Computing in Civil Engineering (2013)

A new methodology to determine the safety of suspension bridge main cables is proposed and illustrated on a corrosion-deteriorated cable composed of 9061 wires. The approach is the first one incorporating a finite element (FE) model to predict the cable's failure load, accounting for load recovery due to friction in broken wires and simulating the reduced cable's strength as a three-dimensional random field. In order to obtain the breaking load of a cable, the load is increased gradually (quasi-static loading) in a cable's FE model, having wires break a few at a time according to their residual strength. Because of the load transfer to surrounding wires, the breakage of an individual wire affects the stress state inside the surrounding wires. This local damage eventually causes a global reduction in the load-carrying capacity of the cable, up to a complete failure. The safety of the cable is determined through a Monte Carlo simulation, in which the reduced strength of the cable is generated for every realization through the Spectral Representation Method (SRM) and is input as a material parameter in the FE model. The statistics of the load that will drive a suspension bridge cable to failure under a hypothetical deterioration state are obtained at the end of the simulation.

Deployment of dense networks of low-power wireless sensors has been shown to be a cost-effective approach to structural monitoring that can generate massive volumes of data. Building a specialized cyberinfrastructure system is an efficient way to store and organize large volumes of data (sensor and metadata) in addition to processing it. Availability of structure metadata (e.g., geometric details, material properties, inspection histories) further enhances the post-collection analysis of the data collected. In this paper, a comprehensive cyberinfrastructure and its associated computing technologies are proposed to serve as the backbone of large-scale, permanent, structural, monitoring systems. A powerful data server called SenStore is described as the primary building block of the proposed cyberinfrastructure framework. This paper describes SenStore and its use in continuously monitoring the Telegraph Road Bridge (Monroe, MI) using a permanently installed wireless sensor network.

In the U.S., many drinking water assets operate beyond their designed lifetimes, and constrained resources necessitate optimizing repair and replacement. For pipe infrastructure, clustering tools can characterize their susceptibility to failure by identifying relationships among descriptive or measured features. In these complex systems, algorithmic learning approaches can provide a first insight before expert knowledge is applied, reducing time and labor. The state-of-the-art techniques often rely upon static characteristics. In this work, pipe maintenance records are analyzed through the network OPTICS ("Ordering Points To Identify the Clustering Structure"), which forms a hierarchal, density-based, clustering structure. The study compares and extends OPTICS to a temporal context; exploring the evolution of clustering structure provides additional insight. The findings suggest this spatiotemporal approach is applicable for improved asset management.

A common approach used by engineers to monitor and analyze data collected from piezometers installed in embankment dams is to generate time history plots and visually identify any spikes or anomalies in them. However, such practice has several limitations when capturing complicated relationships among a number of factors that affect piezometric readings. This is especially true when periodic or dominant variations that exist in time-series data are of concern, given that environmental and process noise can sometimes mask these variations. In this paper, we propose applying Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA), which have been shown to be successful in other applications, to extract relevant components and detect anomalies in piezometer measurements, which are one of the most important data to be monitored when evaluating the performance of embankment dams. The proposed anomaly detection method provides a more efficient way of understanding and detecting changes in piezometer data.

Wireless control systems have advantages of flexible installation, rapid deployment, low maintenance cost and low power consumption compared to wired control systems. The attractive features of a wireless system - as well as the rapid development of wireless sensor hardware, software and middleware - make wireless control a promising field. However, there are also issues associated with wireless control systems such as network-induced delay and data loss. These effects can seriously affect the control performances and may even cause instabilities in the systems. In this paper, a novel data utilization and control strategy is proposed for wireless control system with Time Division Multiple Access (TDMA) network. The proposed strategy utilizes the maximum throughput of TDMA network and is evaluated using a wireless control and monitoring simulator (WCMS) for benchmark active mass driver (AMD) control system. The WCMS consists of realistic structural control components and network components. The structural control components are built in Simulink® (Matlab®) and network components are built in wireless network simulation tool - TOSSIM. The proposed strategy is shown to be effective in controlling the AMD system with TDMA network induced delay and data loss impact.

Owing to the complexity of civil structures, structural behavior predicted by finite element models (built according to design drawings) is usually different from behavior of an actual structure in the field. To improve the prediction accuracy, finite element (FE) model updating can be performed based on sensor measurement from the actual structure. Numerous FE model-updating algorithms have been developed in the past few decades. However, most existing algorithms suffer computational challenges when applied to large structures. The challenges are usually because most existing algorithms attempt to solve non-convex optimization problems. As a result, the optimization process encounters convergence difficulty and cannot guarantee global optimum. To address the issue, this paper proposes an iterative convex optimization algorithm for FE model updating. The convex attribute of the optimization problem makes the solution process tractable and efficient. To validate the proposed algorithm, numerical simulation is conducted with a plane truss structure. The proposed approach successfully updates the simulation model and shows certain advantage over a conventional FE model updating algorithm.

The research team at the University of Arizona proposed several novel structural health assessment (SHA) algorithms. Structures are represented by finite elements (FE) and the health is assessed by identifying the stiffness parameters of all the elements and comparing them with expected values or with previous values, or by observing differences between similar elements. They can identify the location and severity of defect and exact location within a defective element. These algorithms use several system identification- (SI-) based concepts with different levels of sophistications. They do not require excitation information and can assess the health of large structural systems using only limited noise-contaminated acceleration time-histories measured at a small part of a structure. They are widely available in the literature. However, algorithmic and computational rigors of them are generally not presented in technical papers due to severe page limitation. Some of them are briefly presented in this paper without discussing the specific algorithms.

Glass fragments are a prime source of injury to occupants of buildings subjected to explosive events during which window glass breaks into flying shards or fragments that account for injuries ranging from minor cuts to severe wounds. A successful, blast-resistant glazing design considers the principles of a reasonable degree of protection against explosive threats based on the proposed level of security and previous lessons learned. It also requires balancing of the safety and security of the window panels with physical appearance and cost-effectiveness. Therefore, it is necessary to have a better understanding of the response of a glass panel to different glazing system parameters and blast load characteristics. In this study, an explicit finite element analysis is used in conjunction with fracture micromechanics' principles to model the failure of annealed and fully tempered glass panels. The response of the conventionally-framed window panels to blast loading cases is dependent upon the glazing material, blast intensities and glass imperfection locations and types. These results will help develop a more comprehensive flying glass injury model that will eventually enable the decision makers to use the appropriate structural, architectural and building perimeter choices to better address the threats facing the safety of the occupants during an air blast.

Optimal structural control design for controllable passive dampers is difficult and computationally expensive. Even with a linear structure, the controlled system is nonlinear since the control appears parametrically and the damper can only exert dissipative forces. The authors previously introduced a novel method based on a low-order, nonlinear, Volterra integral equation (NVIE) that provides a computationally efficient means to simulate responses for such systems. Thus far, the authors have considered deterministic models with semiactive control devices; however, uncertainties in key structural elements cannot be neglected in real-world systems and may intensify the computational burden, generally demanding a Monte Carlo simulation to characterize the effects of uncertainty. Herein, a 100-degree-of-freedom, isolated building model with a controllable damper in the isolation is studied with uncertainty in the isolation rubber bearing stiffness and damping. Numerical results confirm the accuracy, stability, and computational efficiency of the proposed simulation methodology.

Controllable damping studies for seismic protection of elevated highway bridges found that a clipped LQR strategy achieves performance similar to that with a fully linear actuator when attempting to reduce the bridge deck response, but only comparable to that with a purely passive damper when seeking to reduce pier response (Erkus et al., 2002). This paper examines this more challenging pier response problem using a dynamic programming approach to obtain the optimal dissipative control force - assuming the excitation is deterministic and known a priori - through a numerical solution of the Euler-Lagrange equations. The resulting (non-causal) optimal performance, an upper bound on the performance of any controllable damping strategy, is still rather lower than active control performance (which is causal, requiring no future excitation information). This result suggests that even highly adaptive control laws to command controllable dampers may never approach fully active system performance for some control objectives.

Linear structural control strategies generally cannot focus on different objectives in different excitations. Yet, this is a goal that has real meaning in many structural control applications. For example, minimizing structural drift is necessary during strong earthquakes to mitigate damage to the structure; however, occupant comfort and the safety of building contents and nonstructural components in the much-more-frequent moderate earthquakes demand reductions in absolute accelerations. Drift and acceleration reduction in different excitations are competing objectives that cannot be achieved with conventional linear strategies. Herein, a control strategy is presented that achieves significant improvements in both serviceability (acceleration mitigation) in weak/moderate excitations and life safety (drift mitigation) in extreme events, all in one general control law. A numerical study of a simple single degree-of-freedom system, using scaled records of several historical earthquakes, controlled with conventional LQR approaches and the proposed control, demonstrates that the proposed control law successfully accomplishes both goals.

Substructure identification is a structural health monitoring technique that identifies a reduced order model of structural behavior using local vibration measurements. Previous research by the authors on this inherently decentralized technique has shown that substructure identification can detect and localize stiffness changes in a shear building. However, identification performance varies from story to story, with a few not satisfactorily identified. To overcome this limitation, Zhang and Johnson (2012c) showed how to use a structural control device to temporarily change the dynamics of the structure to improve identification performance. However, this method assumes full state feedback which is not achievable in full-scale implementations. This paper investigates structural control using limited sensor measurements to improve substructure identification. A 12-ft., four-story, single-bay, steel structure model subject to low levels of ground motion is used as the test-bed for numerical simulations. Different combinations of acceleration sensor locations are compared to understand the performance tradeoffs.

Preventive maintenance of roadway pavements requires reliable estimates of pavement deterioration. Existing successful measurements of pavement deterioration rely on knowing the statistical distribution of traffic loads in the roadway. Existing systems are expensive to deploy. This paper describes a sensor network system for road pavement monitoring. It is capable of estimating the load of trucks in real-time and utilizes low deployment and maintenance cost wireless sensors. The approach relies on combining careful pavement models with data measured in real time and provides a reliable estimate of truck loads. Based on this estimates, we are able to infer various pavement performance metrics under strict bounds.

Propagation of nonlinear waves in solid waveguides is a branch of wave mechanics that has received an ever-increasing interest in the last few decades. Nonlinear guided waves are promising candidates for interrogating long waveguide-like structures as they conveniently combine high sensitivity to peculiar structural conditions (defects, quasi-static loads, instability conditions) typical of nonlinear parameters with large inspection ranges, characteristic of wave propagation in confined media. However, the mathematical framework governing the nonlinear guided wave phenomena becomes extremely difficult when characterized to waveguides that are complex in either material (damping, anisotropy, heterogeneous, and the like) or geometry (multilayers, geometric periodicity, and the like). Therefore, the successful use of nonlinear guided waves as a structural diagnostic tool is not always a trivial task. In particular, the efficiency of nonlinear Structural Health Monitoring(SHM) techniques based on higher-harmonics generation(harmonics generated by a monochromatic input in nonlinear waveguides) strongly relies on the correct identification of favorable combinations of primary and resonant, double-harmonic, nonlinear wave modes for which the nonlinear response is cumulative. The present work develops predictions of nonlinear second-harmonic generation and identifies these combinations of wave modes in complex waveguides by extending the classical Semi-Analytical Finite Element formulation to the nonlinear regime and implementing it into a highly flexible commercial Finite Element code. The proposed algorithm is benchmarked for the following case-studies: a railroad track, a composite quasi-isotropic laminate, and a reinforced concrete slab.

Site-specific wind field characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine learning approach towards analyzing and predicting the response of wind turbine structures to varying wind field characteristics. Machine learning algorithms are applied (i) to better understand changes of wind field characteristics due to atmospheric conditions, and (ii) to gain new insights into the wind turbine loads being affected by fluctuating wind. Using Gaussian Mixture Models, the variations in wind field characteristics are investigated by comparing the joint probability distribution functions of several wind field features, which are constructed from long-term monitoring data taken from a 500-kW wind turbine in Germany. Furthermore, based on Gaussian Discriminative Analysis, representative daytime and nocturnal wind turbine loads are predicted, compared, and analyzed.

For civil structures, structural damage due to excessive loading or environmental degradation usually occurs in localized areas. A new, sparse, Bayesian probabilistic approach for computing the probability of localized stiffness reductions induced by damage is presented that uses noisy, incomplete modal data from before and after possible damage. The methodology employs a hierarchical form of the prior for the stiffness parameters that promotes spatial sparsity in the inferred stiffness reductions. To obtain the most plausible model of sparse stiffness reductions together with its uncertainty within a specified class of models, the method employs an optimization scheme that iterates between the groups of modal parameters and hyperpameters. The approach also adopts a recent published strategy for Bayesian dynamic model updating based on modal data that has two important benefits: (1) no matching of model and experimental modes is needed, and (2) solving the eigenvalue problem of a structural model is not required. For validation, a three-dimensional braced-frame model with simulated data from the Phase II benchmark problem sponsored by the IASC-ASCE task group on structural health monitoring is analyzed using the proposed method. The results show that no threshold is required to issue a damage alarm for the proposed approach and the occurrence of false-positive and false-negative damage detection is clearly reduced in the presence of modeling error.

This paper presents a condition assessment approach of cables incorporating experimental deteriorated model and in-service loads collected by SHM system. The high-strength steel wires were placed under simulated corrosive media based on the Salt Spray Test Standards. The geometric feathers of the corroded steel wire surface were measured by to generate the uniform and pitting corrosion depth model. The long-term deterioration process of steel wires in the cables considers simultaneously the experimented uniform and pitting corrosion due to simulated environmental attack and the fatigue propagation induced by monitored cyclic stress. By employing first order reliability method, the reliability of the cables under the monitored responses is further estimated in terms of the serviceability specified in the design specification. The life-cycle condition assessment of stay cables provide guidance to the future decision making related to maintenance and replacement.

Management of our infrastructure systems is not only associated with engineering and economic constraints; it is intricately tied to public policy and institutional control considerations. As such, using simulation models that incorporate aspects of population movements, infrastructure assessment, living conditions, and health data with spatial data modeling, and collectively relate them to the different sustainability indicators could be an ideal tool for providing better information for the sustainability of our infrastructure systems. This paper presents a systems modeling approach to assess the social, environmental, and economic changes within community host systems as a result of civil infrastructure projects, and provide better information to associated stakeholders. In previous research, the author developed three novel benchmarks (i.e. nature, work, and flow) to holistically analyze our infrastructure systems from a sustainability perspective. The "work benchmark" defines the behavioral relationships between the construction processes and the associated stakeholders; the "nature benchmark" studies the interactions between the construction processes and the surrounding ecosystems, and the "flow benchmark" will analyze the overall system change. In this paper, the author shows how the "flow benchmark" will be used to demonstrate technological integration between macro-level system dynamics modeling, micro-level agent-based simulation, and multi-objective optimization to measure the overall system change. Integrating these techniques in a single framework will combine the strengths of the three methodologies and mitigate their deficiencies on stand-alone basis. This aggregation will evaluate societal vulnerability through analyzing which sustainability indicators have the most significant correlation to the affected infrastructure systems. This will create a knowledge base of simulated dataset to understand the micro-level behaviors of community stakeholders in relation to the societal macro-level sustainability outcomes. The envisaged system will analyze qualitatively and quantitatively the various sustainability indicators to help attain Pareto optimal resource balance in our communities. When successful, this innovative systems framework will provide life-cycle analysis method for estimating the probability distributions of financial cost, loss of service and societal costs, and environmental impacts. This will answer important management questions such as "what should we do?", "what if we do?', and "what are the expected societal consequences?", which will promote informed management and decision making.

With an increasing demand for building energy performance, energy audits are being adopted more often by commercial building owners in order to determine the feasibility of building retrofits. There are many resources available for energy audits such as suggested data collection forms. However, there is not a standard that defines the information exchange for the energy auditing processes, which makes it difficult to leverage the information exchange to benefit downstream energy modeling. This paper describes the research on a standardized input data structure for energy audits in order to help define the energy auditing protocol for retrofit buildings. There search defines the standard required input data structure for Level II energy audits based on the content analysis of the current audit data templates and energy modeling input requirements. By aligning the data with the main energy modeling software tools, the consistent categories of data for energy audits are extracted. The benefits of the adopted approach are presented and future research trajectories are outlined.

In the context of pre-disaster investment, we propose an unconventional procedure to prioritize links by evaluating the impacts of their failures on the expected network performance. Our proposed procedure is termed as Link-based Categorization of Network States (LCNS) in the sense that network states are categorized according to each link's failure. In this unconventional method, links' criticality indices are cumulatively calculated, as most probable network states are sequentially enumerated. Our procedure is applicable to the situation when there is no prior knowledge concerning the effect of pre-disaster investment on the link survivability. Moreover, LCNS does not cause any redundancy in enumerating network states. An implementation of the proposed procedure on an idealized transportation network reveals that component links' ranking in terms of criticality indices is converged with only 0.2% enumeration of total network states plausible under a disaster.

Buildings accounted for nearly 75% of the electricity use in 2010, the largest portion among all sectors in the United States. Despite their contribution to the overall electricity demand, they have traditionally been considered as passive end-users of energy, and research in building engineering has largely focused on solutions to curtail their energy usage and improve energy efficiency. However, recently, the potential of buildings to become active participants in the electricity grid by providing ancillary services via direct load control has been garnering interest in the research community. In this paper, we introduce different demand response programs that use thermostatically controlled loads (TCLs) available in buildings. Specifically, we shed light on the existing work on direct load control for TCLs and identify the upcoming challenges associated with this approach. Finally, we introduce BUFFER: the building frequency forecast and electricity regulation framework, a novel decentralized and autonomous framework that uses TCLs in buildings to do frequency regulation for the power grid.

A system dynamics model first developed using the software STELLA, which explores the complex behaviour of the financially sustainable management of wastewater distribution infrastructure, was converted here into a system of coupled non-linear differential algebraic equations (DAEs). Each differential equation involved a time derivative on a primary variable specifying the temporal evolution of the system. In addition, algebraic (secondary) equations and variables specified the non-linearity inherent in the system as well as any controls on the primary variables constraining the physical evolution of the system relevant to the problem at hand. While STELLA employed a Runge-Kutta numerical strategy, the numerical DAE method used a fully-explicit scheme combined with a fixed-point iteration to resolve the non-linearity. The Runge-Kutta and numerical DAE solutions deviate markedly when the non-linearity of the system becomes pronounced. We demonstrate point-wise stability of the numerical DAE solution as the timestep is refined.

With environmental concerns as another dimension of constraints in the built environment, life cycle assessment (LCA) has gained tremendous importance. It has been commonly accepted among researchers that environmental impacts of construction operations need to be analyzed and monitored throughout the whole life cycle, including raw material extraction, manufacturing, transportation, construction, and end-of-life steps. Furthermore, life cycle approach has brought the concept of systems thinking into the built environment to understand the interactions among various systems and elements of these systems. Previous studies have focused on modeling construction projects by using systems thinking. This research builds on these studies in using the results of construction system models to optimize multiple project objectives. Environmental impact is set as a new project objective to reflect environmental concerns throughout the project life. Time and cost are selected to be optimized with environmental impact considering their importance for owners and contractors. Multi-objective optimization is used as an effective tool to optimize these three potentially conflicting objectives in construction projects. This paper aims to model the optimization process of time, cost and environmental impact (TCEI) with the help of an LCA approach and multi-objective optimization techniques. LCA will allow observing and analyzing project objectives (TCEI) throughout the life cycle of the project, while the multi-objective optimization procedure will help to view the interdependency of time, cost and environmental impact by considering the life cycle of construction projects.

Simulation tools can provide valuable information for daylighting system design while optimization methods can determine feasible solutions from a number of uncertain design parameters. However, optimization methods allow for limited interactivity and only consider several performance criteria. Therefore, to utilize experts' domain knowledge, this research presents a rule-based expert system based on OpenStudio platform, which integrates daylighting and thermal simulations. The expert system can interpret and compare different design changes and options based on the pre-defined rules and simulation results, and can provide possible solutions in the case of failure to meet design performance criteria. The users can interactively provide alternative design options to the expert system for evaluation based on users' knowledge. The expert system can provide guidance to evaluate daylighting design.

Today's engineering problems require complex solutions from multiple disciplines. Hence, engineering classes should teach students to learn about how to solve multidisciplinary problems. This paper describes a project-based course that allows a group of students to learn and utilize knowledge and approaches from multiple disciplines and to gain systems engineering perspective to solve a real-world problem. The objective of the project is to develop a system that can facilitate the monitoring and analysis of the performance of HVAC systems, which account for about 40% of total energy consumption in buildings. Students with various backgrounds worked together to develop a prototype system that automatically collects dynamic data from HVAC systems and visualizes the real-time and historical data with floor plans using a web-browser-based interface. This prototype system also embeds fault detection and diagnosis (FDD) algorithms that frequently analyze the performance of HVAC systems. In developing such a system, the students had to learn and utilize knowledge in the domains of civil, mechanical, electrical engineering, computer science, and project management, and approach the problem from a systems engineering perspective. This paper discusses the processes that the students went through to learn, implement and test the system in a real-world testbed.

One of the key factors for enabling sustainable construction is facilitating the acquisition, transfer, and exchange of knowledge about sustainable construction practices (SCPs). However, knowledge about SCPs is complex, interdisciplinary, multi-faceted, and context-sensitive. Semantic modeling is a key to achieving context-awareness. The development of a context-aware knowledge management (KM) system can be successfully achieved based on a semantic (computer-understandable and meaning-rich) model. This paper proposes an epistemic model for SCPs based on epistemology (the theory of knowledge and knowing). The proposed epistemic model is a theory-based, semantic model for representing and reasoning - in a context-aware manner - about: 1) the knowledge of SCPs and 2) the process of knowing this knowledge. This paper starts by presenting our analysis of the requirements of sustainability KM in the construction domain and the limitations of existing methods and systems. It follows by presenting our modeling approach for KM system development, which is epistemology-based, context-aware, and semantic. Finally, the paper presents our initial modeling efforts towards a formal epistemology for SCPs and discusses its application as the backbone of our proposed KM system.

For energy efficient retrofit projects, interactions of integrated design teams have a significant impact on the life-cycle energy saving decisions. While evaluating design options, the team must consider results from various simulation tools (e.g., energy, day-lighting) with the context of the 3D model. However, traditional communication creates a barrier for integrated design teams to make decisions efficiently as information is frequently conveyed through documents in tabular formats, and perceived outside of the context of the 3D representation. One of the primary needs of the team is to integrate the assessment of options with simulation results. This research focuses on using immersive visualization to support multi-stakeholder decision-making processes for advanced energy retrofit projects. An immersive virtual mock-up was developed for visualizing energy modeling assumptions and energy efficient metrics to facilitate integrated design team interactions toward evaluating retrofit alternatives. The mock-up incorporates and enables integrated visualization of a 3D model from a Building Information Model (BIM) application and engineered data from simulation tools, and was developed and compared using a commercial immersive visualization environment and a real-time 3D game engine. This paper provides a comparison of the process for developing the virtual mock-up in two different environments, and lessons learned including the challenges related to exchanging semantic information and developing reusable modules for visualizing simulation results in virtual environments. These lessons will inform the development of a repeatable process that can be scaled to different types of buildings and design decisions.

Occupant comfort is a dominant influence on the performance of HVAC operations. Most HVAC system operations rely on industry standards to ensure satisfactory environmental conditions during occupancy. Despite the increasing building energy consumption rates, occupants are not usually satisfied with indoor conditions in commercial buildings. To address this issue, in this paper, a framework for integrating personalized comfort preferences into HVAC control logic is introduced. As part of the framework, a user proxy, a comfort profile learning algorithm, and a building management system (BMS) controller are presented. The performance of the framework in a real building setting has been evaluated. The framework was successful in a small-scale experiment in increasing efficiency by improving user comfort and slight decrease in collective energy consumption.

Managing change and knowledge is one of the key factors that contribute to a successful energy efficient retrofit project. Due to the constraints of information, time, space and working environment, a retrofit project is more complex than expected. The significance of managing changes and dependencies is that if they are poorly or inadequately managed, changes initiated and implemented during the design, construction and operations of buildings could potentially impact negatively on energy efficiency goals. Similarly, knowledge assets are often disconnected from project teams. Consequently, lack of access to the right knowledge by project teams could result in the design and retrofitting of energy efficient buildings which do not meet the owner's goals. Furthermore, there is potential for lessons to be learned during the process of managing and implementing changes. However, this kind of information is usually not properly captured, validated and stored within a knowledge management system (KMS). To do this more effectively, the change and knowledge processes need to be integrated at the points of interactions. This paper presents an approach to integrate change and knowledge management (CKM) processes. It highlights the importance of the CKM practices in energy-efficient retrofit projects, and describes how lessons learned from change events can be captured and stored as knowledge in a KMS. Furthermore, it explains how a KMS can be expanded into a change and knowledge management system (CKMS) to support the new approach. In the concluding part of the paper, the key benefits of the proposed approach are highlighted and the approach to be taken towards the practical implementation of a CKMS is outlined.

Access and exchange of building information in a timely and standard manner between construction project teams and applications during design and construction plays a major role in assuring efficient building energy simulation during retrofitting. Yet, in spite of several efforts in major technological developments in information management, building data and information remains highly fragmented and frequently inaccessible to project teams. This paper presents a table for categorization of information exchanges items and illustrates their dependencies between internal process activities, and between activities of different processes. The paper uses the HVAC design process as a baseline to map-out the information input-and-output relationships of various activities and shows how an information item produced by an activity is consumed by another, thus creating information exchanges dependency. The table can serve as a reference point for project teams and for software developers to understand interoperability requirements of process activities and applications. It will also be beneficial to analyze impact of changes as change to an information exchanges item can impact on other activities where it is required as an input. The paper concludes by formulating a roadmap to utilize the table in order to support development of Information Delivery Manual (IDM) and other information exchanges initiatives.

Successful building diagnostics requires quick and reliable identification of energy performance problems, and an accurate assessment and cost analysis of the associated energy loss. Today, most auditors rely on thermal imagery for analyzing energy performance problems. Nonetheless, current thermographic inspections are qualitative and thus mainly focus on the visual detection of thermal irregularities. As a result, the quality of the retrofit decision-making is based on how these images are interpreted by the auditors. To overcome these challenges, this paper presents a new method for calculating the cost associated with potential building energy performance problems. First, based on the Energy Performance Augmented Reality (EPAR) modeling method, based on the Energy Performance Augmented Reality (EPAR) modeling method, based on the Energy Performance Augmented Reality (EPAR) modeling method, actual and expected 3D spatio-thermal models of the buildings under inspection are generated and integrated in a 3D environment. This method leverages thermal and digital images captured by a thermal camera for actual energy performance modeling, in addition to computational fluid dynamics (CFD) analysis for expected energy performance simulation. Based on thermal 3D mesh modeling and threshold-based assessments of temperature deviations, building areas with potential performance problems are detected within the EPAR models. By locating a small crumbled aluminum foil on an inspection surface, the reflected apparent temperature is measured. For areas with potential performance problems within EPAR models, this measurement is used to calculate the actual R-value at the level of 3D points. This information is finally used to calculate both heat loss and gain and also to estimate the associated energy costs. The presented method is tested on several interior locations of an existing residential building. Experimental results and the benefits of converting temperature data sensed from building surfaces into actual R-values and the cost of heating and cooling loss are discussed.

Retrofitting existing buildings has the greatest opportunity to reduce the energy consumption in the building market. Even though the opportunity lies in making these design decisions collaboratively, it is difficult to come up with "best project fit" with current techniques by considering all disciplines' priorities. Especially, deep retrofits that provide higher energy targets require integrated design approaches that include the perspectives and priorities of the various disciplines through collaborative decision making within the limits available budget. The aim of this project is to define the critical decisions - when, why, and how these decisions are made in contrast to other design options - by the decision makers in the team. Two deep retrofit case studies will be presented and will deliver integrated process maps and comparative analysis using different delivery methods. The maps and decision criteria are being used in the development of a Decision Support System (DSS) aimed at providing guidance to retrofit project teams by increasing the transparency of the decision making process and highlighting the energy efficiency implications of design alternatives.

The quantitative sustainability assessment of urban systems relies on available predictive models for these complex systems of systems. Markov chains and Markov Decision Processes are among the most used tools to integrate the stochasticity in decisions related to infrastructure management. In the present paper, we investigate the effects of uncertainties in the characterization of these decision models and make the case for the development of rationales that can differentiate between them. We will explain the extension of the theory of random transition matrices developed by the authors to Markov Decision Processes and illustrate, using a numerical example, that the policies obtained by solving the deterministic transitions is not necessarily robust to the potential variabilities of transition rates. The resulting probabilistic framework for Markov Decision Processes will also enhance the maintenance of urban systems by rigorously quantifying the confidence in the sustainability assessment metrics.

Occupancy-focused interventions used to reduce building energy use (e.g., feedback) are exhibiting low and un-sustained energy reductions. Research on residential buildings has shown that the type of building social network determines the effectiveness of these interventions. However, the findings cannot be directly applied to commercial buildings given their more complex social structures formed by independent entities (i.e., companies) with different organizational structures and cultures. Therefore, the objective of this study is to evaluate the influence of social sub-networks on the energy conservation from occupancy-focused interventions. An agent-based model was developed for this purpose simulating sub-networks in a typical commercial building environment. Occupancy interventions were then tested while varying the number of sub-networks in the building. Finally, statistical analyses were performed showing that modeling sub-networks significantly affect the effectiveness of occupancy interventions and need to be accounted for in the design of more efficient interventions for commercial buildings.

We propose a conceptual framework that couples energy modeling with occupancy characteristics and energy use data to achieve comprehensive building energy consumption analysis. Specifically, we aim to: (1) couple distinct and spatially distributed simulation models and synchronize their data exchange; and (2) demonstrate the coupled simulation through a hypothetical case example of a building. This framework has been developed based on the principles defined in the High-Level Architecture (HLA) that enables distributed computing. In other words, the HLA-compliant federation allows federates (e.g., models) to communicate with each other and exchange relevant information to achieve the global objective of analyzing and reducing the building's energy use. A case study of a typical commercial building illustrates how the federates coordinate data synchronization and run in a distributed fashion. This example tests feedback frequency to building occupants on the building's energy use and illustrates the potential application of the framework to study energy interventions in buildings.

This paper uses an agent-based model to illustrate how social networks and agent interactions can help stakeholders of an existing building prioritize their values of cost awareness, energy saving and organizational performance, to ultimately come up with an optimal building retrofit decision. The model is tested using a small-world network to see how the network structure affects the overall retrofit priority in a medium-size building case study. The agents interact based on the bounded confidence model, which may result in continuous changes in their three values over time. The results indicate that, although sustainable retrofits are generally decided on from economical and environmental perspectives, incorporating social values may help the owner choose an approach that can reduce energy consumption while maintaining tenant comfort and wellbeing. Further, the time to reach convergence in values was highly dependent on the owner's initial values and how extreme these were relative to the other stakeholders.

For the planning of large infrastructure projects, such as inner-city subways, widely differing scales have to be considered - ranging from the scale of several kilometers for the general routing of the subway down to centimeter scale for the detailed planning of subway stations and hubs. At the same time, the strongly inter-disciplinary character of these projects necessitates an intensive exchange of data between the individual stakeholders. Data exchange on a high semantic level is supported by the technology of product models. However, currently available product models do not support the multi-scale aspects required to properly handle large infrastructure models. This paper contributes to closing this technological gap by proposing a general methodology for developing multi-scale product models which combine semantic and geometrical aspects in a consistent and coherent manner. The methodology is illustrated by the development of a multi-scale product model for shield tunnels. In addition, methods for the automated preservation of the consistency of geometric representations at different scales are discussed.

This paper presents ongoing work on data collection and collation from a large number of laboratory cement-stabilization projects worldwide. The aim is to employ Artificial Neural Networks (ANN) to establish relationships between variables, which define the properties of cement-stabilized soils, and the two parameters determined by the Unconfined Compression Test, the Unconfined Compressive Strength (UCS), and stiffness, using E₅₀ calculated from UCS results. Bayesian predictive neural network models are developed to predict the UCS values of cement-stabilized inorganic clays/silts, as well as sands as a function of selected soil mix variables, such as grain size distribution, water content, cement content and curing time. A model which can predict the stiffness values of cement-stabilized clays/silts is also developed and compared to the UCS model. The UCS model results emulate known trends better and provide more accurate estimates than the results from the E₅₀ stiffness model.

Design and construction of earth-retaining structures in cold regions require special consideration for potential impact of frost heave. During the freezing process, additional earth pressure can be generated by frost heave as a result of the formation and growth of ice lenses in soil. The additional earth pressure due to frost heave may cause unacceptable deformation of the structures - or even failure - if it is not properly considered in the design. This paper presents a numerical approach to simulate the freezing process and associated frost heave in soil. In this approach, soil is treated as a mixture of solid particles, unfrozen water, and ices. The expansion of soil due to frost heave is described mathematically using the Porosity Rate Function, where the porosity rate (i.e., change of soil porosity over time) depends on the water transport into the freezing zone, which is governed by the soil temperature, temperature gradient, and stress. The heat transfer and frost heave models have been incorporated into a finite element program for numerical simulations. The numerical approach was used to evaluate a concrete cantilever retaining wall under freezing conditions. The simulation yielded realistic results of ground and wall deformation.

We present and review the Granular Element Method (GEM), which is a discrete mechanics simulation technique that uses Non-Uniform Rational Basis Splines (NURBS) for capturing particle morphology and inferring grain-scale kinematics and forces at computer-tomographic resolutions. The target application of GEM is in analyzing granular systems in advanced experiments utilizing visualization tools such as X-ray computer tomography experiments (XRCT). In this context, GEM may help in facilitating the transition from binary data from XRCT to discrete models, as well as lead to more realistic predictions at the grain scale.

This paper discusses requirements to enhance the current decision-making process for tunnel construction, which involves significant uncertainties in ground conditions. In current practice, project managers determine resource-loaded construction schedules based on one specific set of ground conditions. In addition, they do not update ground conditions for unexcavated sections with information from excavated sections. This decision-making process often causes cost overruns, schedule delays, and disputes. To mitigate these problems, managers must make timely and robust decisions about construction schedules, during both the preconstruction and construction phases, in a transparent and integrated manner. To this end, we applied a 4D model-based decision-support system, the Development Strategy Simulator (DSS), to a U.S. tunnel project. Our analysis showed that the DSS was able to simultaneously analyze and represent the construction schedule and the resulting resources. However, we argue that it is still necessary to develop a progress-adaptive stochastic earth model, as well as a formal process for the automatic formulation and evaluation of multiple alternative schedules.

The flow rate of granular material depends on particle characteristics, including: mineralogy, shape, angularity, surface texture, grain size and size distribution. This paper discusses computational and numerical techniques used to measure material characteristics (input) as well as computational experimentation and measurement techniques used to evaluate material flow rate (output) from a hopper. X-Ray Computational Tomography (XCT) image analysis is used to measure sample porosity and porosity distribution in both 2D and 3D, while microscopic image analysis methods are used to compute the angularity and shape factor of sample particles. With these input characteristics, we can evaluate the flow rate of a sample using Discrete Element Method (DEM) Modeling, which can then be compared to and verified by physical experimentation.

Computed tomography (e.g. X-ray CT) has provided unprecedented 3D images of microstructures with the scale of individual voxels on the order of a few micrometers. The technique has been especially powerful in granular media, where a time sequence of snapshots containing thousands of grains can be collected in situ for a given load path. In turn, this has opened up great opportunities in relating the changes in granular microstructure to average mechanical properties and has offered a clear path to move beyond phenomenological constitutive models. A shortcoming of X-ray CT is that it provides images or photographs which then must be converted into usable geometric data. Here, we introduce a framework for this purpose, which we term `Level Set Bridge'. The proposed level set framework provides a basis for a comprehensive geomaterial characterization, and ultimately serves as a direct link to computational methods in geomechanics. We briefly showcase the technique through examples of a natural material, Caicos ooids, consisting of highly rounded particles. In particular, we use X-ray CT images of ooids under a triaxial load sequence to quantify local grain-scale geometry and kinematics, and chart a path toward extraction of granular fabric.

Soil-structure-interaction (SSI) effects are typically non-negligible for structures that possess one or more of the following attributes: a massive super-structure and/or foundation elements, a large uninterrupted footprint or interface with the soil domain, and a soft, supporting soil medium. The SSI effects comprise the dynamic interaction between: (1) the far-field soil domain; (2) the (potentially inelastically behaving) near-field soil domain; and (3) the structure. The far-field domain is semi-infinite unless the bedrock or a rock outcrop is very near and, thus, it can be represented with a reduced-order model in the form of impedance functions. The use of impedance functions in SSI analyses allows the computational cost to be reduced by several orders of magnitude without compromising the solution accuracy. Moreover, it is now possible to obtain time-domain representations of the inherently frequency-dependent impedance functions. As such, accurate nonlinear time-history analyses of problems that involve SSI effects can be now carried out in a computationally efficient manner. However, the current catalogue of impedance functions is limited to simple foundation shapes and soil profiles. In the present study, we provide a systematic approach with which impedance functions for arbitrarily shaped foundations resting on (or embedded in) heterogeneous soil domains can be obtained. In order to obtain the impedance functions, forward wave propagation analyses are carried out on a high-performance computing platform. The finite element method is employed to account for the arbitrary heterogeneity of soil and for different foundation types and geometries. In the forward analyses, the semi infinite remote boundaries are treated with Perfectly Matched Layers (PML) which, to date, are considered to offer the best Wave-Absorbing Boundary Condition (WABC) representation. Practical examples are provided that display pronounced variations in impedance functions with respect to frequency, which illustrate and quantify the importance of using frequency-dependent impedance functions in SSI analyses.

Planning for post-disaster reconstruction of damaged transportation networks is a complex and computationally expensive effort. Decision makers need to prioritize the use of limited resources to maximize societal benefit through accelerated traffic services restoration, which involves evaluating numerous recovery scenarios. This paper presents a new model that enables efficient analysis and optimization of the post-disaster reconstruction of transportation networks that is capable of identifying optimal reconstruction plans using reasonable computational overhead. The model employs a number of search space reduction techniques using mixed linear-integer programming and goal programming-inspired approaches in order to explore the large space of possible alternative solutions. Each of these solutions is comprised of a set of interdependent variables, including (1) contractors selection; and (2) the start dates of reconstruction projects and their constituent work packages. An application example is used to demonstrate the new model's superior computational performance compared to recent research developments.

Finding a vacant parking lot in urban areas is mostly time-consuming and not satisfying for potential visitors or customers. Efficient car-park routing systems could support drivers to find a nun occupied parking lot. Current systems detecting vacant parking lots are either very expensive due to the hardware requirement or do not provide a detailed occupancy map. In this paper, we propose a video-based system for low-cost parking space classification. A wide-angle lens camera is used in combination with a desktop computer. We evaluate image features and machine learning algorithms to determine the occupancy of parking lots. Each combination of feature set and classifier was trained and tested on our dataset containing approximately 10,000 samples. We assessed the performance of all combinations of feature extraction and classification methods. Our final system, incorporating temporal filtering, reached an accuracy of 99.8 %.

Transportation agencies across the world have been implementing different types of Pavement Management Systems (PMS) to facilitate pavement management and maintenance. Obtaining the necessary information in a manual pavement distress data collection process is costly and time-consuming. Research on the data collection of pavement conditions has focused mainly on evaluating the quality of the data collected, and there is limited research on ways to improve the efficiency of the data collection process (e.g., selection of optimized routes for collecting data). In this study, Geographical Information Systems (GIS) and Agent-Based Modeling (ABM), using swarm intelligence, are integrated to optimize decision-making processes for optimal routing selection in manual data collection process. The GIS platform provides the road spatial database containing the pavement sections that will be evaluated. Ant Colony Optimization (ACO) was selected as the algorithm to optimize the decision-making process of pavement evaluation crews (agents) during the pavement distress data collection process. Both the GIS and ABM models are developed using available data from the manual pavement distress data collection process in summer 2012 for the State of New Mexico. The results of this research will improve the understanding of route selection for pavement data collection.

A key element towards development of an asset management program is an efficient data collection on high-quantity, low-cost highway assets. Despite the importance, current practices of asset data collection are still manual and time-consuming. There is a need for a well-managed asset data collection that can provide usable asset inventories to Departments of Transportation (DOTs) for further analysis and condition assessment purposes. In this paper, we present a novel video-based recognition and 3D reconstruction algorithm. Our method takes an input of video streams and combines 2D recognition with 3D reconstruction algorithms. Using a new Support Vector Machine (SVM) classifier and based on the color channels at pixel level, a set of bounding boxes is initially extracted. Using a Haar-based shape recognition algorithm, the 2D candidates are further categorized based on their shape. These candidates are placed into a texture+color recognition algorithm. The benefits and limitations of the method in detection, classification, and localization of multiple types of assets are discussed in detail.

Network-wide assessment of transportation systems is a crucial task in traffic planning and management. Several simulation models (including macroscopic, mesoscopic, microscopic, as well as hybrid modeling) and theoretical models (e.g., macroscopic fundamental diagram) provide such assessments with different levels of detail. Although such models present good insight into dynamics of traffic flow (e.g., the relationship between speed and density) they do not discover the interrelationship between traffic flow dynamics and topology of the network. In this paper, the authors have attempted to macroscopically simulate congestion propagation on a regional road network based on the concept of epidemic spreading models (e.g., susceptible-infected-susceptible method). The results showed that at a critical threshold a transition occurs from a free flow phase to a congested phase. In addition, the simulation results verified that such a phase transition happens at the proposed mathematical threshold based on a topological characteristic (i.e., largest eigen value of adjacency matrix). However, the differences in the nature of congestion propagation and epidemic spreading suggest that further studies are needed to develop a hybrid method (i.e., macro-meso-micro) which mixes the proposed method with existing traffic simulation methods.

Parking is an essential component of urban transportation systems. The price and availability of parking could considerably influence travelers' choice of when to leave, where to park and which mode to choose. We propose dynamic pricing and sensing for parking in order to manage the traffic and reduce social costs. Cutting-edge sensing technology makes it possible to obtain novel parking information regarding real-time usage of parking facilities. The optimal prices are set with respect to the real-time parking occupancy. The effectiveness of pricing-sensing interaction is examined in several parking structures on the Stanford University campus.

A vehicle Predictive Cruise Control system has been developed to improve the fuel efficiency of vehicle and traffic flow performance based on the asymmetric traffic theory. The Predictive Cruise Control system consists of fourparts: (1) Deceleration-based Safety Surrogate Measure, (2) Adaptive Cruise Control, (3) Multi-Vehicle Measurement, and (4)Predictive Cruise Control. Adaptive Cruise Control basically decides the acceleration/deceleration action based on the estimated deceleration-based safety surrogate measure of the first leader vehicle. Then, Predictive Cruise Control adjusts the acceleration/deceleration action based on the multi-vehicle measurements, which represenst the future traffic condition of the subject vehicle. The developed system is tested by simulating the real vehicle trajectories from the NGSIM data and comparing the results with real following pattern. It was found that the newly proposed Predictive Cruise Control system can contribute to energy consumption and traffic flow performance because it can effectively suppress the shockwave from the downstream and remove the unnecessary deceleration and acceleration action.

As Building Information Modeling (BIM) becomes widely adopted by the construction industry, it holds undeveloped possibilities for supporting Facility Management (FM). Some FM information systems on the market claim to address the needs of FM managers. However, are the functionalities provided by the current BIM-based FM software companies those actually required by FM Professionals? What data is required by FM professionals in the operation and maintenance (OM) phases of facilities? The aim of this paper is to clarify the needs of FM professionals for maintenance purposes and to investigate potential areas that can use BIM technology to satisfy such data requirements. A survey was conducted to collect perspectives from industry practitioners for the data requirements for FM in the design and construction phases. The survey results indicated that maintainability considerations should be taken into consideration during the facility design phase. The results address the areas perceived by practitioners that need maintainability consideration in the design phase.

Knowledge discovery and formalization in a computer interpretable manner is a critical step toward effective construction automation. Ideally, any spatial conflicts in design should be identified and coordinated prior to construction execution. Building Information Modeling (BIM) has greatly improved the efficiency of spatial conflict identification and coordination compared to the traditional paper-based approach. However, the current model-based design coordination is still an iterative and experience-driven process, during which considerable knowledge is not properly captured. This research attempts to explore the feasibility of formalizing knowledge regarding spatial conflict analysis in design coordination by capturing necessary information using an object-oriented data capture approach in a model-based environment and analyzing the captured data with data mining techniques. It is envisioned that the formalized knowledge can then be used to narrow down the search space and assist future coordination tasks. Specifically, this paper proposes a process of information acquisition, data capture and knowledge discovery with BIM and supplemented tools. Results of the feasibility study are summarized and discussed. Opportunities and challenges are identified.

The architecture, engineering, construction and facility management (AECFM) industry has been experiencing many changes since inexpensive networked, mobile computing devices have become ubiquitous. With the rising amount of information and data generated in the life cycle of capital projects, information modeling and data interoperability have become a critical element in design, engineering, construction, and maintenance of capital facilities. Recent advances in Visualization, Information Modeling, and Simulation (VIMS) have the potential to address a number of these pressing challenges. The objective of this vision paper is to identify grand challenges related to Information Modeling. This is a companion paper to two others, which will address the areas of Visualization and Simulation. These papers are primers for the VIMS Grand Challenges Report, to be developed by an expert task force assembled by the American Society of Civil Engineers (ASCE) Technical Council on Computing and Information Technology (TCCIT) VIMS Committee.

Traditional education programs often present work aspects such as safety, quality and schedule as separate entities. Cognitive models of expert field managers are capable of integrating these aspects in terms of the information used in work activities. This paper presents a design of safety supervision instruction based on these models. Such instructional design makes use of specific information contained in the models that is not available through traditional instruction. To develop this approach, the knowledge content of the models is compared to standard safety documentation in order to identify the different facts and strategies that they posit for safety supervision practice. Then, these differential elements are used throughout the instructional design process to create programs and information tools for construction management education. In this way, it is possible to create an instructional environment in which to observe the impact of experts' knowledge on learners' cognitive strategies and use of information.

This research looked at potential use of building information modeling (BIM) in aging-in-place (AIP) projects. It hypothesized that BIM could lend a leverage to professionals from the design-for-aging (DFA) community to address the unique challenges of the explosive senior population in the United States. According to the U.S. Census Bureau statistics, more than 80 million citizens will be over age 65 by 2050, a majority of whom prefer to age in place as suggested by a recent national survey. The center piece issue in promoting aging in place is to create the physical and service environment catering to the needs in their senior life. To investigate the feasibility and potential benefits of using BIM in AIP projects, a proof of concept was developed through a 360-degree assessment. The major measures used by the assessment included: 1) a series of stakeholder perception surveys; and 2) a prototype model created with common BIM applications. The proof of concept identified typical challenges and concerns of present AIP practices, such as client buy-in, design conceptualization and communication, performance configuration and code compliance. A prototype model was created with the intention to demonstrate practical solutions that BIM could provide to mitigate these challenges and concerns in different use cases and scenarios. Results of the research attested that appropriate BIM integration could streamline the delivery of AIP projects and increase client satisfaction, which would eventually invigorate the AIP effort at large.

As designers and contractors become more advanced users, facility owners have also begun to make significant strides toward adopting Building Information Modeling (BIM) within their organizations. In fact, many larger owner organizations are now contractually requiring BIM on their construction projects. However, operationalizing the technology during the Operations and Maintenance (O&M) phase is a consistent hurdle that many owners have been unable to overcome thus far. The improvement of building owners' BIM competency is critical to reaping the full benefits of BIM over the building life cycle. Therefore, this research aims to identify what key characteristics building owner organizations must possess to become mature BIM users. An in-depth analysis of the existing BIM maturity assessment tools was conducted to make comparisons between the different variables measured. Suggestions are made for adding variables that have not yet been considered in the evaluation of building owner BIM competency and which may also be important.

Finding an accurate analytical model that is a good match for experimental data is not a straightforward task. Generally, analytical model parameters are tuned, using previously tested methods, to achieve an acceptable match for the experimental results. Model updating is a popular method that has been widely used since the early 1990s for this purpose. In this paper, research carried out for predicting the behaviour of masonry wall panels has been extended to find a suitable non-linear model for reinforced concrete (RC) beams that matches the experimental laboratory results. Data from laboratory experiments carried out by other researchers are used to confirm the validity of this analytical model. A Genetic Algorithm (GA) tool is used for the model updating process. The GA optimisation process finds a set of optimum parameters for use in the FE model. The outcome of this study was a close match with the experimental results, thus utilisation of this technique will dramatically reduce the number of expensive laboratory experimental tests which would otherwise be necessary.

Currently, there is no technology available to validate 4D Building Information Models using formal methods of temporal and spatial analysis. We propose to fill this technological gap by providing a query language which provides dedicated spatial and temporal operators. In the presented approach, a building is digitally represented by an instance of the neutral data format Industry Foundation Classes (IFC). With reference to the temporal and spatial data, the promoted IFC class structure is optimized for the particular query functionality. On the basis of this adapted object model, we make use of the Language-Integrated Query technology (LINQ) provided by Microsoft's .NET framework. As LINQ was originally developed for defining static queries which are evaluated at compile time, we have implemented a dynamic version called Live LINQ in order to allow the user to flexibly formulate queries and to process these during the runtime of the BIM application. The developed interface provides a powerful and easy-to use mechanism for the spatial-temporal analysis and verification of 4D building information models.

The World Wide Web has provided a vast distributed information space that is primarily designed for human consumption. The enmeshment of the Web and various business processes has made it imperative to define business information in a format that facilitates machine-to-machine exchange and automated processing. The construction industry is not an exception; construction business processes depend on a diverse set of information sources. For efficient processing of the construction business information, it is necessary to publish the information in a format that allows computer applications to easily discover, query and share the information. Currently, the standard for representing, accessing, and sharing a building model is the Industry Foundation Classes (IFCs) data model. Several researchers have investigated the limitations of IFCs data model for interoperability among heterogeneous software applications. This paper presents the results of an on-going research on an ontology-based approach to building information modeling to facilitate information exchange among different knowledge domain applications. The approach developed in this study is based on a shared building ontology that models building domain element types and element relationships. The information systems to be integrated should be modeled using the shared ontology; each knowledge domain adds its own element properties to the shared building ontology. SWRL rules are used for mapping element properties from one domain to another. An example demonstrates how the developed approach can facilitate project information sharing between design and estimating domains.

Cost estimating is a vital activity for contractors. Cost estimates require cost information collected from several sources. These sources include material and equipment suppliers. Current estimating software programs maintain a large database of resource cost data which must be updated before starting new estimates. The process of updating such databases is both time-consuming and error prone. This paper discusses the architecture of a new approach to construction cost estimating using the semantic web technology. The semantic web technology structures data according to formal ontologies intended to promote machine understanding. Semantic web services provide interfaces for publishing information. The proposed estimating approach requires that suppliers encapsulate their product information within appropriate interfaces and make this information available to contractors through web services. Estimating software applications can retrieve the latest cost data from suppliers' web services. The paper discusses how ontologies can be used to publish product information and develop ontology-based estimating applications.

Sharing comprehensive and professional knowledge about construction contractual claims through ontology could be a good way to improve contractual management. However, when it comes to the implementation of the ontology development, regarding the characteristics of claim knowledge, the practical and feasible conceptualization methodology is not yet well developed. This paper explores a synthesized methodology to fulfill the conceptualization work for the domain knowledge of construction claims. Based on a literature review on knowledge sharing in the construction claims area and ontology building methodologies, as well as the target ontology's definition and scope, the conceptualization activity in the ontology developing methodology of METHONTOLOGY, was selected as a raw conceptualizing methodology to use in the validation case study. The case study was designed to conceptualize the claim knowledge contained in the AIA A201General Conditions of Contract Document. The results of the case study indicated that METHONTOLOGY provides a valid framework and work flow for this domain.

As Building Information Modeling (BIM) becomes more popular in the construction industry, one of its most useful benefits to construction managers is the assistance it provides them in coordinating different building trades during preconstruction. Tools such as Autodesk's Navisworks for conflict detection have been instrumental to improving the speed, efficiency and accuracy of the coordination process. However, even with these tools, construction managers are faced with a daunting task of managing multiple versions of different trades' models and communicating changes downstream to those responsible for modeling. This paper proposes the development of a tool to evaluate the productivity of model updates made by different trades for different projects. The tool automatically generates a report for 3Dobject changes between different model versions. Through case study analysis, the tool was validated for the Mechanical Duct trade of a sample building project. Preliminary results indicate that it may aid construction managers in assessing the modeling productivity of different trades and making predictions regarding the estimated duration of the coordination phase of a project.

The effectiveness of emergency medical service (EMS) depends on the existing physical infrastructure and the allocation of medical resources. EMS is a challenging task due to the spatial distribution of the population and the geographical layout in the urban region. The management of emergency medical units (EMUs) and hospitals should be assessed to provide an efficient service. The objective of this research effort is to assess the current EMS of New Taipei City using geospatial analyses such as facilities' service area. In this study, spatial statistics for EMUs and hospitals are conducted. In addition, regions that cannot be reached within 10 minutes under pre-assumed conditions by EMUs are discovered, and interpolation of the travel interval is conducted and investigated. The geospatial arrangement of EMUs, and the upgrade or new constructions of hospitals are suggested to strengthen the efficiency of the emergency medical response. Future work is directed towards more advanced and complex spatial and temporal analyses to better assess the city's medical infrastructure.

Energy Efficient Building (EEB) design requires many simulation tools to support making decisions for optimized building solutions, resulting in frequent interactions between computational tools. Building Information Modeling (BIM) server platforms can support the storage, maintenance, and query of IFC-based building information models. However, the lack of a unified interface to support information exchange and interoperability among different building design and simulation tools has become a bottleneck of the EEB design process. This paper describes an integrated approach at the data level to combine BIMserver and OpenStudio to build a unified EEB data exchange model. Our first step is to build an information exchange bridge between BIMserver and OpenStudio which will enable different design and simulation tools that are connected to either of them to interoperate and exchange needed data. In the paper, we also discuss the challenges of the seamless integration due to the dependency on both BIMserver and OpenStudio. The integrated approach, which organizes the data flow in a unified model, enabling effective exchange of data, is currently in a beta-testing phase.

Various Building Information Modelling (BIM) frameworks and workflows have been developed over the last few years. A BIM framework is a theoretical structure explaining or simplifying complex aspects of the BIM domain by identifying meaningful concepts and their interrelationship. BIM workflows are structured information (e.g. process maps) intended for operational applications of BIM concepts and tools. The majority of BIM frameworks and workflows developed in the past years were either intended to build broad understanding and adoption at industry level or specific for BIM usage in large enterprises. This study will illustrate the development and application of a practice-oriented BIM framework that can be used at project level.

Construction engineers increasingly use 3D laser scanners on job sites to capture detailed as-built geometries for construction monitoring, while their data collection time and quality can vary significantly. Various data collection parameters influence the quality of data and time for data collection. These parameters include internal (e.g., the laser signal strength, spatial resolution of the used scanner) and external parameters (e.g., color, object dimension, distance, and lighting condition). Correlations among these parameters, data collection time, and as-built modeling errors, contain principles of efficient and effective laser scan planning. Limited quantitative studies of these correlations result in ad-hoc laser scan planning, missing geometric information, and unnecessarily long data collection time. This paper presents a series of controlled experiments and seven statistical methods for quantitatively modeling the correlations between 3D as-built modeling errors and various parameters mentioned above. The major findings include: 1) internal correlations among data collection parameters need to be resolved for precise as-built error prediction; 2) all tested statistical methods are based on linear regression, while non-Gaussian distribution of the as-built modeling errors and nonlinear correlations in the experimental results distort the parametric error models. Future research will explore nonlinear or non-parametric methods for obtaining more reliable error models for proactively guiding engineers to conduct laser scanning in the field.

Highway construction involves repetitive, sequential operations that make them vulnerable to time and cost overruns. In order to increase their chance of being completed on time and within budget, these projects need efficient resource scheduling methods. This study presents a new scheduling system for highway construction projects. Location data generated from GPS technology can be used to track movement patterns of resources as well as progress of the project. Spatio-temporal constraints are used to generate dynamic project schedules and to visualize actual and potential project schedules and progress. By comparing observed versus planned progress, the system can anticipate delays. This information, in turn, can allow managers to implement adjustments in a flexible way to achieve better efficiency than post-hoc adjustments possible with traditional approaches.

Building emergencies are big threats to the safety of building occupants and first responders. When emergencies occur, unfamiliar environments are difficult and dangerous for first responders to search and rescue, sometimes leading to secondary casualties. One way to reduce such hazards is to provide first responders with timely access to accurate location information. Despite its importance, access to the location information at emergency scenes is far from being automated and efficient. This paper identifies a set of requirements for indoor localization during emergency response operations through a nationwide survey, and proposes an environment-aware, sequence-based localization algorithm that is free of signal path loss models or collection of prior data, and mitigates signal multipath effects. The algorithm enables efficient on-scene ad-hoc sensor network deployment and optimizes sensing space division by strategically selecting sensor node locations. Building information is integrated in order to enable building-specific space divisions and to support context-based visualization of localization results. Proposed algorithm is evaluated through a building-size simulation. Room-level accuracy of up to 87.3% was reported, and up to 15.0% of deployment effort was reduced compared with using randomly selected sensor locations. The algorithm also showed good computational speed, with negligible time required for refreshing location estimation results in simulation.

Machinery- and vehicle-related fatalities represented 32% of construction fatalities in 2010, and backover fatalities were recorded as one of the main culprits due to limited visibility. In order to minimize backover accidents, sensor-based proximity warning technologies - such as video camera, radar, ultrasonic, and tag-based systems - have been developed, tested and in use in many new passenger vehicles. This paper addresses potential implementation of sensor-based proximity warning systems to minimize backing accidents by reviewing sensing technologies applicable for construction equipment and presenting a formalized framework of sensor-based proximity warning systems evaluation. The presented framework includes a generic sensor system selection approach and performance testbed protocol. The proposed framework is expected to help decision makers objectively evaluate sensor-based proximity warning systems in their selection to implementation in the construction work zones.

Monitoring operational efficiency of construction equipment, which indicates how efficiently construction equipment is utilized, provides key information in reducing air pollutant emissions from equipment use as well as improving the productivity of construction operations. In this paper, we report our efforts to measure the operational efficiency of construction equipment, using low-cost accelerometers. The measurement of the operational efficiency of construction equipment is formulated as a problem that classifies second-by-second equipment activity into working, idling, and engine-off modes. We extract various features from the raw accelerometer data and classify them into three different equipment activities (working, idling, and engine-off), using supervised learning algorithms such as Logical Regression, decision trees, k-Nearest Neighbor, and Naïve Bayes. The result from the real-world experiment indicates that the use of supervised learning algorithms provides over 93% of recognition accuracies, and this level of accuracies causes less than 2% error in the measurement of equipment operating efficiency.

Construction is one of the industries with the highest fatality, non-fatal injuries and illness rates across the United States (CPWR, 2008). Although construction professionals have made great efforts to improve safety training, site supervision and design for safety (among others), the safety performance in construction is still not satisfactory due to human errors and lack of situational awareness during construction activities. Recent advances in sensing and computing technologies offer a solution for improving safety performance by providing rich information about location and, hence, worker safety. Our research envisions an autonomous jobsite safety monitoring system which utilizes distributed data and information collected from various sources (RFID, localization sensors, accelerometers, load cell sensors and building information models). The research is divided into three phases:(1) knowledge elicitation; (2) development and deployment of the autonomous safety monitoring system in a distributed computing environment; and (3) exploring the impacts of data imperfections situations (e.g., erroneous or missing data) on the safety monitoring system and approaches to reduce the impacts of the imperfections on the safety system. Prior research has reported on the first two phases (Julien, et al, 2005; Luo, et al, 2011; O'Brien, et al, 2008). This paper reports the preliminary results of evaluating the impacts of data imperfections on autonomous safety monitoring using data from location-aware sensors, which provides the most important information for the autonomous jobsite safety monitoring system. The rest of this paper is organized as follows: The Related Work section summarizes the location-aware jobsite safety monitoring applications in construction and the imperfect data collected from sensors. The Research Approach section describes the research approach, including the testbed used for data collection, the test case used to evaluate the impacts of imperfect data on autonomous safety monitoring and the process of how the data is used to evaluate the impacts. The Preliminary Results section reports the preliminary results of the impacts on autonomous safety monitoring. Finally, the Conclusion section summarizes the results and proposes future research suggestions.

Existing methods for automatically determining the absolute scale of Point Cloud Data (PCD) acquired from monocular video/photogrammetry are suitable to a limited set of applications under specific settings and are not general enough to be considered as practical solutions for reconstructing both indoor and outdoor built infrastructure scenes. To address the issue in this paper, we propose a novel method for automatically calculating the absolute scale of built infrastructure PCD. The absolute scale estimation method uses a pre-measured cube for outdoor scenes and a letter-size sheet of paper for indoor environments as the calibration patterns. Assuming that the dimensions of these objects are known, the proposed method extracts the objects' corner points in 2D video frames using a novel algorithm. The extracted corner points are then matched between the consecutive frames. Finally, the corresponding corner points are reconstructed along with other features of the scenes to determine the real world scale. Three indoor and three outdoor cases were selected to evaluate the performance of the proposed method and the absolute-scale PCD for each case was computed. Obtained results illustrate the capacity of the proposed method to accurately compute the absolute scale of PCD.

This paper presents a novel algorithm for detecting, fitting and classifying the embedded surface primitives from a point cloud dataset (PCD). Given a noisy infrastructure PCD, the final output of the algorithm consists of segmented surfaces, their estimated quadric models and corresponding surface classification. Initially, the PCD is down-sampled with a k-d tree structure then segmented via subspace learning. After pose recovery for each segmented group via singular value decomposition, a full quadric model is fit in MLESAC using the direct linear transform for parameter estimation. From the model parameters, the surface is classified from the rank, determinant, and eigenvalues of the parameter matrices. Finally, model merging is performed to simplify the results. A real-world PCD of a bridge is used to test the algorithm. The experimental validation of the algorithm demonstrates that the surface primitives are accurately estimated and classified.

The main objective of the presented research is to formalize a novel iterative feedback mechanism that facilitates reliable and accurate geometric capture of civil infrastructure systems (e.g., bridges) through laser scanning to support predictive modeling (e.g., finite element modeling). Current condition assessment (of a bridge) based on visual inspection is subjective and often fails to obtain reliable and accurate geometry data which is essential for finite element modeling. The laser scanner, which is a non-contact geometry capture technology, is one of the promising technologies that can capture more detailed (2mm spacing) and accurate (+/- 5mm) geometric data compared to existing surveying and measurement techniques, such as total stations and Global Positioning Systems. The presented work is focused on investigating and formalizing how laser scanning technology can optimally be leveraged and integrated to provide a sufficiently accurate documentation of bridge geometry, and then how such information may be transformed into a reliable finite element model for predictive modeling.

3D as-built models of building indoor environments could be used to facilitate multiple building assessment and management tasks, such as post-disaster safety evaluation, renovation/retrofit planning, and maintenance scheduling. However, modeling building indoor environments is a challenging task. It is even more difficult than modeling building facades due to the issues, such as limited lighting conditions and prevalence of texture-poor walls, floors, and ceilings. This paper investigates the potentials of RGB-D cameras in modeling building indoor environments. Three pilot studies have been performed to evaluate 1) the accuracy of the sensing data provided by an RGB-D camera and 2) the automation that can be achieved for the registration of building indoor scenes and the recognition of building elements with the sensing data. The studies show that the camera can provide a stream of mid-accurate sensing data in real time. Also, a high degree of automation can be achieved through the fusion of spatial and visual data from the camera, when modeling the as-built conditions in building indoor environments.

The construction industry is known to be dangerous and is featured by fatal occupational hazards. Job Hazard Analysis (JHA) is a common approach to mitigate and control these occupational hazards. It analyzes major tasks in a construction activity, identifies all the potential task related hazards, and suggests means to reduce or avoid each potential hazard. Because every project is unique, an effective JHA must consider the conditions specific to the project instead of reusing previous JHAs directly. To this end, JHA requires the participation of experienced construction practitioners and becomes a time-consuming and brain-draining task. While expert involvement is necessary during JHA development, the authors would like to explore the possibility of leveraging existing construction safety resources in order to reduce the required human efforts. This paper presents an approach based on text classification to support the automation of JHA. It uses the CPWR construction solutions database as an example to demonstrate how text classification can be applied to match the database documents with predefined safety violation scenarios and identify potentially useful safety approaches. This paper also discusses how different strategies were tested to optimize the effectiveness of the proposed text classification approach. The results indicate that although some of the classification strategies cannot make obvious progress on the effectiveness, the effectiveness of the text classification without optimization is good enough to support JHA.

Quality assessment of precast concrete panels is an important factor that affects overall quality of construction. As prefabrication becomes popular at construction sites, demands for automatic and accurate inspections of the dimensions of the precast concrete panels have increased. Current techniques for measuring the dimensions of precast concrete panels, however, heavily rely on qualified inspectors and are time- and labor-demanding. To overcome these limitations, a dimensional measurement technique for precast concrete panels is proposed using a 3D laser scanner. For autonomous implementation of the dimensional measurement, a new feature extraction algorithm of extracting only boundary points of the precast concrete panels is developed. To increase the measurement accuracy, a compensation model is employed to account for the dimension losses caused by an intrinsic limitation of laser scanners. This study focuses on measurements of length, width and squareness of precast concrete panels in a non-contact and speedy manner. Experimental validations on actual precast slabs are conducted to verify the applicability of the proposed dimensional measurement technique for precast concrete panels.

Knowledge of workforce productivity and activity is crucial for determining whether a construction project can be accomplished on time and within budget. As a result, significant work has been done on improving and assessing productivity and activity at task, project, and industry levels. Task level productivity and activity analysis are used extensively within the construction industry for various purposes, including cost estimating, claim evaluation, and day-to-day project management. Nevertheless, assessment of task level productivity and activity analyses are mostly performed through visual observations and "after the fact" even though studies have been performed to automatically translate the construction operations data into productivity information and to provide spatial information of construction resources for specific construction operations. This paper presents an original approach to automatically assess construction labor activity. Using data fusion of spatiotemporal and workers' thoracic posture data, the authors have developed a novel framework for identifying and understanding the worker's activity type over time automatically. This information is used to perform automatic work sampling that is expected to facilitate real-time productivity assessment.

This paper presents a new context-aware mobile augmented reality system that provides rapid and robust on-site access to construction project information such as drawings, specifications, schedules, and budgets. The mobile augmented reality system does not need any RF-based location tracking (e.g., GPS or WLAN) or optical fiducial markers for tracking a user's position. Rather, the user's location and orientation are automatically and purely derived by comparing photographs from the user's phone to a 3D point cloud model created from a set of site photographs. After generating a 3D point cloud model of construction site, field personnel can use mobile devices to take pictures of building elements and be presented on-site with a detailed list of project information related to the visible construction elements in an augmented reality format. The experimental results show that (1) the underlying 3D reconstruction module of the system generates more complete 3D point cloud models, and faster than other state-of-the-art Structure-from-Motion(SfM) algorithms; (2) the localization method is an order of magnitude more accurate than the state-of-the-art solutions, and can provide acceptable tolerances of most on-site engineering applications. Using an actual construction case study, the perceived benefits and limitations of the proposed method for on-site context-aware applications are discussed in detail.

Of the construction fatalities that occurred in 2010, approximately one-fourth resulted from workers being struck by an object or piece of equipment on a construction site. The objective of this particular research is to evaluate the capabilities and reliability of low-cost (semi-) passive radio frequency identification (RFID) technology as the main component of a pro-active real-time personal protection unit (PPU). Experiments emulating typical interactions between ground workers and heavy construction equipment are designed and executed. Various positions and orientations of the designed (semi-) passive RFID-based PPU were evaluated based on typical ground worker movements during construction tasks. Results indicate that both position and orientation of the PPU impact the reliability of the system's ability to activate alerts during hazardous proximity situations. The overall purpose of this research is to generate scientific data and knowledge of proximity detection and alert system for eventual implementation of these systems on construction sites. By implementing these systems in the construction environment, ground workers can be provided an alert during hazardous proximity situations.

Navigation through unfamiliar facilities, with labyrinths of corridors and rooms, is difficult and often results in a person being lost. The proposed research is to develop a navigation algorithm to help a person navigate through facilities. The scope is the use of passive radio frequency identification (RFID) tracking technology and Building Information Modeling (BIM). A novel theoretical approach is presented that utilizes current localization techniques, performance characteristics of passive RFID, and a BIM model. A prototype application has been developed that connects the RFID readers with a BIM model. The goals are to locate and navigate any user and/or utility in an unfamiliar facility. Preliminary results demonstrate the feasibility of locating a user inside buildings in real-time. Significantly, the algorithm has the potential to enable anybody to navigate the shortest and quickest route, saving time, money and, in an event of an emergency, lives.

Cumulative trauma disorders (CTDs) are a leading cause of disability which generates huge direct and indirect costs in the construction industry. Existing efforts identify the risk of CTDs through survey or observatory studies which are subjective and expensive. This paper attempts to automatically identify the risk of CTDs on construction sites with the aid of a video surveillance system. Specific emphasis is placed on low back disorders (LBDs), which is one of the major work-related CTDs. To this end, a risk identification method is proposed based on location changes of region of interests in the resulting video streams at minimum supervision. It is used to detect repetitive trunk flexion-extension motions, awkward trunk postures, and to assess the risk of job-related LBDs. Preliminary experimental analysis on lab datasets demonstrates the promise of the proposed method.

Regression techniques are commonly used for addressing complicated prediction and classification problems in civil engineering thanks to its simplicity. For a given dataset, the linear regression from the input space to the output variables can be achieved by using the "least square error" approach, which minimizes the difference between the predicted and actual outputs. The "least mean square" rule can also be used as a generic approach to deriving solutions onlinear or non-linear regressions. The paper addresses the fundamental algorithms of "least square error" and "least mean square" in order to facilitate the prediction and classification of cycle times of construction operations. The classic XOR problem is selected to verify and validate their performances. A viaduct bridge was installed by launching precast girders with a mobile gantry sitting on two piers. The effectiveness of regression techniques in classifying and forecasting the cycle time of installing one span of viaduct considering the most relevant input factors in connection with operations, logistics and resources are demonstrated.

On October 29, Hurricane Sandy wreaked havoc on much of East Coast. The crumbling infrastructure along the East Coast suffered major damage during this historical storm, leaving tens of millions of residents without electricity. Rapidly acquired high-quality and high-resolution spatial and photo data on damaged structures are of great value to scientific studies and post disaster recovery-related decision makings. This paper describes a mobile LiDAR study conducted in the sandy impacted areas. The application and utility of mobile LiDAR data for disaster response and recovery is presented and discussed. The preliminary results of this research demonstrated that mobile LiDAR data fill in the gap between airborne remote sensing data and typical ground survey data.

It is well-recognized that the corrosion of metallic structures has a significant impact (i.e., direct cost of approximately $276 billion per year) on the U.S. economy, including infrastructure, transportation, utilities, production and manufacturing. There is an urgent need to develop more reliable ways to detect corrosion. Image processing techniques have been used extensively to detect corrosion in structures. However, it is essential to evaluate the effect of different parameters on the performance of vision-based corrosion detection systems. This study evaluates the effect of several parameters (including color space, color channels, and sub-image block size) on the performance of color wavelet-based texture analysis algorithms for detecting corrosion.

About 33% of non-fatal occupational injuries and illness are due to work-related musculoskeletal disorders (WMSDs), which had the highest percentage of injuries or illnesses in construction in 2009. Though techniques previously used to prevent WMSDs (e.g., ergonomic rules, checklists based on surveys, and laboratory experiments) provide valuable insights into the prevention of WMSDs, these techniques may not be suitable for measuring the physical demands required for manual ongoing works under real conditions. In an effort to address this issue, we propose a motion capture approach to obtain a worker's posture information for measuring physical loads on body parts (e.g., shoulder, back). The human postures extracted by motion capture are expressed by rotation angles between body joints, and these angles are then converted to joint angles which are the inputs for biomechanical analysis. The resulting information contains the internal forces of body parts (e.g., hands and feet) and is capable of identifying allowable strength ranges, thereby helping determine overexertion and awkward postures during a task. As a test case, motion data for ladder-climbing is obtained using a motion capture system (e.g., Microsoft Kinect) in a laboratory, and the postures are biomechanically analyzed frame by frame. The results show that the proposed method performs well at computing the physical loads, which promises its great potential to understand the causes of WMSDs during ongoing manual construction work. Further, it can automatically predict potentially hazardous postures on certain body parts, which has otherwise only been achieved by tedious and time-consuming manual investigation.

This paper presents a new approach for automated compliance checking in the construction domain. The approach utilizes semantic modeling, semantic Natural Language Processing (NLP) techniques (including text classification and information extraction), and logic reasoning to facilitate automated textual regulatory document analysis and processing for extracting requirements from these documents and formalizing these requirements in a computer-processable format. The approach involves developing a set of algorithms and combining them into one computational platform: (1) semantic machine-learning-based algorithms for text classification (TC); (2) hybrid syntactic-semantic rule-based algorithms for information extraction (IE); (3) semantic rule-based algorithms for information transformation (ITr); and (4) logic-based algorithms for compliance reasoning (CR). This paper focuses on presenting our algorithms for ITr. A semantic, logic-based representation for construction regulatory requirements is described. Semantic mapping rules and conflict resolution rules for transforming the extracted information into the representation are discussed. Our combined TC, IE and ITr algorithms were tested in extracting and formalizing quantitative requirements in the 2006 International Building Code, achieving 96% and 92% precision and recall, respectively.

The accuracy of the results in stereo image-based 3D reconstruction is very sensitive to the intrinsic and extrinsic camera parameters determined during camera calibration. The existing camera calibration algorithms induce a significant amount of error due to poor estimation accuracies in camera parameters when they are used for long-range scenarios such as mapping civil infrastructure. This leads to unusable results, and may result in the failure of the whole reconstruction process. This paper proposes a novel way to address this problem. Instead of incremental improvements to the accuracy typically induced by new calibration algorithms, the authors hypothesize that a set of multiple calibrations created by videotaping a moving calibration pattern along a specific path can increase overall calibration accuracy. This is achieved by using conventional camera calibration algorithms to perform separate estimations for some predefined distance values. The result, which is a set of camera parameters for different distances, is then uniquely input in the Structure from Motion process to improve the Euclidean accuracy of the reconstruction. The proposed method has been tested on infrastructure scenes and the experimental analyses indicate the improved performance.

When a building is under the threat of a disaster/an emergency, it is important to rapidly retrieve up-to-date indoor navigation conditions by identifying the hallways that are blocked. This paper investigates the contribution of data from each sensor that is deployed inside a building in accurately determining the current blockage conditions at hallways. Sensor performances were assessed, both individually and by fusing multisensor data at the building element-level. The final goal is to create and visualize evacuation and navigation paths for the first responders and the occupants during a disaster/an emergency. A prototype was developed to integrate Building Information Model (BIM) with hallway blockage information retrieved from multiple sensors and to provide guidance in a building based on the building's current status using graph theory principles. This paper specifically discusses if less sensors could be used to get similar accuracy results in detecting hallway blockages based on the individual and fused data from multiple sensors.

This paper presents a linear cellular automaton (LCA) method to predict the dynamic instability (DI) mode of large-span, single-layer shell subjected to ground motions. The LCA model of the shell obtains the state values of cells/nodes through its finite element analysis (FEA). Meanwhile, the concepts of nodal domain and nodal domain similarity are proposed. Then, similar nodal domains between two shells are matched through the proposed criterion. Finally, the DI mode of object shell is mapped using the criterion for projecting the formative values of base shell to similar nodal domains in object shell. Case studies show that the LCA method could be used to predict the DI mode of object shell. Consequently, this study would explore an application of LCA technique and FEA data in shells. Also, the LCA method costs much less time than FEA to calculate the DI mode of shell.

This paper presents Magnetic Charged System Search Algorithm (MCSS) of a new Meta-Heuristic Algorithm (MHA) to solve complex, Time-Cost-Optimization (TCO) problems in construction project planning. Project planners face complicated multivariate TCO problems that require time-cost tradeoff analysis to make models closer to the actual projects. Our approach is applied to solve one optimization problem, which is found in the construction project planning literature. It is shown that our proposed approach is superior than existing optimization algorithms in finding better project schedule solutions with less total project cost and shorter total project duration. The results also show that comparing to other existing methods, the presented approach provides more accurate and complete Pareto front for TCO problems in construction project planning.

Construction projects are subject to unexpected factors (such as weather, soil conditions etc.) which cause frequent changes in baseline schedules. Yet, existing precedence diagrams (PERT, CPM) cannot provide reliable results to handle the complexity of an ideal resource allocation (Resource Constrained Project Scheduling Problem) - especially when considering actual state data. Initial assumptions become obsolete and disturbances in process durations or sequence cause iterative revisions of the schedule. Therefore, a method is introduced to optimize construction schedules continuously based on the current progress considering all relevant resource capacities. Actual state data is progressed and variations in level of detail between construction works and the virtual model are leveled. A Multi-Agent Framework generates valid schedules based on heuristic priority rules to minimize project makespan and additionally determines floats and total resource costs. Thus, various scenarios can be analyzed to meet the specific requirements of the project and to assess the consequences of schedule changes more effectively.

Recent research efforts to improve construction progress tracking has focused on employing emerging technologies such as three dimensional (3D) imaging, including digital photogrammetry and 3D Terrestrial Laser Scanning (TLS). Previous research has shown that "Scan-vs-BIM" object recognition systems, which fuse 3D TLS and 4D project BIM, provide valuable information for tracking structural works. However, until now, these systems have focused on tracking progress for permanent structures only; none of them has considered progress of secondary or temporary structures. In the context of structural concrete work, temporary structures include formwork, scaffolding and shoring, while secondary components include rebar. The value of tracking temporary and secondary elements is that it would add veracity and detail to the progress tracking process and, consequently, to billing. This paper presents two different techniques for detecting concrete construction secondary and temporary objects in TLS point clouds, one of which is based on a Scan-vs-BIM object recognition system. Both techniques are tested using real-life data collected from a reinforced concrete building construction site. The preliminary experimental results show that it is feasible to detect and track secondary and temporary objects in 3D TLS point clouds with high accuracy. This will help to improve progress estimation and tracking.

Correctly generating and updating a schedule are critical to the success of a construction project. However, current schedule update techniques are inaccurate and inefficient because they still rely heavily on manual effort. Although some research proposed an automated schedule update system, it has certain limitations because it uses limited information. This paper describes the development and implementation of a system that automates schedule update using valuable information in the 4D building information model (BIM). The performance of the system was evaluated using a 3D point cloud, obtained during the construction of a project. The developed system improves the schedule update process by integrating 4D BIM with the 3D point cloud. In comparison to current systems, this paper demonstrates a new thinking paradigm in the schedule update process, by using 4D BIM.

There has been a growing demand for the three-dimensional (3D) reconstruction of as-built pipeline. The as-built 3D pipeline reconstruction process consists of measurement of the plant facility, identification of the pipelines, and generation of the 3D pipeline model. Although measurement is now efficiently performed using laser-scanning technology and there has been much progress in 3D pipeline model generation, identification of the pipelines from large and complex sets of laser-scanned data remains a challenging problem. The aim of this study is to propose an as-built 3D pipeline segmentation approach to automatically identify as-built pipelines. The steps of the proposed approach are segmentation of the 3D point cloud, feature extraction based on curvature computation, and pipeline classification. The experiment was performed at an operating plant in order to validate the proposed approach. The experimental result revealed that the proposed method can indeed contribute to the automation of as-built 3D pipeline reconstruction.

Today's Architecture, Engineering, Construction and Facility Management (AEC/FM) industry has to deal with complex obstacles (e.g., aging infrastructure, the protection of the natural environment, the need for resilient infrastructure). Among several technical fields that have been adopted and advanced, computer simulation has been widely researched and practiced for the effective delivery and maintenance of capital projects. Considering the unprecedented problems of today's infrastructure and the rapid advancements in simulation research, it is very timely to investigate what grand challenges exist in simulation that, if properly addressed, can help create a more sustainable and resilient engineering of infrastructure lifecycle. To this end, this paper aims to identify grand challenges in simulation by presenting major areas of interest that can benefit the AEC/FM domain and discussing three specific areas identified as challenges in simulation: 1) realistic simulation modeling; 2) applicability of simulation models to the industry; and 3) academic and educational obstacles. Specific foci are the knowledge gaps in these major areas, and the current efforts to mitigate these gaps based on extensive literature reviews. This paper also proposes the next steps for addressing these challenges based on the findings herein.

The emerging four-dimensional modeling techniques have been proven to be useful and promising in construction planning. However, the current applications in 4D modeling still lack an efficient modeling approach, an analytical capability, and a dynamic capability. This paper proposed a 4D-CPM-based graphical scheduling system aiming to address the problems. The system synergizes the creating of a conflict-free 4D model into the process of schedule development, generates and adjusts the workspace semi-automatically according to different construction methods and user options, and identifies space time conflict efficiently by analyzing the CPM network topology. It complements the traditional CPM scheduling and 3D modeling method, and could serve as an effective explanative communication tool in construction project management. The proposed system could be enhanced in three aspects: formalize a 4D topological framework, develop a 4D construction database, and enhance the collaborative and interactive capability to cater for large scale projects.

This paper presents a unified semiotic theory of computer based communication for the built environment. The developed theory understands (computer) sign-based communication as a triadic process involving real world objects, the signs computers use to represent them, and interpretants that represent the understanding that users were able to produce about the built environment by interpreting these signs. The triadic character of the semiotic theory allows us to understand the cascading process of developing and using BI systems during which actors create computer-based signs to represent objects in the built environment (semantic representation), how others interpret signs, and how, based on these interpretations, these others can then encode the interpreted meaning using new signs. Next to developing the above outlined semiotic meaning decoding, interpretation, and encoding process framework, the paper also shows by example that this semiotic framework allows understanding the process of how signs allow for the communication of meaning about the built environment during the development and use of BI systems.

This research is built upon a previously established, early stage, multidisciplinary design optimization (MDO) framework, entitled Evolutionary Energy Performance Feedback for Design (EEPFD), and proceeds with observing the impact of EEPFD on the early stages of design by conducting a pedagogical benchmark experiment. This experiment has two main observational interests. The first objective is to observe discrepancies between the human versus automated decision-making processes and the resulting performance of the solution space from each process. The second objective is to understand students' ability to translate their design intent into a parametric model, as this is a crucial component in the implementation of EEPFD. By comparing EEPFD and the benchmark pedagogical process, this paper provides an initial assessment of the potential of EEPFD to reduce latency in decision-making and to find superior performing design solutions compared to the benchmark process. At the completion of this experiment, it was observed that EEPFD was able to deliver superior performing design solution spaces, but that students encountered difficulties in the translation of their design intent into a functioning parametric model.

The seismic response of statues is motivated by observations from recent earthquakes combined with their high cultural significance. Typically unrestrained with a high coefficient of static friction at its base, a statue can be analyzed as a rigid body which will undergo rocking during seismic excitation. In this paper, a methodology is presented to obtain the critical geometric and mass properties required for numerical rigid body rocking analyses. Two nonintrusive approaches to capture the statue geometry are presented: light detection and ranging (LiDAR) and structure-from-motion (SfM), which generate a three-dimensional point cloud. A Poisson reconstruction algorithm intrinsically filters each point cloud and creates a fully enclosed, triangulated, surface mesh. Integrating over the surface mesh allows for computation of the required geometric and mass properties for numerical rocking analysis to simulate the seismic response of a statue.

The prevalent method of measuring progress when constructing brick facades is through manual site surveys. These surveys are tedious and time-consuming. They are also approximate, as counting the number of bricks in place to compare against those ordered is a very laborious task compared to its end value. This paper presents a novel and automatic method for counting bricks in place to automate the brick site survey during façade construction. This method works by using images or video frames taken at a construction site. Color thresholds are selected based on the color of the bricks and are used on the video frames to distinguish bricks in the image. The edge map of the frames is generated using a Laplacian of Gaussian filter. The edges are compared with the known brick properties, including their rectangular shape, their expected arrangement and their size. Test results demonstrate that this method is capable of counting the number of bricks on a brick façade with acceptable error.

Current practice of post-earthquake damage assessment is based on visual inspections by experts. Research has shown that visual inspection and reports produced as a result of inspections can be subjective, incomplete, and vary in terms of the level of detail and thoroughness. Critical data that provides information about damage can be captured using laser scanners and processed for a more objective and information-driven damage assessment practice. In addition to damage information, the damage severity identification process requires reasoning about the structural behavior and topology. Building Information Modeling can be used as the underlying information source to streamline the damage assessment process. This research aims at representing crack information using a Building Information Modeling approach. In order to support reasoning for damage assessment, three activities were performed: (1) requirements were identified based on accepted damage assessment codes and standards; (2) capabilities of Industry Foundation Classes were explored for representing crack information; and (3) suggestions were provided for using existing concepts in IFCs for representing cracks.

Computational techniques for structural analysis have been systematically studied and developed over the years, while modeling interfaces for these environments have remained largely unchanged. Typically, a so-called WIMP (Windows, Icon, Menu, Pointer) or a script-based interface is used. Even though easy to implement, these interfaces are frequently counterintuitive for the task at hand, abstracting natural hands-on modeling tasks into point, click and drag events. As a result, even experienced engineers have to spend large amounts of time to translate their models into the form that a computer can recognize. With the emergence of new input devices such as digital pens and multi-touch enabled displays, a new generation of sketch-based input technique has become possible. This paper presents a modeling and simulation environment for structural analysis using sketch-based input. Two case studies (including the static analysis of a 2D elastic frame and transient analysis of a 2D nonlinear frame) are presented, showing that model creation, simulation and analysis can seamlessly co-exist, supporting the creation of an integrative analysis methodology.

One of the most challenging aspects of safety for construction sites is ensuring that workers can predict, identify, and respond to potential hazardous conditions before they are exposed. From a scientific standpoint, we currently lack the knowledge of discovering the most efficient training styles for safety and also understanding why and how these styles of training can influence the post-training activities. To address these needs, a System for Augmented Virtuality Environment Safety (SAVES) is designed and presented in this paper. SAVES, which integrates a BIM with 2D images on a jobsite, allows trainees to conduct a set of interactions with SAVES and accomplish multiple instruction-based and task-based training modules. These modules include detection of ten types of hazard and/or energy sources at three levels of severity. The complete process of design, development and implementation of SAVES is demonstrated and the lessons learned are discussed in detail.

This study aims to identify challenges in the AEC/FM industry that can potentially be addressed using research in data and information visualization. These challenges were recognized by: reviewing visualization techniques for addressing current challenges associated with decision-making tasks; studying the fit between visualization techniques and the decision-making tasks; identifying the gaps in knowledge of visualization techniques; and, finally, establishing a framework for measuring the domain requirements and the technology capabilities as a road map for domain-needs-driven development in the area of data and information visualization. The challenges associated with the current practice of project delivery and the limitations of the state-of-the-art visualization techniques in addressing these challenges are discussed. This paper presents where and how intuitive and effective visualization can address these challenges. It also suggests areas where researchers can apply visualization research to further improve existing processes.

A true challenge in the digital age is the fusion of qualitative and quantitative information for meaningful visualizations and interpretations. Nowhere is this more obvious than in the inspection of bridge structures, where multi-piece data is collected for repetitive structural elements with the purpose of differentiating data content for condition assessment. Inspection by experienced personnel remains a critical and necessary time-consuming element. A true 4D BIM approach remains a challenge in the field due to hardware requirements and user skill demands. A new 2D+1 approach has been implemented with a software system on lightweight digital tablets designed to annotate plan documents and photos. The software captures quantitative and qualitative observations (e.g. photos and written observations) referenced spatially to 2D drawing/photos of the structure with near real-time multi-user synchronization. A cloud-based system provides a near real-time replica of the document to most accessible web browsers. Quantitative, as well as qualitative data can be analyzed programmatically together using spreadsheet software or developed into more advanced visualizations through database querying techniques. The software already has been used to improve the efficiency and quality of data collection on numerous bridge inspections across the U.S.

This paper presents a target-free automatic point cloud registration method which is based on identified common features between multiple images in point clouds. Target-free automatic registration of point clouds is an important research area in which several problems are still not addressed completely, such as registration accuracy, time, and size of overlapping area between point clouds. In this study, a series of point cloud data sets from different scan positions are registered based on the texture data in the overlapping area between point cloud sets. Both texture and point cloud data are obtained by a developed Laser Detection and Ranging (LADAR) system simultaneously. The automatic registration algorithm begins by identifying 2D common features of the texture, then constructing corresponding 3D common points, and afterwards finding out the optimal transformation matrix. The introduced method will substantially reduce the amount of time for point cloud registration by using a few images within a small, overlapping area between point clouds without the need of target references, thus significantly promoting as-built modeling process for existing infrastructure or construction progresses.

Thanks to its simplicity and cost-effectiveness, discrete event simulation is preferred for construction simulation, and the issue of how to model the production capacity of a continuous plant by using discrete event simulation is addressed. A plant of continuous nature relies on a material-handling mechanism (such as conveyor or pipeline) to continuously convey material delivered in truckloads to a designated activity location at the site. In contrast with discrete resources commonly encountered and matched in construction (such as a truck, an excavator, and a crane), a buffer is the hallmark of a continuous plant (such as unloading container); and one or multiple feeder resources (trucks) can be simultaneously processed subject to the production capacity of the plant. With a concrete pump example, we discuss the potential pitfall of simplifying a continuous plant as one discrete resource entity. Then, we formalize an approximate method for representing a continuous plant with N discrete resource entities in simulation of a predominantly discrete operations system. We also describe two practical applications: (1) modeling the production capacity of a magnetic separation plant in iron mining operations; (2) modeling the passing capacity of an urban road section in microtunneling and pipe-jacking operations. In conclusion, the proposed method adds to the usefulness and flexibility of a discrete simulation methodology in modeling complicated construction systems.

Extreme events have always imposed radical constraints on planners and logistic managers. Fast-paced dynamics of these events necessitate more deliberate review of response plans. One aspect of extreme events for decision-makers and policy developers is the potential mobilization of the victims in the affected community and logistics of shelter provision. Within this focus area, research that integrates infrastructure characteristics is limited. This study presents an Agent-Based Model (ABM) to evaluate four different scenarios of community mobilization and shelter provision logistics during an extreme event: (1) victims go to a random shelter, (2) victims are directed to their closest shelter, (3) victims are mobilized based on a centralized shelter designation using "triages" and, (4) the shelter designation and the mobilization of victims is performed considering pre-established zones. The model integrates the exchange of information between agents (community and authoritiesi). Variables such as capacity of transportationinfrastructure, number and location of shelters,number of victims, among others, are also incorporated into the model. Preliminary results indicate the potential for application of ABM to improve the understanding of interactions among agents, their associated dynamics, and their impacts on different performance measures of the system (i.e., number of victims accommodated into shelters, and accommodation time) during an extreme event. Scenarios that incorporate triages did not always provide the best shelter provision results.

This paper introduces a real-time visualization method using a hybrid Light Detection and Ranging (LADAR) system to simultaneously assist multiple heavy equipment operators to perceive 3D working environments at dynamic construction sites. A custom-designed LADAR system was used to obtain 3D point cloud data, which can be transferred into data streams and uploaded to a remote operator's computer screen immediately. The data streams can be accessed by different equipment operators through local wireless or global networks such as Wi-Fi and 4G, and be further presented in dynamic 3D views. Strategies to rapidly updating 3D point cloud scenes are discussed in this paper, as well. The methodology presented in this paper has been implemented and tested at an equipment operation site and the proposed 3D visualization technique and data transmission network system were validated effectively. It is highly expected that the assistance of 3D visualization for equipment workspace will significantly reduce blind spots and obstructions, and improves operators' working efficiency.

The repetitive occurrence of similar accident in construction disasters is one of the prevalent features. Similar accident cases provide direct information for determining the risk of scheduled activities and planning safety countermeasure. Researchers have developed many systems in order to retrieve and use past accident cases. Although the developed systems have a clear and limited target, most of them were developed under a retrieval methods based on ad-hoc systems which can cause inconvenience for users in using the retrieval system. To overcome these limitations, this study proposes an automated information retrieval system that can search for and provide similar accident cases. The retrieval system extracts building information modeling objects and composes a query set by combining BIM objects with a project management information system. Based on the results of this study, the users can excessively reduce query generation. Furthermore, they can easily avoid risks by receiving similar past accident cases that can happen while they work.