Construction Research Congress 2018: Construction Information Technology

Construction robots operate under conditions of imperfect metrology, loose tolerances, and large workpiece uncertainties. As such, they must sense and model their workpieces to compensate for localization uncertainty and geometric variation. This research investigates methods to enable construction robots to autonomously sense and model construction workpieces so they can ultimately adapt their plans and perform work. To that end, the generalized resolution correlative scan matching (GRCSM) construction component model fitting technique is presented as a tool for fitting geometric models of construction components to point cloud sensor data. An experiment involving the sensing and modeling of a virtual construction joint was conducted. Virtual sensor data was generated and workpiece models were fit to the data using the GRCSM construction component model fitting technique. A model of the joint’s gap was then extracted from the fitted workpiece models. It was found that the GRCSM construction component model fitting technique could be used in combination with low precision sensor data to estimate the pose (i.e., position and orientation) and geometry of a construction joint’s gap with a mean modeling error of 1.6 mm in the transverse direction and 0.6 mm in the normal direction. Despite its limitations, the GRCSM construction component model fitting technique appears to be a promising tool for the geometric estimation of construction features, especially for situations involving full automation, detailed construction work, incomplete sensor data, and complex object geometry.

4D BIM is one of the most advanced methods for construction planning. Using 4D BIMs properly illustrating hourly or finer details, various pre-construction analyses can become practical and potentially automated. One of the main challenges is intensive manual effort associated with creating detailed 4D BIMs. To minimize the manual efforts, past studies introduced computational methods that automatically generate construction schedules using 3D building models (BIMs). The state-of-the-art technologies, however, could generate only rough master-level plans inadequate for practical pre-construction analyses or optimization of the 4D BIMs. Therefore, this research presents a framework that automatically generates optimal 4D BIMs. Focusing on steel erection planning, the proposed method generates, analyzes, and optimizes steel erection sequences. A prototype software program was developed, which consists of two sub-systems, which are simulator and optimizer. The simulator numerically calculates actions, movements, status, and interactions involved in steel erection in a predefined interval. Then, the optimizer with a sequential combination of A star (A*) and genetic algorithm (GA) solves the computationally intensive optimization problem.

This paper proposes a knowledge-based recommender system framework for improving BIM efficiency, with a particular focus on interior finishing. In the proposed framework, convolution neural network technique is used to analyze user-inputted images in order to identify user preferences. Design requirements and user preferences are formulated into a similarity metric, which plays an essential role in the interior finishing product recommendation. Rich information is extracted from BIM models for metric-based recommendation analysis, while the resulting recommended, in turn, can be readily implemented in the BIM model. The proposed approach is implemented as a proof of concept for interior lighting fixture selection within an Autodesk Revit environment through the application programming interface. A case study is used to demonstrate the effectiveness of the proposed system. The results indicate that users can easily and quickly retrieve their desired lighting products through the use of the prototype system.

Recent studies have focused on various unmanned aircraft system (UAS) applications in the dynamic construction work environment. However, the human performance in human-integrated system (HIS) operations has rarely been investigated in this domain. In addition, it is very challenging to evaluate the human performance because such evaluation and analysis rely on various human factors. From the perspective of theoretical human-robot interaction (HRI) and cognitive science, the situation awareness (SA) and mental work-load (MWL) of human operators have significant effects on operator’s decision-making. Either SA or MWL measurement is representatively used to evaluate the human performance instead of considering many human factors. The main purpose of this paper is to develop a framework to quantify the SA and MWL and identify the nature of knowledge (NoK) of the human operators in the construction worksite. This study also provides the overview of background information related to theoretical HRI and cognitive concepts for UAS operations. The framework consists of three main phases: (1) NoK identification; (2) field experimentation; and (3) evaluation and analysis. This paper provides deep understanding of related concepts, the overall framework including details of each step, and expected outcomes. The expected outcomes are human performance measurement, and identification of any correlations among the performance, personal background, and SA/MWL, as well as identifying considerations to improve the SA/MWL performance.

Inspection planning is a primary element of computer vision- and unoccupied aerial vehicle (UAV)-enabled construction monitoring. Prior to the on-site deployment of camera-mounted UAVs, the inspection objectives need to be identified, and optimal inspection plans must be developed; Such plans should ensure complete data acquisition and minimize the use of UAV’s limited flight time. The image capture configuration must be taken into account since it directly affects the downstream applications of the captured data such as progress detection and as-built modeling. This paper proposes a framework and a novel technique which utilizes four-dimensional (4D) building information models (BIM) and swarm intelligence to automatically generate the UAV inspection mission plans. It computationally supports both static and dynamic site layouts. The inspection objectives, their geometry, and their semantics are automatically extracted from BIM, and the corresponding elements are identified. An optimal inspection plan is developed using artificial intelligence, ensuring complete coverage of inspection targets while minimizing flight duration. The method has been tested in UAV-enabled data acquisition scenarios. It is based on the industry foundation classes (IFC), facilitating OpenBIM and reducing the costs associated with the lack of interoperability, a core challenge in information modeling. Due to the target extraction at element and sub-element levels, it supports computer vision-based construction progress monitoring and automated as-built and as-is BIM development.

Tower cranes are common equipment on building construction projects used to facilitate lifting tasks. Designers need to identify feasible alternatives that meet expectations with respect to project schedule, cost, and safety. The status quo of tower crane planning relies on 2D drawings and planners’ comprehension of site conditions to generate site-specific constraints, which would define the feasibility of tower crane plans. Consequently, the planning process lacks accuracy, efficiency, and consistency. Tower crane constraints need to be transformed to facilitate customization and implementation from one project to another, so that the planning process is consistent and the outcome is accurate. This research introduces an automated tower crane planning approach, that leverages building information modeling (BIM) and rule-based checking to realize the transition from 2D, ad-hoc planning to 3D and semi-automated planning. The feasibility of integrating rule-based design and 3D BIM modeling for tower crane planning is demonstrated, with high recall in initial tests. The capability to handle more complex problems is promising and will be tested in a follow up study.

A construction site is a dynamically changing environment, whose layout continually changes in each process phase. Failing to consider the changes in site layout can cause serious safety issues, and result in extra cost and time for the project. Site layout planning (off-site) and site monitoring (on-site) are two major research topics that help address these problems. However, the gap between available data off-site and on-site must be bridged regularly to update and re-plan the site layout as project progresses. This paper presents a real-time construction site layout and equipment monitoring system using building information modeling (BIM). Site layout and equipment data are collected and synchronized with a BIM database, from which the layout planning is then conducted. In this paper, the first part of the system, an on-site monitoring system, is developed. This system can identify an equipment on a construction site, such as an excavator or a truck, with a camera system, and utilizes a vision-based pose estimation method to determine the position and the orientation of the object. The scale-invariant feature transform (SIFT) method is used to recognize the object on the construction site. Then, the point cloud is generated from object SIFT feature points. Finally, the position and the orientation of the object is estimated from point cloud by the viewpoint feature histogram (VFH) method. Real construction site images with an excavator and a truck are used in the validation experiments. The preliminary test results showed that this method is capable of performing recognition and pose estimation in real-time; however, the accuracy of determining the correct orientation was found to be inadequate for high-precision applications, motivating the need for future work in improving the introduced pose estimation method.

Building energy compliance checking aims to check the compliance of building design information, embedded in building information models (BIMs), with applicable energy rules. To automate the compliance checking process, such design information should be automatically extracted from the BIM and matched to the energy rules. This paper proposes an information extraction method for automated extraction of design information from BIMs, and a semantic matching method for automated matching of the extracted information to the energy rules. The Revit API was used to export the design information captured in a Revit model to an industry foundation classes (IFC) data model format. EXPRESS-based information extraction methods and Java-based post-processing techniques were used to parse the exported IFC data model and extract the design information to an intermediate semantic representation. A semantic matching method was developed and used to select the design information that matches/relates to the energy rules. The algorithms for extraction and matching of design information to energy rules were tested and evaluated using the BIM of an educational building and a set of rules extracted from energy codes. A performance of 98.0% recall and 89.2% precision was achieved in matching, which shows that the proposed methods are effective in automatically extracting design information from BIMs and matching the information to energy rules.

Automated code compliance checking has been implemented using a variety of different methods. These methods vary widely in terms of automation level, expressivity, representativeness, accuracy, and efficiency. Although most of the existing methods/tools are similar in that they represent the code requirements in the form of rules, the semantic representation and language/format they use for rule representation vary from a method/tool to another. One common limitation of all these methods/tools is the inability to fully represent all types of requirements (e.g., requirements that are too complex or that require human judgment by nature). More research is, thus, needed to better identify what the different types of requirements are—in terms of their semantics and syntactics—and which of them could be represented in a computer-processable form. To address this need, this paper proposes a new methodology for identifying the different types of building code requirements in terms of computability. The proposed methodology consists of three primary tasks: (1) use a machine-learning clustering algorithm to identify the computational clusters of the code requirements, (2) identify the different types of requirements in the different clusters, in terms of their semantic and syntactic features and structures, complexity, etc., and (3) determine the computability of different sentence types. The proposed methodology was implemented and tested on a corpus of sentences from the 2015 International Building Code. The paper discusses the clustering and analysis methodology and results.

The use of industry foundation classes (IFC) data can facilitate interoperability of building information modeling (BIM) among different applications to alleviate the problems of information missing and inconsistency. By virtue of its goodwill of transparency and openness, IFC data can be opened and viewed in any text editor. But it normally requires a significant amount of effort when manually interpreting IFC data, due to (1) its large number of entities; and (2) the complex connections between one entity and another. On the other hand, the explanations of IFC entities in the IFC schema specifications are difficult to understand or verify. To address such difficulties, in this paper, an empirical data-driven approach is proposed for achieving a systematic understanding of entity definitions in an IFC schema. The approach utilizes IFC data and schema in a synergistic way, to facilitate such systematic understanding. Experimental testing is used to serve as verifications of the understanding and accrue the understanding, along with which byproduct BIM tools will be developed. The proposed approach was tested on understanding entities for geometric representations in the IFC2X3_TC1 schema. Through the experimental testing, systematic understanding of 62 IFC entities were obtained, and a visualization algorithm was developed and implemented based on this understanding.

For more than a decade, unmanned aerial vehicles (UAVs) have been used on construction job sites for a variety of purposes including structural inspection, and 3D mapping of the site and existing structures. Although the advantages that UAVs have brought to construction are discussed extensively in the literature, applications of other types of unmanned vehicles (UVs) in the construction industry, including unmanned ground vehicles (UGVs), unmanned surface vehicles (USVs) (operating on water surfaces), and unmanned underwater vehicles (UUVs), have received limited attention. Although UAVs are the most frequently employed type of UVs in the construction industry, UGVs and USVs have also been involved in a few construction-related missions. These include search operations and structural damage assessment in the aftermath of disasters such as the World Trade Center in 2001, Fukushima Daiichi nuclear power plant in 2011, Hurricane Wilma in 2005 and Hurricane Ike in 2008. UGVs and USVs have rarely been used in any other type of construction-related application. Furthermore, there is no available data about construction-related applications of UUVs. Recent advances in information technology and artificial intelligence have led to the design of autonomous UAVs, UGVs, USVs, and UUVs that can operate without, or with little, human intervention in their missions. Advances in UVs and their automated control have resulted in commercially available prototypes of different types of UVs. This, in turn, has created an unexplored potential for the applications of a wide variety of UVs to construction. This paper reviews this potential specifically for USVs, UUVs, and UGVs. The details of each mission, including application, UVs type and details of mission are described. The paper concludes with an identification of gaps in the current technology where developments are required before the full potential of USVs, UUVs, and UGVs can be achieved in construction.

Unmanned surface vehicles (USVs) are remotely-operated robotic agents that can safely deploy and maneuver on the surface of bodies of water. Although, there is an established body of literature on large, complex and expensive USVs in structural health monitoring, comparing the solutions provided with the current available UAVs and their scale, cost, and applications, shows that USVs are not yet developed at this scale. The goal of this study is to provide a proof of concept for applications and development of USV devices (small, low cost, and versatile) similar to the current available UAVs. These devices can be used for monitoring the construction progress, facility management or in other words it can basically stretch over the lifecycle of the facilities. This paper first presents a discussion on different adaptations of the UVs and their definitions and applications. This is followed by a literature review on different health monitoring algorithms and methods to establish the body of knowledge in the area of automation of the health monitoring. This includes some related examples of small, low cost UAVs that are used to automate the health monitoring system. These are reviewed briefly to show how such systems can provide the basis for development of a similar USV. Next, deployment and use of various USVs is discussed. To provide a proof of concept for such a USV device an experiment is conducted with a small-scale commercial USV mounted with a camera to visually assess the health of a retention pond in Gainesville, Florida. The paper concludes with a discussion of the feasibility of the device and testing the possibility of automating the health assessment process using image-processing techniques.

Applications of neuroscience are leading to advances in both economics and architecture. For example, the neural correlates of decision making in bargaining games can predict trust among partners. A growing field of study in neuro-architecture is helping measure how the design of urban spaces can improve cortical function. The intent of this paper is to communicate details about the benefits of integrating neuroscience to engineering and project management research and to explain what is involved in adopting these methods and the procedures. The benefits are numerous: offering a new data source, opportunity to advance theoretical understanding, and visualization of the cognitive processing that produce judgment and decisions. These benefits seem to outweigh the learning curve associated with the measurement tools and nuances of the experimental design. There are several instruments such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI). Another is functional near-infrared spectroscopy (fNIRS), which blends the benefits of both EEG and fMIR by providing better spatial resolution and a more realistic environment for data collection. In a pilot study, students were given ten engineering challenges on the most pressing issues facing humanity. The problems spanned topics such as renewable energy, water quality, poverty, and air pollution. Five of the ten challenges included an additional parameter about sustainability. When generating solutions that required meeting the additional parameter, a decrease occurred in area of the brain known to be involved in working memory, cognitive flexibility, planning, and abstract reasoning. Conceptually, these results provide insight into the neural mechanisms that support and facilitate engineering solution generation but also have real world implications. When writing or responding to an engineering request for proposal, when and how parameters are presented may elicit very distinct responses. Suggested areas of future research are also presented including opportunities to apply neuroscience methods in design cognition and hazard detection.

The operation and management of today’s complex built environments pose several challenges for building managers, who increasingly rely on location-based physical and information services for inspection and maintenance tasks. Recent advances in computing and cognitive capabilities enable the possibility of deploying robots for several operation and management tasks in the built environment. Potential examples of such coexisting built environment robot functions include indoor equipment delivery, environmental monitoring, inventory management, scheduled inspection, surveillance, wayfinding and search, and rescue. Coordination among robots deployed in indoor environments relies on the efficiency of task allocation and route planning. Previous studies have attempted to adapt existing algorithms used in outdoor logistics applications (such as genetic algorithm, Tabu search, and integer linear programming) and modify them with context-specific assumptions (e.g., existence of a unique Hamiltonian tour in the optimal solution). This often leads to suboptimal or infeasible solutions. For example, to visit a set of nodes in a graph network based on indoor environment, a Hamiltonian tour might not be possible. This paper addresses these issues and proposes a domain-independent framework to solve the multi-robot task allocation and route planning problems for centrally-located service robots charged with spatiotemporal tasks in indoor built environments. Scenario analysis is conducted to compare the performance of existing algorithms with the developed approach for route planning in building service robots. The results demonstrate the feasibility of the proposed approach in a range of applications involving constraints on both the environment (e.g., path obstructions) as well as robot capabilities (e.g., maximum travel distance on a single charge).

With the rapid advancement of laser scanning and photogrammetry technologies, geometric data collection at construction sites by contractors has been increased to improve constructability, productivity, and onsite safety. Especially, the latest laser scanning technology provides faster scanning-speed, longer ranges, and higher accuracy and resolutions. However, the conventional static laser scanning method suffers from operational limitations due to the presence of many occlusions commonly found in a typical construction site. Full scanning without information loss requires that the scanning location should be changed several times, which also leads to extra work for registering each scanned point cloud. Alternatively, this paper presents an autonomous mobile robot which navigates a construction site and continuously updates a progress of 3D scanning with point clouds. This mobile robot system uses the 3D simultaneous localization and mapping (SLAM) technique to determine its navigation paths in an unknown environment, capture the survey-quality RGB mapped point cloud data, and automatically register the scans for geometric reconstruction of a construction site. The performance of the overall system was tested in indoor environments and validated with promising results.

The current lifecycle management of construction projects has long been plagued by the dilemma of “lack of information” and “information explosion”. This paper aims to develop a closed-loop lifecycle management system that can enable a consistent stream of information for use and reuse of all stakeholders. The framework integrates the state-of-the-art smart construction, construction automation and internet of things (IoT) technologies to help fragmented practitioners access and manage the information via a standardized interface among various applications throughout the whole lifecycle. Within the closed-loop lifecycle management system for IoT-based smart construction, digital technologies such as 3D laser scanner, drone, building information modelling (BIM), augmented reality (AR), Auto-ID, global positioning system (GPS), wireless sensor network (WSN), robotics, mobile digital devices, and web-based applications are used to collect proactive data from different stages. Further, the data are stored, shared, processed, and utilized in one unified platform for all stakeholders to support better decision-making and interaction throughout a project lifecycle. The framework is a guideline of real-time visualized tracking and operation, real-time environment supervision, real-time feedback and alarm, cloud backup, and data analysis demonstration which enable smart construction from head to toe.

With its mobility and high visual data acquisition capability, drones can be used to capture videos for monitoring progress and safety for a variety of construction processes. By reconstructing three-dimensional (3D) point clouds from video images, drones can play an important role for quantitative analysis of productivity and safety-related metrics, especially in situations where construction worksites are rapidly evolving in response to construction operations. In this paper, we explore the usage of a drone to obtain videos of an excavation project in order to monitor slope stability at different stages of the operation. First, the drone is flown over the construction site to collect videos of the ongoing excavation. Then, the structure from motion analysis is performed on the video images to obtain dense 3D point clouds. The terrain points are separated from non-terrain points by detecting elevation changes and clustering smooth surfaces. Based on the terrain points, the surfaces of interest are extracted and their plane parameters are computed in order to estimate the slope stability of the evolving excavation. In addition, the digital elevation models (DEM) and the corresponding slope maps are generated from the terrain points to visually depict the progress of the excavation. Experimental results demonstrate the feasibility of the proposed method and show that a texture-rich 3D model of an excavation can be constructed from drone imagery and subsequently used for quantitatively evaluating slope stability and safety.

In the past decade, an increasing number of construction projects have utilized building information modeling (BIM) for their success. Works of various scholars in the past decade have shed the light on the application potential for BIM through aggregating the results of multiple case studies showing how BIM (or 3D/4D modeling) can be applied to address project challenges. This paper aggregates the results of a more recent set of 28 case studies to assess how the use of BIM applications to address different challenges in construction projects has evolved in the past 10 years, replicating the methodology used in a 2008 study. The main findings of this analysis are that practitioners, on average, have used 3D/4D models for four application areas, as opposed to only one to two application areas from the original 2008 study. The paper suggests developing more updated analysis framework to cope with the expanding 3D/4D modeling applications, as well as tackling the psychological barriers that hinder further 3D/4D modeling implementation.

While problem-based learning can enhance the critical skills for resolving common issues on building information modeling (BIM) projects, there is limited evidence suggesting educators are using the pedagogy to inject common BIM issues in classrooms. Therefore, this study develops a problem-based learning activity that inserts common issues on BIM-based construction projects to develop the critical skills for resolving them. Specifically, it answers the following questions: (1) How can a problem-based learning module be developed to incorporate real-world challenges that are common on BIM-based construction projects? and (2) How can this learning module be implemented to assess the impacts on students? The authors answer the first question by identifying guidelines for problem-based learning, gathering information on BIM issues, developing content using the information, and modifying the content with the help of industry professionals. To answer the second question, the authors select assessment strategies, gather assessments from prior studies, remove overlapping and unrelated assessments, and modify the assessments to fit the module. The findings suggest that the methodology can assist educators in developing content for learning modules from industry-generated data that is not intended for educational purposes. Additionally, educators should focus on adopting assessments from prior studies when evaluating a module’s impact.

Construction-related professionals are required to interact constantly on a variety of complex spatiotemporal occasions with several groups of people (e.g., architects, engineers, other construction professionals). The efficiency and effectiveness of the construction process strongly depends on the successful communication of information between all the functional groups involved in the different contexts of construction projects. However, lack of exposure to construction processes and construction professionals is widely observed in construction graduates. This lack of exposure results in deficient understanding of construction domain concepts in relation to real world problems. The use of intelligent virtual agents powered by building information modeling (BIM)-based virtual environments provides opportunities to incorporate conversational practices into classroom teaching. By employing these virtual interactions, a consequence-free environment that is controllable, representative, and repeatable can be achieved, with the benefit of providing constant real-time feedback to students. In such digital settings, students can observe spatiotemporal dependent occasions and communicate with other virtual professionals to obtain a better understanding of the construction events. In this paper, the technical workflow to create this virtual communication platform will be described in detail. This workflow includes (1) procedure of generating and authoring a virtual agent with its corresponding layers of information, (2) the creation process of the virtual environment that corresponds the conversational contexts using BIM-based technologies, and (3) the integration of the intelligent virtual agent and the digital site into a working platform. A case study is used to illustrate the conversational practice platform workflow for a high-risk caught-in or -between hazard scenario.

Building information modelling (BIM) objects are the primary “building blocks” that form a building information model to store, organize, and visualize project information. “One size does not fit all”; BIM objects, along with their associated information, vary significantly from one construction context to another. In parallel with the deepening of BIM globalization is the increasing emphasis of “BIM localization,” whereby designers desire a context-specific BIM object library that is in accord with the local building specifications and construction needs. Prevailing practice is to manually make the BIM objects and organize them in a library. This practice, however, could be inefficient, tedious and error-prone, particularly when the task is to deal with millions of objects in real-life projects. This study aims to investigate the heterogeneity of BIM objects, namely the variations of BIM objects in different construction contexts. This is the first and foremost step to design the context-specific BIM libraries using automatic or semi-automatic approaches. It first selects the critical parameters that distinguish a general BIM object. Taking these parameters as comparison criteria, the study further summarized the variations of BIM objects in different construction contexts. The study outcomes could enable efficient BIM design and promote BIM implementation in real-life projects. It can also contribute to the existing knowledge of BIM objects and offer significant implications to soft-landing BIM-based building project delivery.

Laser-scanned point clouds contain rich geometric information and are one of the primary modes of data acquired from as-built infrastructure. The creation of a building information model (BIM) from scanned data has several important applications such as construction progress tracking, asset management, structural health monitoring, quality assessment, and safety planning. A conventional scan-to-BIM pipeline typically involves a high volume of manual work to extract and label building elements which is time-consuming and error-prone. This research proposes a voxel grid search method to automatically generate region proposals for objects of interest from a laser-scanned point cloud. The advantages of the proposed method are that it does not require prior segmentation of objects and that it is agnostic with respect to the shape of objects that can be detected. A point cloud template is first created from a computer-aided design (CAD) model by randomly sampling points along the surfaces of the model. Next, a voxel grid data structure is used to represent the occupancy of both the template and the scene point cloud. A search procedure is used to determine candidate regions with high region proposal scores, and thresholding and mean shift clustering are used to refine the region proposal results. The proposed method was evaluated with a laser-scanned point cloud of a building, where a ground truth BIM model is available. Region proposals were generated for five different building elements (beam, column, roof, stairs, and window frame) and compared to the ground truth model using the metrics of precision, recall, and centroid error. Test results showed that the method achieved 0.04–0.50 for precision, 0.50–1.00 for recall, and 0.99–1.82 m for RMSE.

Specialty trade contractors are increasingly using building information models (BIM) and other virtual design and construction (VDC) tools to deliver their projects more efficiently. Trade contractors are prone to VDC performance deterioration and cost overruns due to the lack of true integrated project delivery and their heavy investment in BIM/VDC tools and processes. This paper summarizes the finding of a study to investigate current experiences and best practices of electrical contractors of executing and controlling the costs of BIM and VDC tasks under design changes. The study included two main objectives: (1) examine the current industry experience of performing BIM and VDC tasks under design changes; and (2) identify best practices to mitigate, track and communicate the cost impact of VDC changes. The study depended on two main data collection tasks: online questionnaire and interviews. Sixty-four percent of the questionnaire respondents reported experiencing VDC cost overruns in more than 10% of their projects, and more than 2/3 of them did not recover such cost overruns. Best practices of managing and recovering these cost overruns were suggested based on the interviews and the questionnaire findings.

Virtual design and construction (VDC) is one of the very promising forefronts for improving the construction industry, which involves design assistance, building information modeling (BIM), design for prefabrication, constructability reviews, and automated field layout and conditions assessment. Specialty trade contractors have invested heavily in VDC-related tasks in the form of hiring/training highly-paid qualified professionals and purchasing expensive equipment and software licenses. Contractors’ VDC commitment should also account for the front-loading of project’s effort that results from the required additional early design and planning decisions made by the contractor. Due to this significant time and cost investment in VDC, contractors need to adopt new planning and control methods to: (1) have an accurate estimate of the time and budget required for VDC considering specific project requirements; and (2) monitor, assess, and report the performance of VDC tasks and promptly identify and recover any cost/time variations. Accordingly, this paper presents a multi-case-based (MCB) evidence for the feasibility of developing a VDC planning and control framework for electrical and trade contractors. Six cases of electrical construction projects were collected to analyze their VDC hours, project delivery attributes, timeline, and BIM implementation satisfaction level. There was a correlation between the project’s normalized BIM-related hours and the reported contractor’s satisfaction with VDC implementation. The paper is concluded by proposing a conceptual planning and control framework for VDC work to help to avoid potential performance problems and cost overruns.

As the architecture and construction industries have increasing project requirements and complex processes, a consistent data storing, federating, and sharing method has become critical. However, the establishment of a robust platform for sharing design and construction data has been overlooked. To improve the current practice, this research study adopts a new data archive technology to federate and share the sound data of construction work activities and equipment operations. As the technology of sound recognition and identification has increasingly developed, the construction industry has recognized the significant implications of a sound data analysis of construction work activities and equipment operations. To establish a sustainable research foundation regarding a construction sound analysis and adopt a state-of-the-art sound analytics technology to the construction industry, this paper involves the investigation of a method to federate the distributed web data of construction activity sounds in a data archive and categorize them according to types of sounds and events. As an archive, the DSpace is used to collect the cleaned construction sound data from web and share them with the public. Establishing a sustainable and durable data archive containing sound data is expected to provide valuable and sustainable data assets for researchers and industry professionals to conduct construction sound-related research such as audio-based construction field monitoring.

A building design must satisfy the requirements from diverse sources such as a building code, owner’s specifications, design guidelines, and project requirements. Such requirements entail various types of rules and execution plans. In addition, the increasing complexity and requirements of a building design have required various rule-based checking platforms and capabilities. To explore a new rule-checking method, this paper proposes a node-based scripting approach with integrated speech recognition to define checking rules and evaluate the quality of BIM models for its completeness. The node-based modeling feature and its rule-based validation have a potential to define the rule components units as nodes, to offer flexibility and usability with multiple parameters, to extend rules as nodes, and to manage a large number of rules attached to a BIM design. To achieve this goal, graphical scripting, Marionette in Vectorworks, has been explored as a modular-based rule-checking approach. According to the rule types required during the early design phase, which is the work scope of this research study, the nodes were categorized by the four distinct types: object selection, rule definition, rule implementation, and validation reporting. In addition, to explore the new feature integrated with Marionette, this research study involves the investigation and development of voice recognition-based rule checking. The development and implementation of node-based rule checking features show significant potentials to provide flexible and extensible design checking rules as a form of a module.

The benefits of building information modeling (BIM) have been recognized by many construction companies over past years. Due to daily advancement in BIM, users need to improve their BIM skills and learn its recent developments and changes to earn more benefits for their companies. Currently, BIM-based research mainly focuses on how to improve the short-term benefits at a project level and their majorities are concerned on measuring the benefits of BIM through making a comparison between projects implemented with BIM and those without BIM implementation. However, such research has not well discussed the appropriate investment and training strategy of BIM implementation at firm’s perspectives. In response, this paper develops a BIM maturity prediction (BMP) model that predicts the learning trend of a BIM model for a specific organization based on the current resources and training strategies of the organization. The results of implementing of the developed model on a case study show the effectiveness of the model to predict the learning trend. This model benefits the BIM domain by helping managers understand the current and future BIM maturity level of their teams.

Generation of as-built building information is a growing necessity for retrofitting and renovating existing facilities. As building information modeling (BIM) is increasingly adopted in the construction industry, the development of information-driven models is becoming a standard for existing buildings, even before the design phase starts. Additionally, it is essential to evaluate and review the generated information before the design can begin. Therefore, the goal of this paper is to provide an exploratory two-step study (i.e., process mapping followed by a proof-of-concept experiment) on the implementation and assessment of visualization tools. These tools were implemented for the design review in the generation process of as-built information. To achieve this goal, four residential retrofit projects that required the production of as-built information were selected. In the first step, it was identified, within the generation process, where designers and surveyors typically review information and drawings. This step was achieved by process mapping of the building information flow for a typical residential project. Then, visualization tools were implemented for two of the projects, while the other two served as a control. In the second step, the records for requests for information (RFI) issued by the engineers for each of the projects were recorded. Based on the results, the projects that included visualization tools not only did have fewer RFIs, such as the location of building elements. The paper provides a unique perspective on implementation of visualization technology for the design review of as-built information. This study adds to the existing body of knowledge related to visualization in design reviews, which has been leveraged mainly in new construction projects.

A large portion of the expenses in a construction project are allocated towards the capital and operating costs of heavy equipment. Most of construction heavy equipment and tools carry out activities in the form of repetitive cycles (e.g., a cycle of digging, swinging, loading). Precisely estimating cycle times for those operations is a crucial step toward productivity analysis, cost estimation, and scheduling of a construction project. The traditional approaches for estimating cycle times of construction cyclic activities are twofold: (1) based on direct observations and recordings; and (2) using available graphs and approximate formulas for estimations. The first approach is time consuming and labor intensive and the second one might not be sufficiently accurate and realistic. To tackle the above-mentioned issues, this paper proposes an automated, Bayesian system for estimating cycle times of construction heavy equipment. Considering that construction equipment usually produces distinct acoustic patterns while performing various tasks, the main input for the system is recorded audio data. The presented system includes a de-noising algorithm for enhancing the quality of audio data as well as a short-time Fourier transform (STFT) and support vector machines (SVM) for classifying various activities in a primary stage. A Markov chain model for activity transitions is calculated from ground truth data and used to code an adaptive filter that converts SVM-labeled time-frequency bins into higher-level labels of the full period for each activity. Preliminary results show that, through this system, the accuracy of predicting cycle times could be as high as 90%.

3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection, and post-disaster rescue. Comparing to other sensors, RGB image has the advantages of low-cost, texture rich, and easy to implement that has been used as the primary method for 3D reconstruction in construction industry. However, the image-based 3D reconstruction always requires extended time to acquire and/or to process the image data, which limits its application on time critical projects. Recent progress in visual simultaneous localization and mapping (SLAM) make it possible to reconstruct a 3D map of construction site in real-time. Integrated with unmanned aerial vehicle (UAV), the obstacles areas that are inaccessible for the ground equipment can also be sensed. Despite these advantages of visual SLAM and UAV, until now, such technique has not been fully investigated on construction site. Therefore, the objective of this research is to present a pilot study of using visual SLAM and UAV for real-time construction site reconstruction. The system architecture and the experimental setup are introduced, and the preliminary results and the potential applications using visual SLAM and UAV on construction site are discussed.

In the U.S., buildings account for a large portion of the total electricity consumption, which gives pressure on the national grid during peak hours. Demand response (DR) programs are attractive options to alleviate the pressure by incentivizing owners to reduce electricity usage during event times. Portfolio owners, who are willing to sign up for DR programs, need to work with third aggregator companies to generate customized curtailment protocols and determine the capacity of electricity reduction per building. These protocols provide alternative operations for buildings during the event with the aim to save electricity up to the capacity being enrolled. One of the challenges is that on-site facility managers (FMs) or off-site operators, who are responsible for executing protocols, struggle to collect accurate information of space occupancies and components, which results in compromises in electricity conservation and discomfort for occupants. Building information models (BIMs), which contain integrated facility information, can be leveraged to execute the curtailment protocols efficiently. Given these problems, the objectives are to first identify typically required facility information in managing DR events and then evaluate the capabilities of BIMs in providing such information in DR with the vision to control such actions from a central location. This paper presents the results of a case analysis on 20 campus buildings that are enrolled in DR programs. We extracted the essential information from the curtailment protocols and examined the possibility of retrieving such information in BIMs by analyzing industry foundation classes (IFC). The initial results show that BIMs can provide DR related information such as type and location of HVAC and lighting components, zoning details, and locations. The analysis results will serve as a foundational work for BIM and building automation system integration to aid DR events from a central location for efficiency instead of actions taken by FMs on-site.

The construction industry has increasingly adopted building information modeling (BIM) in design and construction for its numerous benefits. Recently, technological advancements have allowed the visualization of the 3D BIM content on construction sites using smart phones, tablets, portable computers, and site computer kiosks. Furthermore, researchers have experimented with using virtual reality (VR) and mixed reality (MR) to visualize the design overlaid on existing features in its final intended location on site, which allows for a more immersive viewing environment. Using MR in conjunction with head mounted displays (HMD), the user can experience a hands-free visualization of the model, which in turn expands the capabilities of workers on site. However, two of the major problems facing the MR visualization on site are the stabilization of a full-scale model contents and the accuracy of the location on site. Providing an accurately sized and placed model on site offers several advantages: for example, quality assurance (QA) and quality control (QC) processes, where quick and easy comparison between the built project and the plans can be achieved. This paper presents a workflow that allows the projection of full-scale MR content in its correct final location by leveraging marker based MR and existing survey points that are typically found on construction sites. The workflow was validated through implementation on a large construction site in Arizona as an efficient and accurate means of QA/QC. Future work will aim to further streamline and automate this process to get the model from its native BIM software to the desired MR device more seamlessly.

The automated integration of as-built and as-is conditions into building information models (BIM) remains a primary challenge for unoccupied aerial vehicles (UAV)-enabled facility and construction inspection. Due to the lack of site-to-BIM data pipelines supporting reality capture technologies such as camera-mounted UAVs, BIMs lose their effectiveness over time since they do not accurately reflect the as-built and as-is conditions; the UAV-captured visual data also remains underutilized. This paper proposes and demonstrates a novel industry foundation classes (IFC)-based solution for UAV-enabled as-built and as-is BIM development, quality control, and smart inspections. It first identifies the requirements of UAV-enabled site-to-BIM automation and its framework; then, it elaborates on the proposed solution which aims to: (1) enable automated conformance checking and quality control using data queried from BIMs and UAV-captured reality in the form of images; and more importantly; and (2) enable the integration of on-site captured reality, including the as-built and as-is conditions, into BIMs. The method leverages the non-proprietary IFC schema, empowering OpenBIM applications, and facilitating interoperability, a core challenge in the information modeling domain. In addition to its support for UAV-enabled applications, the technique can provide the same site-to-BIM functionalities for other types of reality capture and other robotic data collection efforts during construction, commissioning, and facility management.

Sequence alignment (SA) is a well-established technique in bioinformatics for analyzing deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or protein sequences and identifying regions of similarity. The main goal of SA is to discover relationships between strings of data by deploying a series of heuristic or probabilistic methods to align a new string (e.g., DNA of a new species) with an existing string (DNA of a known species). SA has also been used sporadically in linguistics, social sciences, and finance. In this paper, the authors explore the prospect of coupling machine learning (ML) and SA to improve the output of human activity recognition (HAR) methods. In particular, several field experiments are conducted to collect heterogeneous human motion data via wearable sensors. Collected data is further mined using ML to identify sequences of activities performed in each experiment. Given the inaccuracy of sensor readings and the limitations of ML algorithms especially in handling datasets from complex human activities such as those performed by construction workers, it is expected that the resulting activity sequences not fully match actual activity sequences as observed in the field. To further clean up this inherent noise, SA is deployed to refine imperfections in the resulting activity sequences by manipulating the output of HAR and ultimately aligning noisy activity sequences with ground truth sequences. The outcome of this work is a systematic method to improve the reliability of HAR from sensor readings, which can benefit decision-making as related to task planning, resource management, productivity monitoring, and ergonomic assessment.

The sound recognition technology, which has been adopted in diverse disciplines, has not received much attention in the construction industry. Since each working and operation activity on a construction site generates its distinct sound, its identification provides imperative information regarding work processes, task performance, and safety relevant issues. Thus, the accurate analysis of construction sound data is vital for construction project participants to monitor project procedures, make data-driven decisions, and evaluate task productivities. To accomplish this objective, this paper investigates the sound recognition technology for construction activity identification and task performance analyses. For sound identification, Mel-frequency cepstral coefficients are extracted as the features of the six types of sound data. In addition, a supervised machine learning algorithm called Hidden Markov Model is used to perform sound classification. The research findings show that the maximum classification accuracy is 94.3% achieved by a 3-state HMM. This accuracy of the adopted technique is expected to reliably execute the construction sound recognition, which significantly leverage construction monitoring, performance evaluation, and safety surveillance approaches.

Poor work conditions and excessive physical demands associated with construction tasks increase the risks associated with construction workers’ impaired mental and physical health. To prevent detrimental impacts that such risks have on project performance, measuring construction workers’ physical and mental status should take precedence. In the same vein, recent advancements in wearable technologies and physiology have opened a new door toward an objective, nonintrusive, and continuous field measurement of workers’ physical and mental status using physiological signals acquired from wearable devices. Despite this opportunity, there is a notable paucity of studies investigating the potential of using physiological signals collected from wearable devices to understand workers’ physical and mental status. To bridge this gap, this study suggests utilizing an off-the-shelf wristband-type wearable sensor to acquire construction workers’ physiological signals as a means of assessing workers’ physical and mental state. This study investigates the distinguishing power of three biosignals—electrodermal (EDA), skin temperature (ST), and photoplethysmogram (PPG)—in detecting workers’ physical and mental states during their work on the site. The 10 h of biosignals were acquired from two workers in different conditions (e.g., light work, medium work) and tasks (e.g., resting, installing drywall, etc.). Signal processing methods (e.g., filtering methods) were applied to remove the signal artifacts from the biosignals. Electrodermal response (EDR), mean ST values after finite impulse responses filtering, heart rate variability (HRV), inter-beat interval (IBI), and percentage heart rate (%HRR) were calculated as the metrics to investigate the potential of suggested biosignals in measuring workers’ physical and mental states. Results indicated a clear difference in EDA values, HRV, and %HRR values while subjects were working in various real work conditions. Results confirm the feasibility of the wristband-type wearable sensor to evaluate construction workers’ physical and mental states, which can lead to early detection of the stressor factors in construction sites.

Maintenance and repair are important tasks in building operations; how fast, easy and economic these activities are carried out directly affects the amount of resources (i.e., time and money) to be spent. This impacts a building’s life cycle cost and its level of service. Improving maintenance efficiency can provide notable benefits. However, in the current practice the maintainability is not fully considered during the design phase. This has led to many issues, such as the lack of accessibility to some building components, which threats the effective performance of maintainability tasks in terms of safety, time, and cost. In response, this research aims to develop methods to support the assessment of a building design through integrating game simulation, virtual reality, and brain sensing technologies. Light fixture is selected to be the focus. Bulb placement is a common maintenance activity that can be performed by most individuals without any expert knowledge/skills; but there is a high safety risk if the right procedure is not followed. In this study, three different light design alternatives (i.e., wall-mounted, hung bulb, and ceiling lamp) were created and tested by 13 volunteers in an immersive virtual environment (IVE) using game simulation. In addition, participants’ brain waves were recorded during this simulation through an EEG brain sensing device to assess their emotions. The main task to perform was to place the bulb in the different designs/configurations. A survey was conducted afterwards to collect data about their subjective judgement of comfort, safety, and preferences. The data was analyzed to compare different designs from the user’s perspective based on their emotional states; the result provided an insight into the maintainability of building light fixtures at the design phase.

Deviations from planned schedules in construction projects frequently lead to unexpected financial disadvantages. However, early assessments of delays or accelerations during the construction phase enables the adjustment of succeeding tasks and their dependencies. These assessments involve human resources and as-built information that may not be immediately available. To reduce manual processes of collecting and generating as-built information, this paper presents the development and validation of a new pipeline of localizing a camera to a BIM by detecting and matching perspectives of images and the BIM. The proposed method increases the degree of automation of construction progress monitoring. The novel method registers as-built video data on as-planned BIM data in real-time using computer vision algorithms. The results demonstrate the improved registration errors compared to that of the manual correspondence selection and the potential for automating the initial manual steps of data collection and registration of the past studies that attempted to automate progress detection and quality assessment.

Nowadays, to assess and document construction and building performance, large amount of visual data are captured and stored through camera equipped platforms such as wearable cameras, unmanned aerial/ground vehicles, and smart phones. However, due to the nonstop fashion in recording such visual data, not all of the frames in captured consecutive footages are intentionally taken, and thus not every frame is worthy of being processed for construction and building performance analysis. Since many frames will simply have non-construction related contents, before processing the visual data, the content of each recorded frame should be manually investigated depending on the association with the goal of the visual assessment. To address such challenges, this paper aims to automatically filter construction big visual data that requires no human annotations. To overcome challenges in pure discriminative approach using manually labeled images, we construct a generative model with unlabeled visual dataset, and use it to find construction-related frames in big visual dataset from jobsites. First, through composition-based snap point detection together with domain adaptation, we filter and remove most of accidently recorded frames in the footage. Then, we create discriminative classifier trained with visual data from jobsites to eliminate non-construction related images. To evaluate the reliability of the proposed method, we have obtained the ground truth based on human judgment for each photo in our testing dataset. Despite learning without any explicit labels, the proposed method shows a reasonable practical range of accuracy, which generally outperforms prior snap point detection. Through the case studies, the fidelity of the algorithm is discussed in detail. By being able to focus on selective visual data, practitioners will spend less time on browsing large amounts of visual data; rather spend more time on looking at how to leverage the visual data to facilitate decision-makings in built environments.

To date, indoor navigation systems have been utilized in various areas, including optimal route planning, user tracking and exploration, and voice-based navigation along a route. To overcome the limitations of the global positioning system (GPS) in indoor environments, various indoor navigation systems have been developed using wi-fi, bluetooth, ultra-wideband (UWB), and radio-frequency identification (RFID). However, all these systems require the installation of additional devices to communicate with sensors. The popularity of smart devices, which have built-in sensors to track user movements, affords potential indoor navigation systems without requiring the installation of additional devices. This paper presents a framework for a three-dimensional (3D) building information modeling (BIM) and smart device-based indoor navigation system for construction management. A matching and interacting mechanism between the 3D BIM and the camera view that uses the smart device’s built-in sensors is proposed to navigate indoor environments. The proposed framework is expected to be utilized in various construction projects and tasks.

A good level of understanding of design specifications by all stakeholders is the initial and a fundamental step towards effective communication in a design review setting. Certainly, one of the goals of designers during the review process is to ensure the design solutions are well understood. Recently, virtual reality (VR) systems have been adopted in an effort to improve this shared understanding of ongoing designs. However, the effectiveness and suitability of such systems to design review practices has not been rigorously tested yet. In this context, this paper describes a systematic usability study consisting of a mix of empirical and inspection-based methods (user testing and heuristics evaluation) that aimed to investigate the implications and usability issues from the adoption of an immersive virtual reality system in design review. The usability testing comprised the evaluation of three main aspects from the user standpoint: users’ cognitive performance, users’ interaction performance, and the system suitability to design review practices. The experiment consisted of expert designers performing tasks within the virtual environment and then responding to objective and subjective questions about their understanding of the virtual representation, their overall experience using the platform, and their personal assessment on heuristics regarding the system’s user interface. The usability evaluation provided evidence that the immersive virtual reality system is indeed efficient in communicating design solutions, which could improve communication quality during design review meetings. However, the heuristics evaluation (alone) revealed that the system has significant usability flaws and still requires improvements in its user interface. Nevertheless, since issues were rated as “minor usability problems,” improvements to the system are very likely. In light of the evaluation findings, the last part of this article discusses the extent to which the immersive virtual reality system could benefit the decision-making in design review, and provides some insights on potential improvements.

Timely implementation of construction works (at the scheduled time) is crucial for project success, but a delay is common in practical projects. Virtual reality (VR) and augmented reality (AR) have been explored to improve construction efficiency and assist project management. However, a comprehensive analysis of VR/AR application in assisting schedule control is absent. This study fills this gap by investigating existing studies, application field and challenges, and suggest some future direction. Firstly, prevalent schedules risks are identified. Then, applications of VR/AR in assisting schedule control are summarized regarding application field. Application barriers and future direction are discussed at last.

This research builds on prior work by the authors and aims at developing best practices to define and transfer asset data captured during design and construction to the owner at handover using various BIM tools and platforms. With the goal of developing BIM standards and guidelines for supporting facility management at a large educational institution, work completed under prior research included a data needs analysis and definition phase to identify the properties and attributes of various tracked assets by the owner in support of facility management activities. A specially formatted spreadsheet was created that identified the various types of data that would need to be included in a spreadsheet-based deliverable option for the data turnover process. This paper addresses a model-based deliverable option and identifies various data transfer workflows to exchange asset data with a facility management system. The research uses a case study to describe various workflows available to transfer the data. The case study project involves a $1.7 million (USD) HVAC, lighting, and exterior window upgrade of a 1953 Occupancy Class Group A laboratory and office building at the educational institution. Data collection and loading into the Revit BIM model is briefly described, as it is not the main focus of this paper. A 3rd party software plugin was used to define parameters in Revit and load asset data collected from as-built project 2D plans and approved submittals. The paper reports on the data transfer workflows identified and analyzed, and summarizes preliminary observations of the challenges and benefits of each and how they support the owner’s information needs. Finally, the paper includes a discussion of how the current study has influenced a refinement of the original data needs analysis from prior research, serving as part of the validation process for the BIM standards and guidelines development.

In the architecture, engineering, construction, and operations (AECO) industry, different visualization techniques are used to represent a project. These techniques range from printed two-dimensional (2D) drawings to full 3D building information modeling (BIM) display. Recently, the AECO industry has seen an increase in the research and application of wearable virtual and augmented reality technology for project visualization as well. The purpose of this research is to develop and evaluate a new visualization technique which does not require wearable or individual devices, enabling group collaborative visualization. The proposed technique is a less-expensive technology based on creating holographic illusions directly from BIM models. The proposed hardware is designed to be used on a meeting table where the hologram can be seen from any point and manipulated by meeting attendees using hand gestures and/or voice commands. Content for the proposed device is developed directly from BIM models and imported into the visualization environment. The developed prototype includes sample projects where the user can rotate the building by floor, as well as compare models of each construction discipline, thus enabling model coordination and review activities. The aim of this study is to develop a holographic model visualization system (HMVS), which can seamlessly interface with existing BIM models, to facilitate the understanding of project-related issues in live-projects or academic settings. Additionally, researchers are working to develop add-ins and workflows which enable the direct export of a BIM model for display directly via the holographic projector, without the need for additional user manipulation. Moving forward this research will be expanded to include the testing of the developed HMVS’s performance, as well as the ability to detect clashes, change materials, and compare planned versus actual construction progress using the developed holographic visualization technique.

The building use, operation, and maintenance manual, also known as the building owner’s manual (BOM), is a document that provides all the information necessary to help Brazilian home owners operate, maintain, and repair their buildings. However, such manuals are in most cases presented in textual format with sparse technical terms and references, and lack the necessary information as required by federal government standards. As a result, building owners often find it difficult to effectively use the manual or find specific content pertaining to the problem to be fixed. A potential remedy to this situation is to enhance the traditional building manual’s format using augmented reality (AR) that promotes new forms of interaction between the user and the BOM. In a nutshell, AR enables the displaying of virtual (computer generated) graphics overlaid on views of the real environment. The specific objective of the research presented in this paper is, therefore, to create and assess new formats of BOM enriched by AR in order to promote its use. To this end, a survey was first conducted and a total of 27 BOM samples were collected from Brazilian construction companies. After a thorough content analysis, one of these manuals was selected and used in the prototype living augmented reality (LAR) application. A graphical software and a game platform are used to develop LAR that can be launched on tablet computers and smart glasses. LAR displays a step-by-step AR animation of a maintenance activity to the user. This paper presents results of the survey of BOM manuals currently practiced in Brazil, as well as a description and technical details of the preliminary LAR maintenance experiment.

Building information modeling (BIM) is facilitating a procedural change for the architecture, engineering, and construction (AEC) industry to share information in all the phases of the life cycle of a building. It possesses great advantages in designing, analyzing, and documenting all physical and functional information of a building and construction project. Structural analysis is an integral part of the life cycle phases of building construction projects. The information needed for structural analysis originates from the architectural model, but the architectural model can be created without much consideration of structural analysis. Software tools used by architects and structural engineers are usually different and sustain information inconsistency and or missing information leading to software interoperability problems. As the first step towards addressing this issue, in this paper, the authors conducted a preliminary literature review in order to identify topics and trends on the BIM interoperability problem with a focus on the structural analysis domain, from both the theoretic perspective and the application perspective. Structural analysis is performed and discussed in the following sections to demonstrate interoperability problems and propose possible solutions.

Building information modeling (BIM) is a digital information management system that can facilitate workflows and delivery of information in facility projects. Cost estimating is one of the uses of BIM that has the potential to reduce considerably the time and cost required for estimating construction cost of a building project. In this use, type of materials along with their quantities and properties are extracted from building information models and mapped to a cost estimating database in order to estimate the cost. However, interoperability issues currently exist in the industry for the exchange of information from BIM authoring software to cost estimation tools impedes reaching to the full potential efficiency in this use. This paper identifies common information exchange workflows between BIM authoring and cost estimating tools and discusses their advantages and issues. It continues with studying potentials in leveraging open information modeling standards for addressing the identified shortcomings in the workflows. Next, a level of development (LOD)-based cost estimating framework is proposed that uses open information modeling standards for addressing inefficiencies in different detail levels of cost estimating approaches commonly being practiced in the industry. This framework facilitates automated digital information exchanges in the process of using BIM for cost estimating and promotes the implementation of BIM-based cost estimating from early stages of projects.

Complete 3D mapping of indoor construction scenes using images and videos is a challenging task. It requires all workspaces to be continuously captured to enable complete modeling of the rapidly changing environments. Nonetheless, manually mapping an entire workspace on a daily basis is time-consuming. Also, at building sites, guaranteeing complete 3D mapping require carefully accounting for transitions in hallways for moving the camera from one workspace to another. To address these challenges, this paper presents a new method together with a workflow that optimizes data capture and enables complete 3D modeling. Using daily images taken, a pipeline of structure from motion coupled with multi-view dense reconstruction algorithms enables complete 3D modeling in each workspace. Walkthrough videos are then recorded weekly and are provided as input to a variant of a SLAM algorithm to tie in locally reconstructed workspaces in 3D. This enables project teams to capture images of each workspace-where work is continuously changing-at a high frequency (e.g., daily) and continuously produce dense 3D models. It also leverages a video capture of the entire workspace on a low frequency (e.g., weekly) to tie in locally produced dense 3D models. Preliminary results from a project site show that by decentralizing the image capture of each workspace to the trades as part of their required daily field reporting, and rapidly videotaping all work locations by a site engineer as part of a weekly walkthrough, the entirety of an indoor phase of construction project can be mapped in 3D. The specifics of the method and the benefits and challenges of the implementation workflow are discussed in detail.

3D modeling of structural and mechanical components from point clouds is a time-consuming and error prone task. It usually involves experts to analyze a captured point cloud, segment and label it into meaningful components (e.g., pipe, ceiling, beam, column, floor, wall), and then fit solid geometry to these components. Despite a large body of research on Scan2BIM over the past few years, there is still a gap in knowledge on how automatically segmented point clouds can be labeled and merged into 3D surface representations. To address this gap, this paper presents a method that takes in a segmented and semantically labeled point cloud, with mechanical components, at a desired level of abstraction and fits in solid geometrical elements. The input model was automatically labeled using a recent method of jointly labeling point clouds based on their underlying semantic and geometrical representations. To fit solid geometric components, the method uses a multi-scale features extraction method to identify the segment cross-sections and centerlines. The centerlines are used to infer the connection types (e.g., elbow, coupling, union, or diverter tee) among all segments. The method then uses the center lines and the cross-section radius to fit non-uniform rational basis splines (NURBS) over the point cloud segments and then fits in cylindrical elements into the produced NURBS surfaces. The method is validated using six real-world point clouds and the experimental results are discussed in detail.

A reliable source of obtaining accurate feedback flow of information from on-going construction projects is essential for an efficient quality monitoring process. Progress information from an ongoing project assists field engineers and superintendents in keeping track of the usage of resources, duration of activities and the costs associated with each activity. Transportation construction projects, such as those related to highway construction, are often sensitive to cost and schedule overruns due to the magnitude of risk and uncertainty involved. The inaccuracies, inconsistencies and delays associated with manual collection of progress data further contribute to the inefficient tracking of resources, activities and cost, consequently causing issues to be overlooked and leaving them unsolved. This study provides a review on current state-of-the-art of technology supplemented construction progress monitoring techniques, and proposes a framework that uses 3D laser scan point clouds and 4D information models for keeping track of construction progress. The framework identifies discrepancies between as-built information obtained from point clouds and as-planned information obtained from 4D information models, thus enabling the timely modification of cost estimates and revision of schedules. In future work, the framework will be tested using as-built data collected from a terrestrial laser scanner from an on-going construction project. The proposed framework can assist state highway agencies in identifying and addressing issues in a timely manner by acquiring timely and accurate progress reports from the site. Furthermore, using available information from the project cost accounts, an earned value (EV) analysis can be performed based on the amount of volumetric work measured using the framework.

One of the main desired qualities of an efficient project management in the construction industry is the ability to visualize a construction project in 2D drawings and execute them accurately and efficiently on-site. However, there are several types of delays that significantly influence project durations, often resulting from misunderstanding and miscommunications among parties (owners, contractors, and other stakeholders). One of the more common delays is change orders that are usually attributed as one of the major reasons behind delays in construction. Making changes after a building enters the construction phase can be very expensive, conflicting, and time-consuming. Such delays may arise due to the lack of communication and coordination among the owner and the contractors regarding the change orders. In most instances, it is highly conflicting to form a mutual agreement when there is a predicted price difference to accept a change order. To minimize the impact of change orders on the design and total cost, an approach that allows communication and interaction with 3D models through advanced visualization tools (i.e., virtual and augmented reality environments) can be effective. To further identify the influence of immersive virtual environments (IVEs) on project management, a systematic approach is proposed through which stakeholders can: (1) visualize and interact with 3D models in one-to-one scaled realistic virtual environments (fully immersive); and (2) visualize the dollar amount changes as the results of change orders. The results of the presented case study show that clients can exercise the ability to make changes virtually well before actual construction begins.

Various stakeholders are involved in a construction project in which communication plays a critical role in building a successful team. Effective communication not only improves management of the project team, but also builds confidence and strengthens the teams by creating constructive feedback loops. Hence, effective communication helps to deliver construction projects on time, under budget, and with exceptional safety records. Communication in construction is a dynamic process of exchanging and sharing project-specific information between the sender and the receiver. The main purpose of communication is to inform or share facts, ideas, and opinions for desired actions between the sender and the receiver. The construction industry is well-known as a fragmented and dynamic sector due to the subcontracting system. The contractually-driven relationships in construction sometimes bring a lack of mutual respect and trust as the result of ineffective communication. In the process of communication, information must be transmitted to the receiver without misunderstanding or confusion. Otherwise, the message which is understood by the receiver can be different from what the sender intended to send. The distorted and misunderstood information may lead to costly problems during construction. The purpose of this paper is to establish a conceptual framework for effective communication in the construction management domain. Therefore, this paper reviews several purposes of communication in construction as well as the typical communication cycle drawing upon Shannon and Weaver’s model of communication. This paper further reviews related concepts on information processing and visual communication. The results of this study will establish a baseline to determine whether future research on information visualization and visual communication is warranted in the construction management domain.

Building information modeling (BIM) allows new levels of spatial visualization, as well as more efficient project management. Despite the advantages of BIM, its proper implementation in projects faces challenges such as the time-consuming and manual process of entering input data and updating models. In countries like Iran, which BIM is not widely implemented, BIM-based project planning, however, will be more difficult than traditional methods like critical path method (CPM), considering the large number of pertinent scheduling parameters. This research focuses on developing a method to create four-dimensional (4D) models with BIM-based scheduling using constraint-based simulation. By using known standard documents and methods in Iran, this framework facilitates the process of creating BIM-based schedules and 4D models in Iran. In this paper, literature related to the research subject will be reviewed, and the modeling method will be explained then capabilities of the proposed will be demonstrated and described through a case study.

Project delivery describes the system used by the project owner to plan, design, construct, operate, and maintain facilities by entering into legal agreements with one or more entities or parties. Building information modeling (BIM), on the other hand, is a process involving the generation and management of digital representations of physical and functional characteristics of places. While BIM and its associated tools are argued to be applicable to all types of project delivery, their successful implementation by engineering firms on projects is still not being achieved consistently. The application of BIM to support an optimal cross-disciplinary and cross-phase collaboration creates a new vision for the roles and relationships among all project participants. In particular, the architect/engineer (A/E) who is accustomed to playing an independent role under the traditional delivery approach is now a design subcontractor under the design-build (DB) delivery approach or a project team member under the integrated project delivery (IPD) approach. Through focusing on the DB delivery system, this paper investigates the applicability, in terms of the usefulness and the degree of relevance, of BIM to DB projects. This is achieved by modeling and comparing the design-construct phase in traditional design-bid-build (DBB) projects and in DB projects, both under a BIM environment. The main contribution of this analysis is to offer a better understanding of the use of BIM on DB projects. Designing in concurrency with construction under the DB approach in a BIM environment is envisioned to have a crucial role in providing documented historical data on the levels of development for modeled elements, when applied systematically. Future analysis of this data using statistical methods is expected to reduce the rework associated with the fast-tracking nature of this approach.

Inspecting large equipment and facilities, such as cranes, is a challenging task. The current inspection process associated with construction industry has a number of limitations, resulting primarily from human error and lack of consistency. Conventional metrology instruments such as micrometers and calipers were typically used to perform these inspections, but they did not provide the accurate 3D results, needed to properly assess the conditions of complicated modern machinery. This study discusses the potential applications of three start-of-the-art technologies including 3D laser scanning, infrared (IR) thermography, and ultrasonic to improve the current inspection processes and to contribute to the existing body of knowledge.

At a dynamic construction site, conversations convey vital information including construction activities, operation status, and task performance. Even though because of information security, recording the entire conversations of a construction project is currently somewhat restricted, establishing a framework to capture and analyze construction conversations would be a promising approach to enhance the utilization of new field information for construction progress monitoring and safety surveillance. The construction industry, however, has no proper method to deal with onsite conversations. To enhance construction process and safety monitoring that is crucial for construction project management, this paper proposes a new framework to acquire onsite conversations and analyze their significance and interrelationship. The proposed conversation analysis framework involves the integrated implementation of the speech recognition library and the Natural Language Processing Toolkit using the Naive Bayes classifier, which helps translate the conversations to a text script and classify them according to the distinct types of construction activities and operations. Using the conversation videos, this paper represents the translation and classification accuracy of construction relevant conversations. The web audio and text data related to three possible conversation topics at a construction site were collected and used to test the framework in this paper. The proposed framework reached 90.9% overall accuracy. This research is expected to help domain experts monitor construction work processes and make data-driven decisions based on analyzed onsite conversation data.

The emergence of BIM represents a considerable opportunity for stakeholders in the AECO industry to more effectively deliver and manage the built environment. Of the lifecycle opportunities that have been documented for these stakeholders, none have apparently more to gain than large built asset owners. There is a challenge, however, in moving from theoretical applications of BIM to implementation, especially for large organizations such as public owners. This study aims to uncover how BIM can be effectively used for design review in a large owner organization. The research team undertook a retrospective case study and analyzed a series of design review comments on 12 different projects. During the analysis, prevailing, and recurring issues were extracted and characterized to better understand how a BIM-enabled design review process could benefit the owner compared to conventional 2D drawing-based review methods. The results of this study showed that 26% of the design review comments were related to information content or structure. This suggests that the current method being used to convey information is inefficient, and considerable amount of time is being spent simply retrieving and processing information. Examples of BIM-enabled design review are also provided to demonstrate the potential benefits of BIM from an owner’s perspective.

Prefabricated buildings have been shown to provide a new way of sustainable construction to reduce the amount of work on construction sites, reduce pollution, shorten construction time, and increase safety. However, many issues in the actual production process (e.g., quality check, storage, and installation of the prefabricated components) are mainly caused by the lack of the information being collected and shared. In order to better monitor and manage the prefabrication process efficiently, this research proposes a framework that integrates radio frequency identification (RFID) and building information modeling (BIM) technologies. The scope of this paper is the discussion of the framework and its application for prefabricated housing production. A preliminary study focused on the manufacturing process of integrating RFID tags with the prefabrication of the components. About 200 reinforced concrete components were embedded with a passive RFID tag. Results of the experiments showed a 90% reduction of signal strength when passive RFID tags are embedded in concrete, with less than a 1.5% fail rate. Current RFID tag implanting techniques still need to be improved to meet the actual demands of the construction industry and increase the signal strength. Significantly, this research shows promising results that the framework could establish a high efficient, low cost, and easily implementable information capturing and sharing system for further planning and management of prefabrication.

Monitoring the amount of 3D physical progress of indoor buildings is crucial in demonstrating of the level of productivity and efficiency on a construction site. To monitor this type of progress, accurate 3D data such as advanced mobile and terrestrial lidar data should be collected at each step of the work process. While 3D physical progress of outdoor construction sites have been studied so far, algorithms of progress monitoring have been rarely evaluated for application on both mobile and terrestrial lidar data sets for indoor buildings. This paper aims to evaluate the performances of two progress monitoring algorithms, namely C2C and M3C2, for application on time series mobile and terrestrial lidar datasets. The evaluation is based on speed of processing and classes of magnitude of change. Better performance of M3C2 is proven by the results because it is faster than the C2C algorithm especially when both types of mobile and terrestrial lidar data sets are used. In addition, M3C2 is able to detect both classes of change, either increased or decreased during the construction progress.

As a traditional industry such as the construction, it is hard to collect data for management and improvement. Traditional ways such as questionnaire survey, interviews, or focus meetings to collect data are both time- and cost-consuming. Recently, with the rapid development of social media services, data can be collected and extracted for topic analysis to provide officials and managers with fresh perspectives on participants in the construction management. In this paper, a topic analysis systematic framework is proposed. This system collected user messages from social media sites, establishes and compares different clusters’ topics and keywords in their messages. This paper generated valuable information and knowledge in the construction domain. As an initial trial, this study selected social media of Weibo because of its wide usage in China. Four clusters which include construction workers, construction companies, construction unions, and construction media were analyzed. For each user, the crawler is used to collect the Weibo messages from his/her web page. On average, there are 135 messages collected for each user. This research then analyzed these data in the following aspects to dig out information behind data: keywords, hashtags, and topic modeling. Detailed findings, benefits, and barriers to incorporating social media data analytics in the construction industry, along with future research, were discussed. This paper benefits the academia by testing an alternative way of studying the construction population, which further will help decision makers better understand the real situations of the construction industry.

Change orders lead to the modification of construction works, which has a direct impact on various aspects of construction projects, including scope, budget, and schedule. The traditional change order process involves the production and communication of a significant amount of information, usually in the form of paper-based documents that are mostly narrative and not well organized. It is therefore difficult for all project participants to understand the physical changes to the construction work and identify the information necessary for determining the resulting time and cost impacts. These challenges point to the need for an integrated decision support system that can assist project stakeholders to visualize the physical alterations, to manage the pertinent information, to perform quantity takeoff, and to assess the impacts of construction changes, all of which can be facilitated by building information modeling (BIM). This paper develops a BIM-based system that can be used to evaluate the impacts of construction change orders. The proposed system focuses on quantifying three major impacts of change orders in construction projects, namely, physical conditions, schedule, and budget. The system architecture and integration of several BIM-enabled tools, including Autodesk Revit, Dynamo, Microsoft Excel, and VBA are presented. Given a particular construction change order, the system automatically detects the altered building elements and evaluates the schedule and cost impacts of the proposed change. This system can assist all project participants in visualizing and quantifying the impacts of construction change. It can also enhance the accuracy of time-cost impact evaluations as well as facilitate the overall change management process.

Construction is a labor intensive industry. Many of the tasks on construction sites require minimal formal training, and thus workers develop their skill sets as they are working. Construction workers learn the required skills by interacting with other workers. The effectiveness of such interactions is expected to affect the development of workers’ skills and eventually the quality of their work. Understanding the relationship between team dynamics and quality on construction sites calls for using higher order modeling techniques such as agent based modelling (ABM). This paper presents an ABM that evaluates the effects of team dynamics on quality on construction sites. The outputs of this model are analyzed using social network analysis (SNA) for four different scenarios with varying crew sizes and worker skill levels.

The completion of a construction project requires a great deal of data management to successfully implement the personnel and material resources in the most productive manner. In general, construction documents, specifically technical specifications, require contractors to submit product data, samples, and shop drawings as well as change orders to architects and engineers for approval. After the approval, the material will be ordered and delivered to the construction site, but the material itself does not contain any information regarding change orders or submittals that are already made. After the delivery of materials, construction workers and managerial personnel have to check the materials separately based on the construction documentation. The process is therefore ripe for a more efficient method to manage data and improve the communication from the managerial positions to the craftspeople. Fortunately, two-dimensional (2D) barcodes are able to bridge the information gap between the documentation and the material. The resilience of barcode technology to compete, reinvent, and refine itself every few years is astonishing. The amount of data transmitted, collected, and deciphered via this technology is truly monumental. In fact, without identification, tracking capabilities, and economics of barcode technology, gigantic global couriers, and logistics management companies such as FedEx, DHL, and UPS could not have thrived. One of the next generation barcodes is a two-dimensional barcode known as the visual quick respond code (QR-Code). One-dimensional (1D) barcodes can encode just several digits to hundreds of characters while the QR-code can store thousands of characters. This paper discusses the feasibility of introducing QR-code implementation to the construction management process for reducing unnecessary procedures in material handling and installation.

Delay analysis is an essential tool to support construction delay claims. Therefore, when performing time impact analysis (TIA) resource constraints should be considered since these may affect the project completion time. Previous studies have shown that resource-constrained scheduling (RCS) techniques neglect the resource activity relationships creating phantom float in the schedules—a float that does not really exist. At present, the available scheduling software in the construction industry do not remove phantom float from the schedules when RCS is performed. Hence, when users utilize the RCS schedules to perform a delay analysis, the decisions are made on unreliable schedules. This study shows how to perform TIA using resource-constrained schedules without phantom float. These schedules were developed based on the serial method (incorporated in P6) and the parallel method (incorporated in MS Project). According to the results, different outcomes can be obtained for the same delay claim scenario if the initial resource relationships between activities are not removed before re-scheduling the project or if different methods are used to mitigate the resource supply demand problem. Under both circumstances, the activities sequence changes, and so the resource relationships and the critical path.

Most decision making in construction projects is accomplished by human beings based on manually collected information. This process is labor-intensive, error-prone, and heavily reliant on the knowledge and experience of the decision makers as well as the quality and reliability of the available information. Cyber-physical systems (CPS) offer the potential to transform traditional data collection approach and strengthen the coordination between cyber models and physical assets in a construction project. This paper describes a knowledge-based CPS architecture that aims at augmenting human decision making in construction. Domain knowledge, such as site policies, safety rules, and workflow logic, is elicited and compiled in an ontology-based knowledge hub. With the support of bi-directional cyber-physical communication, the knowledge hub inquiries relevant data from cyber models and physical assets, integrates real-time project data, and synthesizes context-specific information to facilitate human decision-making. This paper outlines the key challenges and technical requirements for creating the knowledge hub and its linkages with traditional CPS systems. It also presents two deployment scenarios in highway and building construction projects to demonstrate the realization a knowledge-based cyber-physical system and its potential in facilitating coordination and improving project performances in safety and efficiency.

The uniqueness of the construction industry can be understood in the large volumes of information in terms of paper works, processes, and communication disseminated daily. The voluminous information requires close coordination which cloud based systems offer. The aim of this research is to assess the linkages and leakages in a cloud-based computing collaboration among construction stakeholders. A purposive sampling technique was used in selecting the participants of the study. A questionnaire based instrument was distributed to eighty (80) construction stakeholder in Lagos State, Nigeria. The data collected was analyzed using SPSS v.21. Statistical tools such as frequencies, stacked bars, mean scores, factor analysis, and analysis of variance (ANOVA) were used in the study. The result of the study revealed that construction stakeholders rarely utilize cloud-based technologies for their construction processes. Construction professionals that access cloud based technologies, do so with their smart mobile phones, laptops, and tablet. The study classified the leakages that exist in the use of cloud-based technologies as security-based factors, cloud-based infrastructure factors, and cloud-based benefits deficiency based factors. In conclusion, the study revealed that the linkages in the use of cloud-based technologies include knowledge sharing, remote access of back-office activities and engendering collaboration among construction stakeholders. It was recommended that construction stakeholders should leverage on the benefits cloud-based technologies has to offer in today’s competitive economy. Construction stakeholders should be adequately informed on the available cloud-based computing technologies and the additions it can bring into the construction process. Cloud computing technology vendors should improve on the security and privacy features of the platform for adequate protection of building data.

This paper discusses a framework to track, measure and report construction projects progress in the oil and gas sector. The tracking method has been applied to a natural liquid gas project that involves procurement, fabrication, construction, and commissioning. The framework consists of: (1) building information model, (2) rules of credit, (3) database system, (4) interface application, and (5) reporting module. To achieve a high level of tracking accuracy, the BIM is developed from the issued for construction drawings. Material takeoff is extracted and classified from the BIM. The field construction supervisor interacts with a mobile application to actualize material received, installed, or quality inspected. The method was tested using natural gas plant project for tracking and calculating progress. The results generated from the progress system such as percentage completion, earned value, project indices, and cash flow provided the management team with information on the health and status of the project. The next stage of the research will involve enhancing the system algorithms and expand the method to progress tracking for turnkey projects.

Resources play a very important role in the success of the construction projects. Estimating the required resources such as equipment, material, and labor is a necessary step in the development of construction technical plans. Resources are selected based on the properties of the elements to be constructed, construction method, activities, and tasks and their precedence relations. This paper proposes a BIM-based ontological framework for developing detailed construction method statements for single construction products. The construction method statements are developed based on the product information from BIM and available resources. An ontological approach provides excellent means for this purpose. Ontologies can capture, manage and represent knowledge in a machine-readable format. Moreover, they support consistency checking for and reasoning. We used web ontology language (OWL) for ontology representation and semantic web rule language (SWRL) to express constraint knowledge in the form of rules and provide reasoning capabilities. We pay special attention to detailed definition of project resources and their operational specifications. In the proposed framework, product information from BIM are received and structured in an ontological product model. This approach paves the way for an effective construction planning with details on required resources, based on available resources and their specifications and the specifications of the product and other resources. Through the implementation of the framework to a real construction project, the capabilities of the proposed method are presented.

Social network mining is a typical application of the network intelligence that can facilitate investigating social context of work processes and provide insight into increasing the efficiency and effectively of processes and organizations. This research develops a systematic methodology to deeply mine tremendous volumes of design logs, which are generated by using Autodesk Revit in building information model (BIM) design process, in order to discover social networks in BIM-based collaborative design practices. A large dataset of design logs that involves 51 designers working on 82 projects with 620,492 lines of valid commands, provided by a major international design firm, is used as a case study. From a macro-level SNA, there are 514 links among 51 nodes in the discovered social network, the density, mean distance, and average degree of the network is 0.202, 1.984, and 10.078, respectively. This indicates the collaboration is finely intensive and one designer can connect with another in a few steps in the BIM-based collaborative network. From a micro-level SNA, degree, closeness, and betweenness centralities of each node in the discovered social network are calculated out to measure the embeddedness of each node and his position in the whole network, and it is found that designers located in the center of the interaction map (with highest degree centralities), such as “#2” and “#24,” are basically those who provide shortest communication channels (with highest betweenness centralities) and are most reachable for others. This research provides insight into (1) capturing collaborations among actors from objective event logs to discover social networks, and (2) a better understanding of network characteristics of designers for enhancing the likelihood of project success within organizations.

The construction industry, with most of its processes being repetitive and labor-intensive, is one of the least digitized industries around. The industry has evolved immensely over the years, and its future is dependent on the ability of construction companies to modify their mode of operations, regulations, and skill sets to thrive in the information and technology-driven world. The adoption of modern technologies is very critical for construction companies who aspire to remain economically feasible and competitive. Augmented reality (AR) is an emerging technology that can be implemented in the construction industry to enhance real-world site conditions through computer-generated content and information linked to site-specific locations and activities. However, the widespread implementation of AR by professionals in the industry is limited, despite its potentials. The purpose of this study is to promote the adoption of AR by accounting for the high initial costs of AR technologies, and presenting the potential cost benefits associated with the application of AR in construction processes. The objective of this research is to review literature that covers the possible benefits of implementing of AR and present the qualitative and quantitative benefits accompanied by the application of AR solutions to determine how the use of AR can generate value for construction stakeholders. The expected financial benefits to companies willing to capitalize on AR are determined by the significant cost savings accrued from enhanced productivity, shortened project duration, reduced conflicts, improved project collaboration, and reduction of rework. This study is a step in determining how useful AR can be in the improvement of construction processes with the aim of increasing the awareness and acceptance of technology in the construction industry. The study provides construction professionals an understanding of the economic value of AR, consequently advancing its deployment in the construction industry.

The potential of building information modeling (BIM) to add value to facilities management (FM) has long been recognized. The usefulness of BIM in asset management, including operations and maintenance has been described by numerous authors. Crucial to its implementation is the integration of information, which increases efficiency and productivity on the job and, in turn, positively impacts the primary organization’s mission and goals. In view of the potential of BIM to add value to FM which in turn can boost the mission of organizations, there is a potential to study the experiences of early adopters, map out patterns and differences and to record lessons learned. This research aims to investigate how BIM is implemented in FM operations, how value can be derived and what the critical success factors are. It further explores the areas of process waste and consequent loss of value within the lifecycle phases of facilities. To this effect, the case study of a large tertiary educational institution is undertaken, mapping the processes of information flow during the translation phase from 2D drawings to 3D models of existing buildings. Value stream mapping of organizational processes will identify areas of potential waste or non-value-adding activities, and also areas of potential value-adding opportunities. By studying BIM value through the lifecycle value chain, and identifying good practices and challenges in light of the more subjective nature of value delivery in FM, more impactful outcomes should be derived.

Nowadays in some areas of Nigeria, it has been noticed that the search for a satisfactory team of labourers (skilled and unskilled) or even the right amount of people to undertake the job has become a laborious activity. Therefore, there is need to build a two-way partnership system linking construction firms with the required skilled labour and vice versa. The aim of this research is to develop a web-based human resource sourcing system for labour only contracts. The study made use of a questionnaire instrument and developed a web-based interface for skilled labour sourcing. The questionnaire was distributed to fifty (50) construction professionals who were selected through a convenience sampling technique. The data collected was analyzed using SPSS v.21. The design of the system was done using hypertext markup language (HTML) for authoring web pages. The study revealed that contractors’ sourcing techniques for skilled labour on construction projects were mostly by referral from colleagues and workers from past projects. The study identified the major barriers in sourcing for skilled labour on construction projects as the unavailability of trained skilled labour. The study revealed that years of experience, wages charged and behavior of the worker were major criteria considered while sourcing for skilled labour on construction projects. In conclusion, the study developed a web based human resource sourcing system for labour only contracts that incorporated the needs of the skilled labour and requirement of construction firms. It was recommended that construction professionals and contractors need to harness the benefits of web-based technologies. In addition, there is need to increase the information and communication technology (ICT) trainings of workers in the construction industry so has to have a successful integration of ICT and the construction industry.

Engineering deliverables to construction craft workers have remained largely unchanged for a century. Black and white, 2D paper drawings are the primary medium by which engineering designs are communicated, but a certain level of experience is required in order for a craft worker to efficiently extract necessary information from a drawing. This work presents a novel method of visualizing and analyzing eye tracking data through the use of convex hull areas. Convex hull areas are proposed as a metric for measuring the amount of information that a participant is processing from a drawing at a given instant in time. Eye tracking data was collected in a previous study where 20 construction craft workers were tasked with assembling a PVC pipe assembly from traditional 2D isometric pipe spool drawings. Demographic data and spatial cognition data were also collected from each participant. In the present work, craft worker spatial cognition and years of construction experience were both shown to correlate with a craft worker’s average convex hull area. The authors developed software for producing animations of eye tracking data convex hull areas, but have only begun to assess the data produced from the convex hull analysis method. Average convex hull areas were analyzed in the present work, but several potential additional metrics are suggested. This study was severely limited by the small sample size of the previous study and further data collection is warranted to confirm the findings presented herein.

Following several years of proposed regulations, opportunity for public feedback, and revised proposed regulations, in August 2016, the United States federal aviation administration (FAA) enacted regulations governing the use of unmanned aerial vehicles (UAVs) in the airspace of the United States. The enactment of these regulations ended a period of uncertainty regarding the viability of widespread use of UAVs in the construction industry. This paper will discuss the evolution of and current regulations in the context of the use of UAVs in the construction industry. To begin to understand if and how the regulations may effect UAVs use by construction companies, the paper will also discuss the results of a survey regarding the current use of UAVs in the construction industry, including but not limited to how long UAVs have been used on construction sites, how frequently UAVs are used, for what purposes, and at what expense. Initial results indicate: general contractors with fewer than 50 employees have the highest rate of UAV usage, among non-users the regulations have not been a deciding factor, and among non-users those most likely to implement UAVs in the future are companies that employ 101–300 employees. The majority of those who currently use UAVs plan to increase their use of UAVs, while non-users more often than not do not recognize the benefits of UAV use and therefore, the majority do not plan to implement UAVs in the future.

With greater demand for high performance buildings and more specialized expertise within design and construction professionals, there is an increase in demand for new methods to support collaboration. One collaborative technology is building information modeling (BIM), to generate and share content across disciplines and organizations, supporting the conveyance of building design and construction information. BIM by itself is not enough to engage participants in collaborative practices. Interactive workspaces, technology, and media-enabled collocated spaces, can be used by project teams to leverage this digital content associated with a project design. These facilities can support large-scale visualization of models, multi-modal interaction, and aid collaboration of team members. This paper presents a survey of interactive workspaces currently used by architectural, engineering, and construction (AEC) industry teams, and their relative use cases in the design and delivery process. Facilities were identified and documented via a content search in literature and publicly available resources. Twelve semi-structured interviews were conducted with interactive workspace owners/managers to understand the feature sets and use cases of these facilities in more detail. Current use cases, as well as the similarities and differences in feature sets for interactive workspaces used in the AEC industry, are documented. This research presents three categories of feature sets: physical features, application features, and interactive and immersive features. The results document the variety of interactive workspace use cases and provide a deeper understanding of their role in the AEC Industry.

Understanding the impacts of point-cloud data density on the reliability of change detection calculations is crucial for maintenance planning of civil infrastructure systems. Automated spatial change detection of structures (e.g., deformations of bridges) based on laser scanning data can help engineers assess structural conditions and prioritize maintenance activities. Collecting point cloud data with high accuracy and level of detail is time-consuming and difficult due to complexed environments on construction sites. An efficient method that evaluates the impacts of data quality on change detection is thus important for inspectors. Inspectors could collect point clouds with relatively low data densities to save time for data collection and data processing without compromising domain requirements of change detection in engineering projects. As a step toward comprehending 3D data quality on spatial change analysis of structures, this paper examines an automated 3D data quality checking method to quantify the impacts of point cloud data density on the reliability of spatial change detection of civil infrastructures. The authors designed five comparisons for supporting the impact analysis of data density. In the five comparisons, the authors used the same reference point cloud and five point clouds with different data densities. The authors sub sampled the point clouds to make them having lower, similar, and higher data densities than those of the reference point cloud. Based on the change detection results, the authors found that when the data density of the point cloud is similar as that of the reference point cloud, the change detection method detects most of the changed areas. When data density of the compared point cloud is much higher than that of the reference point cloud, the area of the detected changes increases a little but it takes significantly more time to collect and process the data. The future research will further characterize the impacts of data noise, accuracy, and data completeness on the reliability of change analysis of structures of various geometric complexities.