overall simulation runtime.

Monitoring actual energy performance of existing buildings and measuring performance deviations from benchmarked values can help identify the potential areas for building retrofit. However, current building auditing practices include application of energy simulation tools or sensor networks, which can be time-consuming and may only provide system or appliance-level retrofit suggestions. In this paper, a quick and cost-effective approach is presented to recognize potential areas for building upgrade using digital and thermal imagery in addition to Computational Fluid Dynamics (CFD) models. In the proposed method, a dense 3D point cloud model of an existing building is first generated using an image-based 3D reconstruction algorithm. Next, thermal images are fused by a novel thermal mapping algorithm. The expected thermal performance is then simulated by CFD analysis using the dimensions extracted from the reconstructed 3D building model. Finally, the actual and expected thermal performance models are automatically superimposed within a new Energy Performance Augmented Reality (EPAR) environment wherein performance deviations are visualized. The proposed method is validated on several rooms in an existing instructional facility. The experimental results reflect the promise that EPAR models can be used as a building retrofit decision-making tool to facilitate the retrofit process.

Florida has become the third largest consumer of electricity in the United States. Its residential energy demand is about 1400 trillion BTU which is among the highest in the country. In an effort to reduce residential energy consumption, the State of Florida is conducting extensive efforts to improve the energy efficiency of existing building codes. One of the recent developments in this regard includes its decision to use more elements of the 2009 International Energy Conservation Code (IECC) as the foundation. The aim of this paper is to study the energy savings potential of the proposed 2012 IECC for residential construction as compared to the current Florida Energy Efficiency Building Code (FEEBC). A baseline model of a single family house was developed using BIM and simulations were conducted using different geographical locations in Florida with varying number of cooling degree days. After performing the energy analyses of the model for the selected cities using FEECBC, it was modified based on the proposed IECC 2012. The two sets of analyses were then compared to check the impact of those modifications on the overall energy performance of the baseline model house in each selected city. This research further strengthens the idea that the recent advancements in BIM/Simulation technologies play an important role in energy efficieny decision making.