Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation

For interactive visualization in AR devices, feature descriptors of point clouds (as-designed model and as-built model) are corresponded and registered. However, point cloud of indoor environment has lots of similar feature descriptors (e.g., indoor scene with similar doors and windows), which leads to many false correspondences and affect registration accuracy. This paper proposes a random sample consensus (RANSAC)-based false correspondence rejection to compute accurate transformation for the registration of such 3D point clouds. Point cloud data is collected from rooms and a hallway of a campus building, and transformation accuracy for the registration of those point clouds is tested. The results show that RANSAC-based false correspondence rejection gives transformation accuracy of 0.017 radians and 0.1924 meters in aligning two point cloud models, and hence the proposed registration approach of a model point cloud with scene point cloud may provide a foundation to accurately implement the AR on a construction jobsite.

Image-based technology is effective for recording on-site data geospatially and chronologically. It has gained increasing attention in the construction field for progress monitoring. However, a notable downside of image processing is the light condition, particularly for noisy environments such as construction sites. Poor or undesirable ambient light conditions produce low quality images which significantly affect the accuracy of data extracted from related images. This paper proposes an innovative approach based on thermal image analysis to mitigate problems related to the image quality. Construction materials with various emissivity resulting in measurable temperature differences can be a crucial evidence to identify different materials. In addition, a convolutional neural network was used to segment different components semantically. Together with a camera-view image extracted from BIM model, a cross matching can be applied to confirm the presence of different building elements with specific materials, which is the key step to estimate the construction process qualitatively.

In recent years, advanced visualization tools for the representation of construction projects has gained increased attention from educators and industry practitioners. As they become more receptive to such technologies, it is important to quantify and compare the impact of these technologies on users’ visual cognition of building representations. This study performs a quantitative comparison of users’ cognitive performance when using 2-dimensional (2D) drawings of a structure versus using a BIM-based immersive virtual reality (VR) model of the same structure. Participants were asked to complete visual tasks using the VR platform and the 2D drawings. Findings suggest that visual information depicted in VR is more effective in terms of one’s perception accuracy and memory. The study concludes that the immersive VR tool is suitable for architecture, engineering, and construction (AEC) students and professionals, even those with low spatial ability, facilitating learning and enhancing conventional 2D representations.

This paper aims at contributing to the debate on the extent to which cues of visual perception and pictorial realism in virtual reality (VR) environments are relevant in the context of construction practices. The discussion is conducted with the support of literature on human visual perception in physical and virtual environments. The article provides and develops on the argument that for various current VR applications in the construction industry VR environments may not need to resemble the physical world, especially in the design phase. Understanding the intricate relationship between our visual perception processes and information demands in the various construction activities could lead to the identification of visual aspects that are actually relevant for effective VR experiences. The discussion is merged into a summary on the power, limitations, and suitability of VR technology in building construction, followed by final considerations.

The availability of inexpensive and high-quality cameras has enabled research towards computer vision systems for tracking construction productivity and safety by detecting, tracking, and estimating pose of construction resources and recognizing their activities. To verify these algorithms can generalize to novel visual scenes before deployment, large amounts of labelled training and real-world validation samples are needed. While automatic generation of synthetic data helps with the former, obtaining the latter is less practical. To address this gap, we introduce a tool with which designated annotators perform pose annotation by interactively aligning 3D kinematically configurable equipment models with their depictions on 2D images. Multiple types of informative visual annotation can be retrieved using this technique, notably segmentation masks and equipment pose. Experiments demonstrate our tool’s effectiveness for annotating long-form videos of earthmoving operations. We demonstrate how our tool can provide ground-truth annotations for the evaluation of a variety of computer-vision algorithms.

Interactive workspaces and immersive virtual environments have come under academic and industry scrutiny as viable solutions to support the collaborative delivery process of facilities. However, choosing the appropriate and cost-effective technology for the development of an interactive workspace can be challenging. Therefore, there is a need to investigate the key technological features necessary to develop an interactive workspace. In this study, the researchers have set forward the goal of developing such a framework, which can guide the process of creating an interactive and collaborative workspace. Factors such as space, purpose, processes, cost, and technology were selected as the main drivers of the framework. Once its main components were defined, a decision map was created to indicate the path to achieve the best configuration for an interactive workspace. Future research will aim at validating the framework by interviewing experts in the professional and research world.

Since the advent of building information modeling (BIM), computer-generated models have been used to simulate design and construction, aiding team members in attaining a shared visualization of the project, and identifying potential errors before construction begins. Furthermore, visualization plays an essential role in estimating, as the process begins with a thorough understanding of the project scope. Construction estimating is the process of projecting the total cost of a project and is considered one of the most critical processes in construction. The outcomes of this process are used to make crucial decisions throughout construction. With the information embedded in the model, the time and laborious work required for performing quantity takeoffs are substantially reduced. However, the visualization that BIM provides is in a two-dimensional computer environment which is neither immersive nor interactive. Virtual reality (VR) is a three-dimensional interface that allows project members to walk through a building model and interact with its elements in a virtual environment. This study presents a methodology that uses VR as a visualization technique to streamline the cost estimating workflow. A case study is then presented to demonstrate the workflow and capacities of an estimating system that creates quality, interactive estimates in VR. The results indicated that implementing VR in cost estimation reduces the time needed to complete the estimating process and facilitates the discovery of a variety of value engineering options that benefit all parties involved. Moving forward, this research will be expanded to evaluate the use of VR in estimating courses in construction management education.

Virtual reality (VR)-based approaches have been used to facilitate safety knowledge transfer and increase hazard awareness by providing safe and controlled experiences of unsafe scenarios for construction safety training applications. However, high computational costs, long development times, and the limited sense of presence and realism associated with existing VR methods have posed significant challenges for using such VR-based safety training platforms. Unlike the VR environments that provide computer-generated simulations of the environment, 360-degree panoramas create true-to-reality simulations of environments while maintaining a high sense of presence. The purpose of this research is to develop and compare hazard identification training scenarios using 360-degree panorama and VR techniques. A pilot study, based on OSHA’s focus-four hazards, was conducted to assess the effectiveness of safety hazard identification training using these techniques. The results of this preliminary research indicate that hazard identification index scores were slightly higher on average in the VR condition comparing to the “messier” or “dirtier” as-build representation of the 360-degree panorama.

Holograms are digital representations blended into the physical space that enable the interaction of the users with the physical environment. This research explores the effect holograms have on the users’ experiences of the interpretation of representations of architectural and engineering designs with users’ association of the physical and functional characteristics in construction sites. The approach builds a mixed reality (MR) environment to create holographic visualizations from representations of designs generated from building information models (BIM) and construction document data (e.g., specifications, schedules). The approach immerses users into co-existent physical and virtual worlds using head mounted display (OST-HMD). The technology maps the users’ holographic experience with real-world construction context using spatial mapping and location tracking techniques.

Modular construction has gained increasing popularity, to various extents, in both building and infrastructure projects with the merits of accelerated schedules, improved quality, and reduced cost and waste. The successful delivery of modular projects heavily relies on safe and efficient on-site assembly of prefabricated modules, and this job is primarily executed by tower or mobile cranes. Compared to loads present in typical on-site construction, prefabricated modules being lifted are likely more substantial in size, heavier in weight, and more expensive, and therefore more stringent requirements on lift safety and precision are expected. However, due to the lack of appropriate tools, it remains a challenging task for the lift team to recognise potential spatial constraints and safety risks associated with lift tasks on the dynamic and complex construction site. This study explores a human-in-the-loop simulation approach to facilitate the identification and mitigation of task-specific and human factor related safety risks in crane lift planning. This approach employs as-built 3D point cloud to simulate pre-existing spatial constraints in a lift task. In addition, it incorporates human factors in the simulation through an immersive user interface and collaborative virtual environment. The proposed approach was tested in a case study of a module lift task, and the results indicate its potential to improve crane lift safety by eliminating hazards that are difficult to be recognised in conventional planning practices.

Visualization has shown to significantly assist communication and decision making within the architecture, engineering, construction, and operation (AECO) industry. The current visualization techniques do not incorporate the building spatial context due to lack of interaction and immersiveness. Extracting and visualizing building data are mainly focused on the statistical methods in the current practices which require user expertise to understand the information. With the advancements in augmented reality (AR) technologies, as well as sensors and Internet of things (IoT), physical and virtual worlds can now be connected to each other, allowing for real-time access to various information about the physical artifact. By integrating these technologies within smart-buildings, where real-time and historical information sensor data are available, AR tools can provide meaningful visualizations to inform occupants about their impacts on the total energy consumption. This paper proposes an approach where occupants can explore, visualize, and interact with office appliances and building systems energy consumption information by bridging the built environment data (sensor-readings) and AR technology.

A growing number of community energy initiatives have enlarged energy-related social networks to the community level. Information provision is deemed as an important role in such programs while energy data disclosure offers a great opportunity to promote energy savings by engaging energy-related actors. However, it is crucial to communicate this data in an effective way. In this research, we develop a virtual reality (VR) integrated eco-feedback system that enables both occupants and facility managers to interact with real-time energy consumption data represented in a community scale 3D immersive environment. This paper presents the detailed front-end and back-end design and development of this novel VR-integrated eco-feedback system using Georgia Tech’s campus as a test case for implementation. The VR-integrated community scale eco-feedback system is capable of visually characterizing differences in energy consumption across a large number of buildings of different types, and will be tested by users in future research. This research, when deployed broadly in cities, may help promote energy-aware behaviors of occupants and timely intervention strategies to achieve energy savings in urban areas.

To fill the lack of research efforts in virtual assembly of modules and training, this paper presents a virtual manipulation of building objects in an immersive virtual environment (IVE). A worker wearing a virtual reality (VR) head-mounted device (HMD) virtually perform an assembly of multiple modules while identifying any issues. Hand motions of the worker are tracked by a motion sensor mounted on the HMD. The worker can be graded based on his/her overall performance and speed during this VR simulation. The developed VR simulation can ultimately enable workers to identify unforeseen issues (e.g., not enough clearance for an object to be installed). The presented method can solve current deficiencies in discrepancy detection in 3D scanned models of elements. The developed VR platform can also be used for interactive training and simulation sessions that can potentially improve efficiency and help achieve better work performance for assemblies of complex systems.

Fire simulator training is a very effective way to reduce firefighter injuries and improve their mission performance. However, the usability of existing fire simulators remains an issue as most of these simulators require users to set up hundreds or even thousands of parameters to initiate the simulation. In this paper, we present a participatory and evolutionary fire simulation method that balances the accuracy and fidelity of the fire simulation and the usability. The base parameters for accurate and usable fire simulation can be selected by a combination of the evolutionary algorithm and the knowledge of domain experts via the virtual reality (VR) fire simulator. These base parameters are expected to expedite the use of fire simulators and reduce the adoption threshold.

Over the last few decades, the investments in more complex construction projects have increased the need for better communication means. Effective means of communications should be employed on construction projects to help aggregate dispersed information between project stakeholders. Advancements in information technology have resulted in new construction visualization techniques aiming at enhancing communication between project stakeholders. Some of these technologies include virtual reality, augmented reality, and holography. The aim of this paper is to further investigate the use of holography in the architecture, engineering, construction, and operations (AECO) industry. The holographic technology used in this research was previously developed by the authors through a game engine and an add-in for Revit. The add-in allows an easy transfer of information from a building information model in Revit to be used in the game engine. In addition, the developed hologram allows the user to interact with the hologram using voice commands and/or hand gestures. Both the game and the add-in were the subject of an experimental study conducted to evaluate their ease of use and effectiveness. The results proved that the developed add-in is easier to use and more effective than traditional methods used to export building models from modeling platforms into a gaming environment. Moreover, the interactive hologram was able to enhance the human-human interaction between multiple users. Future research will focus on further developing the software and on investigating the use of the holographic experience on job sites.

This paper describes the implementation of building information modeling (BIM) and life-cycle cost (LCC) analysis in the development of a BIM-game prototype. The authors address the interoperability of the game engine and use LCC analysis to enhance visualization and education. Inexperienced owners or stakeholders have difficulty understanding the relationship between initial cost and LCC, and therefore typically choose the initial value, which seems economical from their point of view. The primary objective of this paper, however, was to create a BIM-game prototype that helps owners, stakeholders, or students visualize and quickly understand the relationship between LCC and initial cost. To this end, the authors create a BIM-game prototype that integrates BIM with a game engine simulation (i.e., Unity 3D) into an LCC analysis visualization. The BIM-game prototype allows users to navigate their building environments with the capability of simulating the LCC. The elements of the system are a combination from among many fields including architecture, engineering, computer programming, and game development. These elements connect various areas such as building modeling, animation, navigation, and collision detection. Changing the materials and adding construction accessories simultaneously improved the LCC and the interaction of game engine controllers and the player’s interface. Finally, the authors developed a survey to evaluate the effectiveness of the BIM-game prototype. The results show significant potential for using the system in future projects.

Route choice models are essential to predict traffic levels and to provide real-time traffic information. Yet, developing predictive models that are sensitive to the contextual factors are rare or impossible with the conventional type of data collection. This study proposes the use of virtual reality (VR) for generating context-aware and high-fidelity data related to drivers’ route choice behavior. In this study, VR experimental platforms combined with a driving simulator are used as the test-bed to generate the initial inputs regarding drivers and their route choice behavior. This approach provides the chance to consider the impact of a specific contextual factor as well as the combination of sets of factors on drivers’ decision making. Moreover, it enables to incorporate human-related factors into the models and advance a more customizable research tool for future predictions and evaluations of roadway congestion. The study presents ten experimental scenarios aggregating several contextual factors (i.e. traffic condition, journey type, and social impact) and repeated measurements were collected from forty-one subjects (20 male and 21 females; age: 31.44±7.97). The authors applied a specific regression model using generalized estimating equation (GEE) to analyze the response data and make inferences on the model parameters.

Despite substantial efforts to address fall prevention and curb the number of injuries that occur in the construction industry, this sector still experiences the highest fatality rates among all industries. The fact that injuries still occur even after implementing these interventions highlights the need to determine whether providing more safety protection causes an increase in a roofer’s risk-taking behavior, which, in turn, contributes to increasing risk of an injury. Cave automatic virtual environment-based mixed-reality is used to create the perceptions necessary to capture the naturalistic behavior of roofers at a job site. The immersive mixed reality environment consists of a virtual projection of a residential environment and passive haptics feedback of the roof on a 2-story house. Participants were asked to complete a roofing activity under three manipulations (i.e., three levels of protection). A real-time tracking sensor, a physiological sensor, and subjective measures are used to localize worker position, obtain physiological responses, detect at-risk workers, and monitor their risk-taking behavior in each manipulation. This feasibility study enabled the first steps in the discovery of how and why roofers who are equipped with protective equipment may take more risks under hazardous situations. The contribution of this study is significant because it is expected to have immense translational importance in how the industry approaches the control needed post safety interventions.

The use of immersive virtual reality (IVR) with mobile platforms in the construction industry has been researched, but its application in the evaluation and selection of fall protection equipment presents a new challenge. On the other hand, the choosing by advantages (CBA) decision-making system has been applied with success in several construction projects and has proven to be beneficial over more traditional methods. In this study, a qualitative analysis is made to determine the impact of IVR in the selection of a fall protection system and in the increase of transparency when using the CBA method. The framework used consisted of performing two experiments with CBA for participants to choose the best option between a safety net and a lifeline during the construction of a concrete structure building. On the first experiment, the alternatives were shown in videos and in the second experiment the VR platform was used. In both cases, the same option was selected. Finally, a survey was conducted, and the findings indicate that IVR improves the process of reaching agreement through collaborative planning, helps to better comprehend the relationship between the safety system and the production system and enriches the CBA method by giving stakeholders a more comprehensive approach.

In emergency events, first responders often have to build an accurate spatial working memory of unfamiliar spaces in a short time period. This study investigates the impact of information format on first responders’ short-term spatial memory of large-scale spaces via a human-subject experiment (n=63). A virtual model was created to simulate a real building on Texas A&M University campus. A total of 28 building components were modified in the virtual model. Participants were asked to review the virtual model with one of the three methods: 2D drawing, 3D model, and VR model. After the review session, the participants were sent to the real building to identify the discrepancies, and accuracy was documented as the performance measure. The results reveal that the 3D and VR groups both significantly outperformed the 2D group in spatial working memory. This study sets the foundation to further understand how instructional information affects the performance of first responders in emergency response.

With the ability of mixed reality (MR) in overlaying digital information in the real world, architecture and construction practitioners have quickly recognized its value in on-site visualization for planning and verification. Although a few contractors have piloted some initial tests, no thorough reviews on its uses have been reported in existing literature. To bridge this gap, a comparative review of the capabilities of MR applications for architecture and construction was performed. Nine MR applications were identified, and their capabilities of on-site uses were evaluated and compared. It was found that although some applications performed better than others, no single application offered all desired features. In addition, the limitations of each application and the requirements of on-site uses were discussed. This paper provides timely and first-hand information to help the vast architecture and construction practitioners and researchers better understand the current capabilities of the MR technology in on-site construction visualization.

Design of office spaces plays an essential role in people’s day-to-day work productivity. Research in environmental psychology and neuroscience indicates distinct architectural design features (e.g., color coding, texture, and space layouts, etc.) impact human performance and motivation to work in office spaces. In the current practice, occupants evaluate work space designs via after-the-fact post-construction surveys subjectively. Limited studies exist in the literature on objectively quantifying motivational impact of space design on occupants. This research stems from the need for having objective ways to assess human experience in the built environment for design improvement. Integration of electro-encephalograph (EEG) and virtual reality (VR) equips researchers with the tools to measure human responses when subjects are immersed in alternative virtual designed spaces. This study proposed a machine learning based method to label subjects’ experience in spaces using their EEG data collected when they were in distinctly designed spaces. Results showed this method provided around 85% classification accuracy, which is comparable to other state-of-the-art EEG classification methods. Practitioners in the architecture engineering and construction (AEC) domain can use this method to identify if proposed design options have positive or negative impacts on future occupants.

Intelligent transportation systems (ITS) produce valuable data by using various sensors incorporated with transportation infrastructure. The use of ITS has transformed infrastructure management by making it easier and more efficient. Quality and comprehensiveness of the ITS data have direct impact on the effectiveness of planning, design, and maintenance activities. Previous research suggested that the fusion of ITS data with bridge information modeling (BrIM) can provide a reliable visual database that can satisfy design and maintenance needs while enhances the integration and management of databases. However, one major challenge identified is the lack of interoperability between the various software and systems required for the fusion. The industry foundation classes (IFC) is an international data exchange standard for building information modeling (BIM) that has been developed for the exchange of data within the building industry. Recently, IFC has been developed to include the required elements for modeling transportation infrastructure including bridges, highways, and tunnels. This research investigates the use of IFC to include the data that have been produced through ITS and are needed for the full exchange of information between transportation stakeholders. The scope of this paper is to provide a high-level evaluation of IFC to determine the feasibility and applicability of the augmentation of BrIM and ITS fusion data. The result of this research will help in the improvement of communication and collaboration between designers and stakeholders that can eventually help the management of transportation infrastructure.

Adverse environmental and economic impacts of construction waste (CW) have increased the need for automated estimation of CW generation to allow for more accurate and convenient quantification, which is necessary for effective construction waste management (CWM). Even though wood formwork quantities ordered for a project typically becomes waste, due to deterioration with time and usage, the accurate assessment of these quantities is critical for reducing CW at the site level. This paper seeks to propose a BIM-based automated estimation method for wood formwork, independent of geometric representation and parametric definitions available in a BIM model. The research is demonstrated on a real-world project, with wood formwork waste generation rate estimated at 0.005 m³/m² based on the proposed method. The comparison of the estimated wood formwork waste quantities and the actual formwork quantities manipulated from model parameters reveals a total difference of 19.7%, disaggregated to 5.4%, 11.5%, and 30% for the column, beam, and slab categories, respectively. The wood formwork waste estimated by the proposed method was further validated using the quantities reported in the waste hauling tickets of the project, revealing a 21.8% difference. This study provides a much-needed automated estimation method of wood formwork that is applicable on BIM models that have been authored with varying parametric conditions. Findings can serve as a guide for construction practitioners to assess key benefits of more accurate estimation of materials needed for a project in facilitating waste reduction and more effective CWM at the site level.

At present, indoor positioning has great potential for disaster mitigation, such as guiding evacuees to safe places. This research aims at developing such a sound-based method using artificial intelligence (AI) and building information modeling (BIM). Amid a disaster, first responders can quickly set up the proposed system to help indoor positioning, which relies on BIM, virtual reality (VR), and head related transfer functions (HRTF) techniques to simulate virtual sound fields. Then, a deep learning model is trained so as to be able to predict the current zone within a room based on the sound received. Unity, a serious game platform, and Steam Audio, a Unity plugin designed for adding 3D audio to VR experience, are employed to generate input data sets. The overall accuracy of the output results is about 90% though the training time is long, which can be reduced if more powerful computing resources are utilized.

This paper proposes a BIM-enabled method that assists in designing green plantation with a more realistic estimation of CO₂ capture than the current methods. For example, Taiwan’s building code provides an evaluation method for estimating the CO₂ capture of green plants. However, this simple manual calculation does not take two key factors into account, i.e., sunlight and plant variety, for CO₂ capture estimation, causing considerable uncertainty of estimated results. Recently, building information modeling (BIM) has emerged as an enabling technology for designers and engineers to make the best use of digital space for simulation of project tasks in advance and management of project information/knowledge. To fulfill the design requirement, the proposed method takes advantage of BIM to enable the sunlight simulation in 3D digital space along with a specialized plant database to help the designers select suitable plants for regions with different sunlight conditions within a building site. This BIM-enabled method is also different from the BIM-based ones that mainly take advantage of BIM to ease the design tasks under the current design paradigm and process.

Previous research used keywords like Hurricane Matthew/Sandy to filter the disaster- and damage-related social media data. However, various Twitter data containing these keywords were not describing the disaster events or their impacts. Meanwhile, machine learning demonstrates its potential for classifying social media data. Nevertheless, very limited existing research employs this approach for identifying damage-related social media data. This paper introduces the machine learning approach for identifying the damage-related social media data. Naïve Bayes, support vector machine (SVM), and decision tree are employed for training the classifier. The 10-folder cross-validation method is utilized for evaluating the performance of these three classifier models. Naïve Bayes model demonstrates the most reliable results. This paper provides a new solution for filtering the damage-related social media data during natural disasters. The manually annotated Twitter data can be used for classifying social media data in future disaster events.

In this paper, we present a machine-learning based method that allows company-specific construction knowledge to be automatically learned from past project schedules and weekly work plans without the need for any manual human input. The proposed model is built using long short-term memory recurrent neural networks (LSTM-RNNs) and is trained on construction sequences extracted from previous project schedules. While training, the model learns the likelihoods of different successor alternatives given a sequence of previous schedule activities. Experimental results on 12 real-world schedules show accurate and consistent predictions of potential future activities at various stages of construction. Results also demonstrate the method’s ability to formalize sequencing logic and mine what we call dynamic means and methods templates (DMMTs) from previous projects. When used as the engine for a project controls system, this solution has potential to automatically generate schedules using work templates; validate the correctness in the logic of an existing schedule; and revise look-ahead schedules.

Over the last few years, Worcester Polytechnic Institute (WPI) has been gradually exploring the potential contributions of embedded design and construction information contained in the building information model (BIM). The recent completion of a new mixed-use facility at the WPI campus provided an excellent opportunity to plan and execute an integrated approach to streamline the information handover between the designer, builder, and owner of this facility. This paper presents and evaluates the approach followed by the project team to accomplish such an integrated information handover. It describes the collaborative contractual strategy developed by the project team and their consultants and subcontractors. It also describes the implementation/execution plan involving the design and construction professionals as well as the academic opportunities and contributions by student projects. It also presents the results obtained at the completion of the construction and the plans to maintain continuity with information updates throughout the project lifecycle.

Indoor positioning is pivotal to various promising applications in building construction and facility management. Indoor position information of humans and objects, such as construction workers, materials, vehicles, and tools can largely improve working efficiency and workplace safety. Wi-Fi fingerprinting is a popular technique that collects Wi-Fi signal strengths, which are measured by received signal strength index (RSSI), for indoor positioning. However, Wi-Fi access points (WAPs) are normally placed arbitrarily, which causes poor positioning accuracy. In fact, positioning accuracy can be considerably enhanced by optimizing the access point (AP) placement strategy. In this paper, the objective of the AP placement optimization is to maximize the distinctiveness between individual Wi-Fi fingerprints in a virtual environment. BIM technology provides 3D geometric and semantic information to accurately reproduce the virtual environment for realistic Wi-Fi signal propagation simulation. The Wi-Fi signal propagation is modelled by a modified indoor radio wave path loss model, combined with the ray-tracing method using particle swarm optimization (PSO), to emulate the indoor multipath effect. The optimal AP placement strategy is obtained by using the genetic algorithm (GA). The accuracy test results inside a university library have validated that the developed AP placement optimization algorithm substantially enhances the positioning accuracy by 57.4%.

This paper focuses on linking currently disparate “global” and “local” descriptions of architectural intent and corresponding components via parametric digital models, with the aim of improving representations, enabling more informed conversations, and streamlining exchanges during early stages of design. In order to show the potential of this framework, research is focused on architectural precast concrete facades. Research references The Information Delivery Manual for Precast Concrete, describing methods for exchanging global descriptions (a layout of individual panel boundaries) and local descriptions (piece detailing of individual components). The goal of this work is to link data from parametric models representing global and local descriptions of architectural precast concrete façades in order to bridge the currently disparate workflows and values of designers and fabricators, simultaneously enabling design exploration and incorporating fabrication details, during concept phases of work.

The decisions taken throughout a building’s design stages steer a project’s success and outcome. The early stages involve choosing between alternative designs and form the basis of the following stages. Therefore, managing the early stages is crucial in avoiding a substantial amount of rework and reduced productivity. However, the lack of adequate and accurate information impedes informed decision-making. At the same time, current model-based planning techniques require extensive input data and produce very detailed designs, which can lead to false assumptions and model evaluations. Hence, this paper discusses a meta-model approach to support a formal definition of the model information in multiple design stages, incorporating potential vagueness. Thereafter, multiple visualization techniques are proposed for conveying the specified vagueness. This approach facilitates performing subjective estimation of a building model’s information at the early stages to support model analyses and decision-making.

In 2017, the Construction Industry Institute (CII)’s Upstream, Midstream, and Mining Commodity Market (UMMC) Research Team was established to explore design standardization through collaborative research. The RT’s essential research question is: “How can higher levels of facility design standardization be achieved in the UMM sector?” To tackle this research question, the RT identified and organized 38 innovative technologies and management approaches from various sectors. Based on various innovation theories and practices, the RT generated unique standardization applications for the UMMC sector and assessed the impact and likelihood of occurrences of their applications in the UMM sector. The results show that innovative technologies and management approaches such as modularization, mobile user interface devices, wireless networks for construction sites (WLAN), stakeholder engagement and alignment, and automated design have high impact toward overall design standardization that are likely to occur within the next five years. The analysis provides a snapshot to practitioners achieve higher levels of design standardization that will lead to greater predictability, increased agility, and increased cost-effectiveness of capital projects. The managerial implications of adopting innovative technologies and management approaches are also discussed.

Object signatures have been widely used in object detection and classification. Following a similar idea, the authors developed geometric signatures for architecture, engineering, and construction (AEC) objects such as footings, slabs, walls, beams, and columns. The signatures were developed both scientifically and empirically, by following a data-driven approach based on analysis of collected building information modeling (BIM) data using geometric theories. Rigorous geometric properties and statistical information were included in the developed geometric signatures. To enable an open access to BIM data using these signatures, the authors also initiated a BIM data repository with a preliminary collection of AEC objects and their geometric signatures. The developed geometric signatures were preliminarily tested by a small object classification experiment where 389 object instances from an architectural model were used. A rule-based algorithm developed using all parameter values of 14 features from the geometric signatures of the objects successfully classified 336 object instances into the correct categories of beams, columns, slabs, and walls. This higher than 85% accuracy showed the developed geometric signatures are promising. The collected and processed data were deposited into the Purdue University Research Repository (PURR) for sharing.

Recently, the number of BIM-applied construction projects has been increased with the expectation that it will turn labor-oriented industries into knowledge-intensive ones. Prior to application of BIM data, 3D modeling process should be preceded. The time and effort for modeling varies depending on the type of work, and therefore, most BIM applications has been focused on a certain work trade which shows high effectiveness. As a result, a doubt as to whether this new technology is effective in the construction industry has emerged. In this paper, we propose a quasi-automated BIM modeling methodology that can shorten the requisite time for modeling regardless of the type of work. This approach would resolve biased utilizations of BIM as well as suspicion issues regarding its efficacy. BIM is semantically-based and object-oriented, that is, individual element has its own geometrical shape with specific attribute as architectural element. This distinctive feature enables the proposed method. Based on drawing recognition and following line-text extraction, an object-oriented model is automatically generated. An effort demanded for modeling process can be reduced dramatically by automated modeling process. This procedure is demonstrated specifically with the practical experiment as well as detailed dataflow algorithm.

Volumetric construction, of which prefabricated prefinished volumetric construction (PPVC) is a specific type, offers numerous advantages such as reduced project duration, decreased on-site manpower, and better project quality over traditional construction methods. Additionally, in regions of high humidity, concrete volumetric construction modules offer greater maintainability than steel or timber. Hence, concrete volumetric construction is widely adopted in tropical built-up areas. However, concrete modules are heavy and methods to reduce the lifting weight are necessary. One such method is to partition concrete modules into smaller submodules or components that can then be joined onsite. Such a method offers a compromise in terms of faster installation, as well as smaller crane requirements. Despite the advantages, determining any optimal partition is computationally hard. Moreover, not all submodules are constructible; several constraints such as transportation, crane lifting capacity, and structural rigidity exist. To determine the constructability of a volumetric construction submodule, an ontology-based representation is developed from its building information model (BIM). Rules are defined to identify the constructible submodules from the ontology-based representation. The rules are validated using test cases against expert judgement, and shown to be accurate in identifying constructible concrete volumetric construction partitions.

Building information modeling (BIM) has changed the way information in design and construction is handled by allowing the possibility of exchanging project models and data together. To optimize the process, standards have been developed to define what is required in each exchange and how to represent it. The American Concrete Institute (ACI) has been working for several years in the development of the standards for one of the most important segments in the construction industry: cast-in-place (CIP) concrete. However, the monolithic nature of the material and its complex supply chain makes it harder for this development to be properly addressed. Parallel to the recent publications of the CIP BIM standards, further research is required to help the development of such standards move faster in order to get to a level where other materials already are. This work discusses the computational requirements that BIM software must address to allow a product model data schema, such as the industry foundation classes, optimally represent the updated CIP standards. It also proposes algorithms to automate processes typical of the material, such as structural detailing, construction requirements, and temporary structures.

Suboptimal information sharing is a key a factor that negatively impacts the construction sector’s massive productivity gap when compared to other sectors of the economy. This is due to management difficulties, supply chain issues, and rework. Building information modeling has been shown to improve dissemination of information but has not yet been exploited to its full potential. In this paper, we propose a new notation for visualizing project information in a BIM context. It is inspired by music and dance notation, and is designed to overcome current limitations that may cause the technology’s limited use during the construction phase. A proof of concept was implemented and tested in an experiment with stakeholders. The use of the proposed BIM notations appeared to make access to and interpretation of available data more effective and resulted in more correct responses.

Automated data capture systems could significantly improve the efficiency and productivity of the architecture, engineering, construction, and facility management (AEC/FM) industry. However, automatically collecting spatiotemporal information in an unstructured environment such as a construction site or a work place remains a time consuming and challenging task. This paper presents a new approach to automated data capture and processing, referred to as object characterization. In object characterization, the goal is to identify common objects in a scene and extract rich semantic information about those objects. A novel 2D-3D object detection algorithm is designed for detection and characterization of common worksite objects. The proposed system has applications in automated surveying and data collection, especially in applications which leverage unmanned aerial vehicles or mobile robots. To demonstrate this utility, the proposed system is deployed on a mobile robot and used to detect newly placed objects in a worksite environment.

Representing a new generation of mass timber products, intended for commercial construction, cross laminated timber (CLT) panels are a reliable, cost effective material for mid-rise construction due to their physical characteristics and suitability to pre-fabrication. These qualities combined with the maturing North American forestry stock have prompted an increasing use of CLT in the U.S. AEC industry. However there is no established framework to support the information requirements for CLT building systems, to permit highly automated industrial production. Our research focuses on developing a data schema for streamlining information flows and coordination in different stages of design, manufacturing, and assembly of buildings using CLT. The framework is demonstrated for CLT floor systems, and shows the adaptability of the schema for different support configurations. In the proposed system, the floor framing adapts to global geometry. Finally, an automated system for panel edges, based on the overall framing logic, is proposed.

Indoor path networks are the foundation for route planning and navigation during evacuations. However, indoor path networks are still not developed sufficiently to represent building layout as street maps have been because of complicated building layout and the lack of definite paths in buildings. This study proposed an approach to automatically generate indoor path networks in building information modeling (BIM) platform. The proposed approach consists of four steps: construct corridor path network using straight skeletons, connect doors to corridors, connect different floors by stair paths, and remove redundant path lines. To validate the proposed method, it was used to generate indoor path networks for a three-story building in Revit software. The achieved path network can benefit evacuation planning and indoor navigation, and also provides potential applications for designers to check or optimize building layout.

As the growing demand for building new housing units hits the record, significant pressure from stockholders investing in the construction industry has been challenging the authority having jurisdiction (AHJ) to issue the construction permit in shorter time. As a result, some government entities throughout the state of Florida have adopted an online permit application process to reduce the time and paperwork with little to no success. This paper presents data collected from AHJ plan reviewers, architects, engineers, and general contractors as an essential part of the investigation along with the results of the analysis of the actual plan review process in the state of Florida. Based on these outcomes, a virtual permitting (VP) framework is proposed using building information modeling (BIM) and the 2017 Florida Building Code-Residential (2017 FBC-R) to enhance the efficiency and cost-effectiveness of the building permit process.

Route planning inside buildings is critical for route optimization during daily indoor navigation and emergency response. Accurate spatial information to develop indoor route plans has become essential as buildings get bigger and more complex. Previous studies have proposed and developed different approaches to improve indoor route planning by using the building information modeling (BIM) technology as BIM can provide detailed semantic and geometric information. However, a comprehensive review of previous publications is still unavailable. This study reviews the relevant papers published from 2008 to 2018 to demonstrate the existing efforts on different stages of its development process, namely BIM preparation, navigation model generation, path-finding implementation, and applications. In addition, this study further conducts technical discussions and identifies potential research directions for future development. It is expected that from this study researchers can holistically understand the prior development trends of BIM-oriented indoor route planning and identify potential places for future work.

Blockchain technology has been devised from the first cryptocurrency known as Bitcoin and was soon noted to have a much more full range of applications beyond serving as the platform for the digital cryptocurrency. Conceptually, a blockchain (BC) is defined as a decentralized ledger that registers every transactional operation made in encrypted data format. The implementation of such concept bring about augmented security, enforce accountability, and could potentially accelerate a shift in the work environment dynamics from the current centralized structure to a decentralized, cooperative chain of transactions by encouraging trust and transparency. This paper presents an overview of BC technology and its applications in the AEC industry and its potential integration with the building information (BIM) process. Furthermore, the study explores how BC technologies can improve the BIM working environment by reinforcing network security, providing more reliable data storage and management of permissions, ensuring change tracing and data ownership. The paper discusses the fundamental principles of distributed ledgers, their current applications and potential future advances, and their classification based on inherent characteristics of consensus reaching and permission management. The study presents some examples the prospective application of BC technologies in enhancing the framework for automating the code compliance checking process such as smart contract technologies and Hyperledger Fabric, as well as discussing the pros, cons, possible directions, and future research directions.

Today’s model checking applications are capable of checking building models for conformance to code clauses which restrict explicit dimension values, but very little more than that. Complex, implicit code clauses are still beyond the scope of such applications. Much research and development is needed for semantic enrichment (to pre-process models for checking) and for automating checking of complex conditions. A review of the state-of-the-art reveals that research and development has been limited to the application of symbolic AI methods, such as rule inferencing. Based on this review and on a set of early experiments designed to test basic feasibility, the authors propose a framework for research and development that specifically extends current thinking to include machine learning methods for both semantic enrichment and model review. Machine learning is shown to be applicable in situations where expression of the constraints in the form of explicit rules is impractical.

Building information modeling (BIM) is widely used in the architectural, engineering, and construction (AEC) domain to support different applications such as cost estimation, planning and scheduling, and structural analysis. Structural analysis is an essential way to ensure structural safety. However, different structural analysis software may not process all information from building information models (BIMs) correctly, which impedes BIM interoperability. To address this problem, the authors proposed a new method for automatically checking information completeness of BIMs to support BIM usage in structural analysis in an interoperable manner. The method was tested in an experimental implementation using python programs and a structural analysis software. The checking results using the proposed method was compared with results from a manual checking and a model view definition (MVD)-based checking, respectively. The experiment showed a comparable or better performance of the proposed method in accuracy and efficiency than manual checking and MVD-based checking. Furthermore, the proposed method overcomes the scope limitation possessed by MVD-based checking. Therefore, the proposed information checking method is expected to support BIM interoperability by helping people identify missing information from IFC-based BIMs. The authors also proposed a new system model for the BIM information checking domain [i.e., information, model, application, and application context (IMAAC) model].

Equipment travel path planning is one of the significant components of construction site layout design. Navigating a terrain and determining the shortest path on a construction site is one fundamental concern in path planning. The major constraints used for our path planning process is the shortest path between desired cycle locations and obstacle avoidance. By implementing building information modeling (BIM) as a project communication platform, automated equipment path planning within a dynamic construction environment becomes possible. This created BIM-based path planning strategy also enables construction managers to plan and schedule equipment operations with other project stakeholders. The objective of this research is to design a BIM-based strategy and automated path planning method to calculate and display an efficient equipment travel path in a BIM environment. The research framework is tested with active construction sites data. The feedbacks from workforce and management is assessed and integrated into the research approach.

The transfer of design data into nature is a necessary task during the construction process. For this, the geodetic coordinate reference system (gCRS) used during the design process needs to be accounted for and the distortions included need to be handled appropriately. In the context of building information modelling (BIM) and the vendor-neutral data format industry foundation classes (IFC), the gCRS represents an important metadata item of the model which is included and maintained throughout the project’s lifetime. Although the IFC4 supports gCRSs by providing the option to refer to an identifier of the European Petroleum Survey Group (EPSG) database, it is not able to handle custom gCRSs, which can be defined for large infrastructure projects, such as the Brenner Base Tunnel (BBT). We highlight these deficiencies of the schema and propose a novel approach by expanding the IFC schema with the well-known text (WKT) notation.

Construction projects often exceed budgets and schedules, and the industry, in general, has failed to keep up with other sectors in terms of productivity growth. Fragmentation of the many designers and builders and the methods to bring these participants together for project delivery are major factors affecting project outcomes. One commonly-cited remedy is the application of integrative processes, where the project team is brought together early and often to collaborate on the best design options for the project goals. However, integrative practices have received limited application in the construction industry and have demonstrated only incremental improvements to project outcomes. This paper presents three case studies of integrative structural design processes that are enabled by systems-level automation and have demonstrated significant benefits in terms of design exploration and knowledge management. These systems are evaluated for their potential implications for structural engineering and the industry in general.

Information exchange among architects, contractors, and clients is found to be limited and not completely digital, which creates difficulty in tracking and analyzing the generated unstructured data. Design drawings are developed during the design phase, but not transparently shared among contractors, due to the fear of uncertain changes in design without knowing who changed the designed element and what element is been modified, which can lead to disputes. Blockchain technology enables the transaction of digital information, and revolutionizing different industries like banking, crypto exchanges, insurance, etc., due to its decentralized, encrypted, and secured functionality. This paper proposes the potential application of blockchain technology in design development phase of architecture, engineering, and construction (AEC) processes by developing a blockchain framework. In this research, the design data generated from design development software and its log files are categorized, and flow of data encryption is determined by using blockchain concepts in order to have a secure ledger, which has the potential to provide a validation to an argument of disputes. The expected contribution of this proposed research is to create a framework to track and register the design commands and events in a secured, transparent, and collaborative platform. The anticipated benefits of this research include the record of proof of work and the mitigation of disputes and litigations related to design defects which includes design error and omissions and construction defects which includes either replacement or modifications in design, resulting to change orders and monetary losses.

A major gap in the automation of construction cost estimation is the need of manual inputs to complete cost estimation processes. To address this gap, the authors propose a new method for matching wood building elements from a building information modeling (BIM)-based design to cost data entries in a cost database. The proposed method uses a java constructor and HashMap to create objects, and store and retrieve the created values of the objects. Term matching and natural language processing (NLP) techniques are used in the method to match items from a design model and automatically extract their unit costs from a cost database. These unit costs retrieved are then used in generating the cost estimates. The proposed method was tested on estimating a wood construction model retrieved online. A cost estimate was successfully generated. Comparison of the experimental results with results from the state-of-the-art commercial software showed that the algorithms developed from the proposed method reduced the manual inputs required in generating wood construction cost estimates.

Creating deep learning classifiers requires large labeled datasets; and creating large labeled datasets requires elaborate crowdsourcing systems and many hours of manual human effort applied to classification and data entry. Fortunately, much of this effort can be bypassed in the building industry because of as-built building information models (BIMs), a semantically rich form of facility information. From these BIMs, semantics can be transferred to point clouds. This paper presents a method for creating large labeled datasets for training deep neural networks to semantically segment point clouds of buildings. Geometry and attached semantics are extracted from a BIM. The geometry is registered with the point cloud and the BIM semantics are copied to the points in the point cloud. The presented method enables organizations with access to as-built BIMs to forgo the effort of creating large labeled datasets and instead use the embodied effort in their pre-existing BIMs.

Buildings contribute a large portion of world’s energy consumption and greenhouse gas emissions. Researchers have endeavored to optimize the architectural layout design for minimizing energy use and carbon emissions from building operation, whilst others focused on structural optimization to reduce the embodied carbon of construction materials. As previous studies targeted on individual aspects and provided incremental improvement only, it is necessary to synthesize architectural and structural optimization in a single process to enhance the low carbon building design. This paper presents an integrated simulation-based optimization approach to optimize building architectural layout and structural form designs, with the aim of minimizing both the operational and embodied carbon. Building information modeling (BIM) is utilized to interoperate multiple design aspects, providing an integrated analysis on low carbon building designs. Illustrative examples reveal that the proposed method reduces 30% of the building operational carbon and 20% of the embodied carbon from construction materials.

Recently there has been growing recognition of the value of facility management (FM) in the construction field. Nowadays, FM data can be extracted from parameters of BIM (building information modeling) models. Thus, it is increasingly important to manage parameters of BIM model elements in the operation and maintenance (O&M) stage. Currently, parameters in a BIM model are mostly treated as static (i.e. fixed information of a model element). In order to make BIM model more valuable in the O&M stage, variable parameters (operating information of a model element) should be considered in a BIM model to address the data management need in actual environment of a building, such as status of a building element (e.g. movable wall’s status), IoT (Internet of things) data, etc. This paper presents an effort in designing a database schema for integrating both static and variable parameters of a BIM model into the database. The design considers the compatibility with COBie (construction operations building information exchange) and supports visual management of BIM model parameters, especially variable parameters. In addition, applications of the database with the proposed design are demonstrated through API implementation in Autodesk Forge.

Over the past two decades, hundreds of studies have been published that demonstrate the benefits of 4D building information modeling (BIM) for optimizing construction planning and scheduling. Nonetheless to date, 4D BIM has only been adopted by 35–40% of top engineering news-record (ENR) companies and only used on a small fraction of their projects. The longevity of using 4D BIM also rarely outlasts the pre-construction phase. While the value associated with using 4D BIMs during the construction phase is well documented, the level of effort required to create them has significantly impacted perceptions about their return of investment (ROI) and limited their adoption. To address these inefficiencies, this paper presents a new method—comprised of text mining and machine learning (ML) techniques. Our method parses the description of construction schedule activities, assembles a breakdown structure of work locations/areas, and maps each activity to its corresponding 3D BIM element. Our method also labels each activity to a project phase such that these activities can be animated with different visual cues. Experiment results on 10 real-world construction projects show that the method achieved 89% accuracy in the parsing task. The benefits of the proposed method are discussed in detail.

Utility regulatory documents usually stipulate the spatial configurations between utilities and their surroundings. Noncompliance with these spatial configurations will lead to recurrent utility incidents (e.g., pipeline contamination and erosion). Therefore, spatial compliance checking is required before a utility routing design is proposed. A critical step of utility spatial compliance checking is to translate natural language (NL)-based spatial constraints into executable logic rules. This paper presents a generic methodology of modeling NL-based spatial constraints. First, formal spatial knowledge representation is proposed. Then spatial cognitive-linguistic information is extracted using the natural language processing (NLP) algorithm and formalized into spatial knowledge. Finally, deontic logic (DL) is used to capture the information regarding normative modalities and hierarchical structures prescribed in spatial constraints. The performance of the NLP algorithm is evaluated and a framework of implementing DL-based spatial constraints in a spatial database is presented in this paper.

Bridge information modeling (BrIM) techniques have been developed to integrate information from architecture, engineering, construction, and operation. The models developed by these different stakeholders, however, are oftentimes not interoperable. To address the challenge, this paper proposes a BrIM framework to ensure consistent data exchange. Designed as the central hub for data exchange, the Python program translates between the BrIM model and Mongo database, a NoSQL (Not Only SQL) database. The unifying Python framework is demonstrated with the BrIM model of a steel pedestrian bridge located in Atlanta, Georgia. In this case study, the OpenBrIM standard is employed to create the geometry model and the finite element model of the bridge structure with visualization. If change is made in either the OpenBrIM model or the Mongo database, the Python program can automatically synchronize the changes to the others.

Generating a semantically enriched solid model of a structure from point cloud data is time-consuming. Many reinforced concrete bridges consist of a specific set of structures for which a large part of the processing from scan to bridge information model (BrIM) can be automated with today’s algorithms. We contribute a pipeline for going from a laser scan point cloud to a BrIM for simple concrete bridges (girder, box girder, and slab). The procedure consists of four major steps: (1) top-down partitioning of the bridges, (2) bottom-up segmentation of the component surface elements, (3) recognition of components from surface elements, and (4) reconstruction of the components. The top-down partitioning step informs machine learning methods for identifying the bridge component type and its geometric model. Application of the pipeline to two bridges demonstrates that conversion to BrIM at level of detail 200 is possible. The pipeline takes less than 1 hour of user time, which is less than scanning time, thus inverting the Scan2BrIM labor ratio.

Model view definitions (MVDs) in various domains have been developed under the IFC schema, as subset standards for their respective domain-specific BIM data exchanges. To avoid repetitive definitions of data exchange requirements, numerous concepts that entail modularized definitions for entities, attributes, relationships, and properties have been employed to construct MVDs. However, the lack of a framework for solid MVD development and concept reuse has resulted in an inconsistent data mapping among information delivery manuals and MVDs and caused heterogeneous MVD definitions and structures. To address this demanding issue, this study proposes an entity-based MVD integration approach that generates encapsulated MVD concept modules and provides semantic query features allowing MVD developers to identify targeted concepts from an existing MVD concept library and retrieve the associated concepts to develop a new MVD. By using an mvdXML format, this approach currently aggregates the following three MVDs: precast/pre-stressed concrete, steel, and concrete domains. This new approach is expected to facilitate MVD development by providing reusable concept modules to initiate MVD development from other domains’ predefined MVDs by retrieving their concepts and giving them opportunities to confirm the accuracy and integrity of an MVD according to other domains’ ones.

Building information modeling (BIM) has been increasingly used to coordinate construction tasks, and one of its most widely used applications is automatic clash detection. Clash detection consists of discovering potential problems or interferences in the model, but it does not entail solving these clashes. Some studies have discussed automatic clash correction from the knowledge management perspective, but their approach to clash correction rarely discusses the dependent relationships between building components. However, a building is an inseparable whole and building components have dependent relations. Relocating one object to correct one clash may result in other objects violating spatial constraints, which may directly cause new clashes or indirectly cause them by relocating other components. The dependency structure propagates the impacts of clashes. Therefore, it is necessary to holistically solve clashes by considering the dependence relationships between elements. This paper analyses six kinds of spatial relations: intersect, penetration through, penetration, containment, connection, and impact relations, and designs algorithms to query these relations from an industry foundation classes (IFC) model based on a bounding volume hierarchy (BVH) structure. Spatial networks among building components are formulated based on these relations, and several potential network applications are proposed. The spatial network methods are tested on a real project, leading to help identify irrelevant clashes and reduce the number of objects that need to be changed.

Planning an efficient construction site layout increases safety and productivity of operations. As opposed to considering only confined construction sites, this paper optimizes site layout for linear infrastructure projects in congested inner cities roads (i.e., road maintenance). The construction site location of these projects dynamically changes as the work progresses. Therefore, selecting construction facilities locations (CFL) is a major problem prior to organizing these facilities. The presented work optimizes CFL selection in inner-cities congested roads, using uniform-cost search (UCS) as an optimization tool, through minimizing the (1) resources transportation cost, (2) land renting cost, and (3) facilities relocation cost. The proposed model is coded in Java using NetBeans IDE 8.1 platform. A hypothetical case study with a solution space of 243 solutions was conducted to demonstrate the model’s benefits. The model succeeded in finding the optimal CFL for road segments in under 1,100 milliseconds using an 8 GB RAM, 2.00 GHz machine.

The rapid development of cities has increased the demand for educational facilities that can cope with current issues such as environment conservation, climate change, and safety, among others. In order to adapt to the needs of this new generation, renovation of existing facilities using concepts such as sustainability can improve the performance of new and existing infrastructure while taking into account cultural and economic factors. This research was done using environmental simulation software. The analysis was divided in two parts. First, the weather of the site was analyzed using simulation software to test which passive design strategy was more appropriate to focus the design on the next stage. Second, based on the results from the previous step, the research proposes 3 basic massing shapes for the school building, which were evaluated to discover which shape and building height is more appropriate for the site by analyzing incident solar radiation on the walls of the building. At the end of the analysis, the research team would choose one option to continue the design stage of the project.

The adoption, implementation, and the extended use of technologies in the construction industry is a complex phenomenon, impacted by multiple industry-specific variables. Researchers have suggested that the lack of decision support mechanisms—such as forecasting and predicting tools—are significant reasons for the stark contrast in the rate of technology diffusion between construction and other industries. Some industries have successfully utilized simulation tools, such as agent-based model (ABM), to forecast the adoption of innovations at an organizational level and their potential impact on work operations. Construction management research is yet to explore the utility of similar forecasting tools in the context of technology adoption decision making. This study presents an exploratory framework that integrates ABM into technology adoption decision making. The ABM was developed using archival data extracted from a composite model consisting of two established and complementary socio-cognitive theoretical models-technology acceptance model (TAM) and theory of planned behavior (TPB). The applicability of the proposed simulation model is discussed. This simulation approach is intended to lead to the development of an analysis tool for assisting construction organizations involved in technology integration. It is expected that by using a version of the proposed framework, an organization can develop policies and training programs that ensure future successful adoption of a construction management technology using the process described in this paper.

Overlapping of activities on fast-track projects generates unique risks which can be grouped using a new quantitative model. The model employs clustering analysis to classify the risks according to the risk categories and activity evolution, sensitivity, production reliability, and constraints. A hierarchical method is first applied using the centroid method and square Euclidean distance to define the number of clusters. Then, we apply a nonhierarchical method using K-means and a convergence criterion of zero to generate the final groups. A base case with data collected through a case study using a commercial construction project illustrates the clustering approach. Results showed that the risks could be combined into ten clusters. Although the results will vary by project, the model can be applied to group risks of any fast-track construction project and support the definition of mitigation actions and allocation of responsibilities.

Previous studies have recognized the social factor of safety violation within the construction crew. However, discussion on the social contagion effect of safety violation is still sparse. Therefore, this research aims to develop a hybrid simulation approach for looking into the cognitive, social, and organizational aspects that can altogether determine the social contagion effect of safety violations within the construction crew. The hybrid approach integrates system dynamics (SD) and agent-based modeling (ABM) to better represent the real world. SD is used to capture the systematic level dynamics such as the influence of perceived production pressure. ABM is employed to mimic the decision rules and interactions among the worker and management. The research contributes to providing a practical framework to examine how specific accident prevention measures (e.g., setting the safety goal) interacting with worker or environmental characteristics (e.g., the hazard level) influence the social contagion effect of safety violations. Our findings show that different interventions should be taken for situational and routine safety violations. Our model can also serve as a test-bed to evaluate different safety programs or strategies by considering the social contagion effect of safety violations.

The traditional literature review process is not quantitative in nature, leading to duplication of work and publication of papers that tackle secondary knowledge gaps while missing the primary ones. This paper proposes a novel network-based methodology for quantitative analysis of model-based literary works, which utilize simulation models such as agent-based modeling (ABM), system dynamics (SD), and discrete event simulation (DES). The proposed methodology uses concepts from social network analysis (SNA) to: (1) study how the relationship between different modeling parameters, (2) compare their relative importance, (3) and identify the areas that are ill-studied. The developed methodology was used to analyze the literature of the application of SD models in managing construction projects. The results show that there is a significant gap regarding out-of-sequence work. The developed methodology directs researchers towards the proper knowledge gaps. This will lead to more well-directed research that has significant additions to the body of knowledge.

The use and development of system dynamics (SD) models has been growing in construction management research. However, it is noticed that the complexity of such models has stagnated. This holds back on the full capabilities of SD. It is hypothesized that such stagnation is caused by the lack of multi-stage calibration guidelines. In SD, calibration is the process of finding the optimal values of certain model parameters to replicate the behavior of the project that is analyzed. Current models use single-stage calibration. The authors propose a multi-stage methodology for constructing advanced SD models and calibrating them in sequential steps. The developed methodology was validated by testing it in an advanced SD model that studies the impacts of out-of-sequence work in construction projects. By using the developed methodology, researchers will be able to develop more advanced SD models. This has practical benefits in dispute resolution and project control.

The increasing adoption of small-scale technologies for generating power represents a transition from centralized electricity systems to distributed energy resources (DER). This paper develops a multi agent-based model (ABM) to study the dynamic behaviors that influence DER investment decisions. The proposed model accounts for both local utilities (and load serving entities “LSE”) and power generating plants. DER is introduced into the model by allowing LSE customers to invest in a particular photovoltaic systems. Consequently, the model forecasts electricity market outcomes and PV system adoption. Using an illustrative hypothetical case study, results show that customer demand in most LPCs cut in half, annual capacity factors at coal-fired power plants reduced by more than 60 percent, and sharp decreases in locational marginal prices. Ultimately, this research will result in a decision-support tool that will identify least cost strategies that utility companies can use to respond to increasing penetration of DER.

Nuclear power plant disasters in the past century have led to the major economic loss, negative environmental impact, and danger to human lives. Disaster relief operations such as search and rescue, hazard detection, and damage assessment face significant challenges due to the difficulty in categorizing and analyzing damaged building structures. This study proposes an efficient method to generate a large number of 3D geometric models of a post-disaster nuclear power plant site using the Bullet physics engine. Two scenarios of nuclear power plant disasters, which are explosions due to reactor failure as well as earthquakes, are considered in this study. Different degrees of virtual forces are applied to the nuclear power plant site, and the resulting geometry of damaged structures are recorded. The generated database of damaged structures has many potential applications in object recognition, risk classification, and hazard analysis.

This paper describes a dynamic simulation application in modeling complex workflows of a bridge girder fabrication shop based on a three-tiered modeling framework. The objective of this framework is to produce straightforward, adaptive simulation models that can sufficiently represent essential operational details on the shop floor from the perspective of a shop superintendent. The resulting top-tier model allows the practitioner to define complex process logic in a way similar to developing activity-on-node network diagram for critical path scheduling. The middle tier is the core of the simulation where the entire shop operation logic, duration of each activity, and resource use constraints are encoded and mapped into a network diagram model. The bottom tier is essentially the simulation engine which efficiently schedules time events as per the logic and constraints defined in the middle tier and the top tier. The process of splicing two girders for trial assembly in a fabrication shop is illustrated in order to demonstrate the applicability of the proposed framework.

Scheduling linear projects requires an optimization tool that does not only minimizes project duration and cost, but also maximizes the utilization of crews, accounts for travelling distance between units, and meets the delivery dates of the project’s units. This paper presents a multi-objective optimization model for scheduling linear projects through developing set of non-dominated optimal schedules. The proposed model consists of: (1) a resource driven scheduling module accounting for heterogeneity among construction crews, and (2) an evolutionary optimization module via genetics algorithms (GAs) and Pareto front sorting (PFS) that searches the solution space for optimal schedules. The model is tested on a case study drawn from the literature and provided significantly better results compared to some of the well-recognized scheduling models. The proposed model is coded using Visual Basics for Applications on a commercial scheduling tool and can be easily adopted by practitioners to provide a broad-spectrum of optimal schedules.

Construction practitioners face considerable challenges when selecting economically feasible policies for maximizing crew motivation and performance. This paper proposes a methodology for integrating fuzzy agent-based modeling (FABM) with multi-criteria decision-making (MCDM) to develop a decision support system, which takes into account the complex relationships and social interactions between crews and crew members. This decision support system both accounts for the dynamic construction environment and captures the subjective and the objective factors that influence crew motivation and performance. The proposed methodology and decision support system are illustrated with a case study, which demonstrates that integration of FABM with MCDM serves to address subjective uncertainty when analyzing different policies related to crew motivation and performance. These findings can in turn help construction practitioners adopt economically feasible strategies to improve the motivation and performance of their crews, thus increasing their competitiveness in the market.

Accurate cost estimating is vital for the survival and success of construction contractors. While improving cost estimating accuracy is encouraged in the literature, there is not sufficient evidence on how it changes contractors’ profitability and winning rate in the cost-based competitive bidding. The objective of this study is to examine the effect of improving cost estimating accuracy on the performance of contractors and the market using agent-based modeling (ABM). ABM is a powerful tool for simulating and analyzing complex systems and conducting controlled experiments. ABM is used in this study to simulate the interaction among contractors in the competitive bidding and conduct multiple research experiments with different settings. The results suggest that improving cost estimating accuracy leads to a considerable increase in the long-term profitability of the improved contractor in all market scenarios. However, this improvement may lower the winning rate or project profit depending on the market scenario.

In theory, time-cost tradeoff (TCT) analysis is a classic planning problem appealing to construction management; yet, existing analytical methods are found inadequate to make a significant impact in practice. Heuristic methods lack a theoretical basis to ensure arriving at optimum solutions in solving specific problems; on the other hand, mathematical programming requires cumbersome, complicated formulation. This study proposes a new computing framework for TCT optimization that takes advantage of a path-float based technique and integer programming (IP). The project duration can be shortened in each crashing cycle based on path-float analysis; while IP is nested to inform on which activities on the critical path(s) to shorten by how long duration. The new TCT optimization approach streamlines critical path analysis in each cycle and finds global optimums in terms of lowest project cost or shortest project duration. Because only part of the network (critical path) is considered in IP formulation in each intermediate cycle, the complexity of IP formulation plus the search space is substantially reduced. A case study is used to verify the proposed method and demonstrate its application. The proposed method can be automated to tackle large project networks commonly encountered in workface planning.

Lighting occupancy sensors offer great potential for decreasing lighting energy consumption by enhancing control options in residential and commercial buildings. Although occupancy sensors and their energy saving potential have been extensively studied through case studies, methods for investigating the feasibility of implementing this technology to renovate a specific building, considering a pre-defined occupant behavior, are very limited. This study introduces a framework that considers occupant behavior to analyze the impact of lighting occupancy sensors on the building lighting energy consumption, through the use of agent-based modeling. This study considers three main occupancy parameters, including number of occupants, occupant presence and movement, and occupant action behavior (i.e., switching the lights on/off in this case). The framework is able to analyze the feasibility of installing different lighting occupancy sensors and select the most cost-effective one, considering particular occupancy behavioral patterns. To validate the model, this study conducted a simplified office case study.

The efficiency and effectiveness of information transfer in collaborative project networks directly affect successful delivery of collaborative projects, especially large-scale construction projects. However, current research seldom explored the information transfer mechanism in collaborative project networks in a dynamic fashion. The purpose of this study is to investigate the dynamic information transfer mechanism in collaborative project networks. An agent-based model is established and programmed in NetLogo, which simulates dynamic information transfer processes of collaborative project networks. The key part of the model is the rules system that includes three aspects: the rules of information dissemination, rules of information absorption, and rules of efficiency and effectiveness evaluation of information transfer. The case study of a typical collaborative project is conducted to perform the model. The simulation results show that the model can present dynamic processes of information transfer in collaborative project networks. Based on the model, strategies of network structure adjustments can be proposed to promote the efficiency and effectiveness of information transfer in future research.

The purpose of this study is to investigate performance indicators measuring the effectiveness of the claim management process of construction projects. As current construction projects become larger and more complex, claim management becomes more diverse and important for the success of the project. Although claim management has been the subject of many previous research studies, few researchers have investigated performance indicators measuring the claim management process. After interviewing practitioners who have expertise in claims, the researchers for this study propose indicators representing the performance of the claim management process. These indicators can be used as performance measures to enable more detailed status checks and to improve the process for construction claim work, which will contribute to better claim management and more successful project delivery.

The economic performance of additive manufacturing (AM) is a heated discussion in the construction industry. The affordability of 3D printing is a critical factor for massive application in practice. Contour crafting (CC), one of the most common AM methods adopted in the construction industry, creates objects with smooth surfaces using polymer, ceramic slurry, cement, etc. This paper first analyzed the direct and indirect costs incurred during the CC process and then built an activity-based cost model. Direct costs were associated with the volume of the materials consumed in the printing process (including waste), while indirect costs relate to the manufacturing time, including labor hours and operating time of equipment. The activity-based cost model was formulated based on a concrete wall fabricated by CC. Next step of the study is to account for the impact of uncertainties in the modeling process and validate the model through an empirical study.

Virtual design team (VDT) is one of the architecture and engineering (A&E) design planning and control methods. Current VDT software is not data-driven like most available methods, and its simulation is based on questionnaires and on designers’ experiences. Managers may end up making decisions using limited information because there is no simple process that allows them to become familiar with all team members’ performances. There is a need to investigate the benefits of a data-driven approach that supports design process simulations by using A&E designers’ performance parameters. This study explored the performance parameters of a designers’ performance statistics, being how early they started tasks. Designers’ outputs for the same tasks can be different when they are working for different clients and managers. Designers’ performances described and extracted from data acquired from past projects allow for the customization of design simulations by changing their inputs and parameters according to specific project characteristics. The comparison between original simulations and simulations with features extracted from an existing A&E database revealed that a process simulation that is data-driven can improve the accuracy of the simulation to help better plan and control the design process.

The use of prefabricated interior panels is increasing in the construction industry because of their proven quality on projects and ability to shorten project time. However, stacking and installation sequences of the panels often do not match because stacking and installation planners generate sequences based on different criteria (e.g., transportation safety and cost, installation time-efficiency) in a separate manner. Thus, reshuffling the panels delivered on site is required to comply with the installation sequences. To develop optimal stacking and installation sequences with respect to multiple objectives (e.g., cost, duration, safety), it is fundamental to be able to evaluate reshuffling efforts for multiple sequences of stacking and installation in a timely and quantitative manner. However, current practitioners have difficulty rapidly and quantitatively evaluating those efforts because existing methods focus on minimizing the number of bunks and material handling distances. To overcome this challenge, the authors defined any additional movements as reshuffling efforts and formalized procedures to count the number of movements required to install each panel from bunks delivered on site. Using this method, the authors evaluated the reshuffling efforts for 83 bunk reports and five installation plans. The results showed that, on average, 34% additional movements were required for installation crews to complete the installation work following the installation sequence. This proposed method will enable practitioners to evaluate reshuffling efforts in a rapid and quantitative manner and help them identify the optimal stacking and installation sequences in advance. Future research will account for planners’ rationales of generating stacking and installation sequences and formalize an integrated method to assess multiple performance metrics of transportation and installation, such as costs, time, and safety.