Journal of Construction Engineering and Management

The construction site environment is quite complex with many dangerous hazards (e.g., foundation pits, holes). To avoid injuries, workers must wear helmets that are color-coded for the specific type of work, which is helpful to identify whether workers are in permitted areas. Therefore, it is possible to identify unauthorized intrusion by classifying the safety helmets. This study proposes a vision-based method called Helmet–Yolov5 to automatically detect unauthorized intrusions by workers on construction sites. Multiple improvement measures are made to enhance the model performance. First, the attention mechanism is used to enhance the weights of object regions in the image, which makes the detection of small objects more effective. Second, atrous spatial pyramid pooling is adopted to preserve the detail information of the image. Third, the universal upsampling operator is introduced to fuse image features at different scales. To verify the effectiveness of the improved model, images collected from a real construction site are used to build a large-scale image dataset of safety helmets for model testing. It shows that the proposed Helmet-Yolov5 model is more accurate than the original Yolov5 model, also with high inference speed. Compared to other state-of-the-art models (e.g., Yolov4), the Helmet-Yolov5 model has considerable advantages in term of high detection accuracy and efficiency.

Digital technology is evolving, and construction companies now have various choices of these technologies to implement into daily business. This has, however, resulted in a growing argument on how digital technologies can be accepted in various contexts. However, most arguments are based on technology acceptance models with the core concept of individual behaviors, which ignores the influence of other stakeholders and organizational characteristics. This paper aims to present a novel model representing the successful practice of the assimilation process for digital construction technologies that should be considered before any technology acceptance analysis. The model was tested via empirical investigation with 372 practitioners from Australia involved in building and infrastructure construction projects. Structural equation modeling, measurement invariance of composite models, and some semistructured interviews were used to test and verify the relationships between key variables of the model. The results suggest that the capability of assimilation can be enhanced by improving innovativeness and the relevant operational processes, with a $t$-value of $>6.27$. The findings show that a project’s likelihood of successful technology assimilation can also be increased if vendors are supportive and the company improves its operating performance assessment and absorption capacity. The findings clarify theoretical and practical contributions for a better understanding of critical factors of assimilation and provide a tool to potential company users for predicting the success of technology assimilation.

Acquiring accurate spatial memory is critical to many architecture, engineering, construction, and facilities maintenance (AECFM) tasks. The literature has recognized that spatial memory (i.e., the memory of locations and spatial relationships) quality can be affected primarily by visualization methods, i.e., the formats in which spatial information is visualized and presented. In addition to traditional two-dimensional (2D) drawings, emerging visualization methods, such as building information modeling (BIM)-based models and virtual reality (VR), provide more options to visualize complex spatial information in an immersive and interactive way. Although it is well recognized that new visualization methods can benefit performance in many spatial tasks (i.e., tasks that rely or partially rely on spatial memory), there is a lack of explicit evidence about how they affect spatial memory development. Many studies used spatial task performance and spatial memory quality interchangeably, which actually are distinct. This paper presents findings of a mental mapping method (i.e., hand sketching the memorized locations and spatial relationships) to directly measure a person’s spatial memory quality of different building components and layouts. First, a human-subject experiment ($n=63$) was performed in which participants were instructed to review a building model with varying visualization methods [2D, BIM-based three-dimensional (3D), and VR]. Then participants were required to sketch the memorized building landmarks and layouts as an explicit measure of the spatial memory quality, followed by an inspection task in the real building. The result showed a significant impact of visualization methods on the quality of spatial memory, which was not correlated with the inspection performance. The findings suggest that explicit mental mapping measures instead of final task performance are needed for evaluating spatial memory in AECFM tasks.

This research develops and tests a structural equation model of the factors which determine the work-seeking experiences of refugees and skilled migrants in the Australian architecture, engineering, and construction (AEC) industry. Past studies had identified a number of potential barriers to employment that refugees and migrants face when looking for work, though they are typically treated in isolation, meaning that the way they interact in determining employment outcomes is poorly understood. To address this gap in research, data was collected from 100 migrants and refugees that were either working in or actively seeking employment in the Australian AEC industry. The model, developed through partial least square analysis, provides new insights into the interaction between labor market familiarity, human capital, perceived support structures, labor market discrimination, work-related acculturation and the experiences of securing meaningful work in the Australian AEC industry. The results indicate that the level of familiarity of a candidate with the local market positively influences their perceived support structures, which in turn impacts their work-related acculturation, and opportunities for obtaining meaningful work. Perceived labor market discrimination also influences the relationship between the familiarity of the candidate with the market and their perceived levels of support. It is concluded that policy makers should focus on better integration of skilled migrants and refugees in the local AEC industry via enhancing familiarity in the local market, qualification and experience recognition, raising awareness of work culture, and legal systems and procedures, along with removing negative stereotypes to help reduce the barriers to employment that they face.

The teleoperation of robotic arms is expected to play a key role in dangerous or inaccessible construction workplaces. Most robot arms rely on mechanical designs that are completely different from human arms. It could lead to a risk that certain joints move with undesired poses and cause collisions because of the mismatch between robot mechanical design and human operators’ egocentric perception of their own arms. This paper proposes an innovative robotic control method that mimics the human shoulder-arm structure, enabling human operators to teleoperate with unfamiliar robotic arms intuitively. A two-tracker system is used to map human arm motions into a revised robotic inverse kinematics (IK) algorithm called humanlike IK. One tracker controls the end effector of the robot, and the other tracker drives the middle joint of the robot, analogous to how a human moves the arm. A seven-degree-of-freedom robotic arm was repurposed based on the revised IK. A human-subject experiment ($n=26$) was performed to test the effectiveness of the proposed humanlike IK method in a pipe maintenance task. Results confirmed the performance and functional benefits of the proposed method. It can inspire the design of a new robot teleoperation method for dexterous tasks in construction.

The effectiveness of the prevention through design (PtD) approach to the management of health and safety (H&S) risks on construction sites is widely acknowledged. This approach underlies the construction, design, and management (CDM) Regulations in the United Kingdom that provide for a Principal Designer (PD) role with a statutory duty to plan, manage, and monitor the preconstruction phase of projects and to coordinate matters of H&S during that phase. Although there is a growing body of research literature on PtD practice, there is a gap in the general issue of the practical implementation of the CDM Regulations in general and the performance of the PD in particular. The purpose of this paper is to report research undertaken with the aim of plugging this gap. The part examined concerns the challenges that beset the performance of the PD role and the drivers behind such barriers. A qualitative research design was adopted using, for data collection, 14 focus group discussion sessions involving 89 participants with direct experience of practical implementation of the regulations. The research uncovered three broad categories of barriers to the performance of the PD role: inadequacies in the client’s general approach to its duties; supply chain fragmentation and insurance challenges; and performance-related challenges stemming from limitations in PD technical competence and interpersonal skills. The research is the first study focused on the H&S risk management processes and the organizational and operational barriers to effective management and coordination of H&S matters by PDs. The research outcomes are of obvious relevance to H&S management practice in not only the United Kingdom but also European Union countries and other countries with similar regulations. As management of design H&S risks at the preconstruction stage is an inherent feature of the PtD concept, they could also inform PtD practice with respect to coordination of the work of the different design specialisms involved.

Postdisaster housing reconstruction (PDHR) is a highly complex process because of the large number of recovery projects for affected communities and the shortage of resources after a disastrous event. PDHR also needs a strategy that reconsiders it as a large-scale integrated portfolio of projects instead of individual building reconstruction projects. However, this complexity and the lack of a holistic, systematic approach for planning frequently lead to an ad-hoc or case-by-case decision-making process. To resolve this critical challenge in postdisaster housing mass production, this study investigates and develops a systematic approach that optimizes the design modularization of housing recovery projects considering manufacturing, transportation, and assembly factors for a cost-efficient and sustainable implication of modular construction (MC) in PDHR. Using the genetic algorithm-based optimization method, the proposed method addresses the possible trade-offs between the commonality and suitability of the module configurations for PDHR projects. In addition, the authors used a set of feasible configurations of a variety of modular housing designs created from the AI-based generative design system and conducted the mass production scenarios after a disaster to validate the accuracy and robustness of the proposed methodology. The results clearly show that the proposed method significantly improved optimization and decision-making of MC design and construction processes and considerably enhanced rapid and logical responses to the demands of the postdisaster recovery process. The newly developed method is expected to assist the planners in formalizing the commonality concept in the PDHR process and achieving an optimal level of modularity and commonality that meet the required variation while maintaining the advantages of mass production.

The trend toward more compact designs and congested site layouts makes it challenging for lift planners to provide feasible lift paths for mobile cranes, confronting the added risk of potential collisions when maneuvering through on-site obstacles. In some cases, particularly in congested industrial modular projects, it is inevitable for mobile cranes to walk with loads to a position with sufficient clearance to perform the lifts and place the objects in their final set position. This study contributes to the body of knowledge by introducing a comprehensive lift planning framework to plan complicated lifts involving mobile crane walking operations. Due to the lack of reliable and accurate plans for such lifts in practice and the added complexities, they are often eluded by practitioners compared with the more straightforward pick-and-set scenarios. This study proposes an algorithm for optimized planning of crane walking–involved lift operations borrowing an obstacle avoidance technique from robotics. The proposed path planner thoroughly considers site constraints and crane configurations to prevent collision between the crane body and the load with preinstalled objects. Actual case studies are presented to validate the efficiency of the proposed algorithm. The system generated the presented real-world lift in less than 5 min, satisfying the operations’ optimality, safety, and feasibility.

In the construction industry, collision accidents between excavators and workers can result in a significant number of fatalities. Hence, low-cost supervision systems are necessary to monitor the motion status of excavators and extract the dynamic hazardous proximity zones around these excavators. Recent computer vision–based methods are convenient and economical, and they also do not require the installation of additional sensors on excavators; however, most of these methods only focus on two-dimensional (2D) or static hazardous proximity zones, which are excessively rough for the fine management required at construction sites. Therefore, a framework for the three-dimensional (3D) dynamic hazardous proximity zone design of excavators based on real-time 3D pose estimation is proposed herein. For this purpose, a set of excavator 3D pose estimation algorithms containing the 2D keypoint detection module and 2D-to-3D lifting module were trained based on two corresponding data sets, one of which was collected on the Internet and the other was generated via Cinema4D software. The real-time motion status was calculated based on the 3D poses estimated from the video after postprocessing and coordinate transformation. Moreover, trajectories could be predicted to form the 3D dynamic hazardous proximity zone for an excavator. Notably, this study developed a computer vision–based pipeline for sensor-free excavator 3D pose estimation and applied the 3D pose estimation algorithm to a specific management issue, thus providing a reference for excavator–worker collision prevention and transforming the conventional static and general onsite construction safety management to a dynamic and specific one.

Construction robots are expected to frequently communicate in situ improvisations with human workers to adapt and change their workflow and methods. One way to achieve this is through deictic gestures that are one of the most effective forms of human–robot interaction (HRI) in delivering spatial information. Nevertheless, the limited coverage of deictic gestures in large-scale environments poses some challenges for both humans and robots in leveraging such techniques for HRI in construction. To identify the feasibility of deictic gestures in the construction domain and find applicable solutions for improving performance, this study aims to extend current knowledge on the performance in communicating positional information using deictic gestures by investigating the effects of spatial characteristics on spatial referencing, focusing on the target configuration, target distance, and relative position of human and robot. We observed that the recognition and estimation of deictic gestures were affected by the target plane, target position, and the target layout and that the robot performance was significantly reduced as the distance between the human and robot increased. The findings of this study demonstrate the challenges in spatial referencing within a large-scale environment and highlight the need for bidirectional communication in HRI.

Deficiencies of tendering systems in public projects can leave governments with subsequent issues in projects and waste taxpayers’ money. Electronic tendering (e-tendering) systems have been found beneficial for awarding public projects; however, current practices still have deficiencies. Legal and security issues and the lack of transparency have been identified as the main shortcomings in current tendering practices. This paper argues that the underlying issue with e-tendering systems is due to their centralized nature. Therefore, this research presents a critical assessment of the blockchain technology in developing distributed e-tendering systems and proposes a novel framework for decentralizing e-tendering systems. The proposed framework is created by integrating three technologies: blockchain, public key infrastructure (PKI), and interplanetary file system (IPFS). Using the proposed framework, a decentralized application (DApp) is developed to demonstrate the concept and evaluate the potentials of blockchain in mitigating the security and legal challenges and improving transparency and trust. The result shows that the reliance on centralized authorities for managing the tendering process is reduced in a blockchain-based e-tendering system, and a transparent and tamper-proof record of the tendering history is distributed among parties.

Abstract

Although novel construction tech systems have been developed to monitor construction sites, each collects data in isolation. We present an event processing model (EPM) that applies multiple monitoring technologies in parallel and a set of algorithms to interpret data from the multiple streams for operations control. In a full-scale lab experiment, multiple monitoring systems were applied to monitor workers as they built drywall partitions with a variety of systems and finishes. Data from the different streams were merged to establish what work was done, by whom, where, and for how long. The results suggest that collecting, merging, and interpreting diverse data streams using a multiple monitoring technologies system on-site can provide more complete and more accurate project status information than can be obtained from the use of any individual technology. This is essential for the automation of project progress monitoring, which is important in automating production planning and control tasks.

Practical Applications

Modern construction management approaches need more accurate, more reliable, and automated construction-progress monitoring. Manual monitoring is complicated and expensive in large, complex projects. It is difficult to perform the production control process successfully for whole projects with many related trades and resources. Automating data monitoring, data collection, and computing can reduce errors and increase efficiency compared to manual approaches. This paper presents a fully integrated system to manage production information through the construction life cycle. Different types of construction technologies could be integrated, along with a building information modeling (BIM) environment, into one common data-management system to report actual work progress and events completed on-site. The proposed system would support the decision-making process as part of construction planning in large and complex projects by providing accurate real-time information defining what work was done, by whom, in which location, and for how long. The information can be tailored to suit planners’ needs at four different levels: (1) work package, (2) task, (3) daily recording, and (4) building element. Broadly speaking, this paper presents a digital twin construction (DTC) framework to integrate data transfer and monitoring from field-to-BIM and from BIM-to-field for automated, accurate, and intelligent production control. This system can help planners improve look-ahead planning (LAP) and decision-making processes.

An effective facility management process is crucial to overall firm performance and is becoming a core competency for organizations seeking to gain a competitive advantage. The recent literature on facility management reveals that poor facility management is a major cause of management inefficiency, leading directly to budget overruns, maintenance delays, repetition of work, variations, noncompliance incidents, unnecessary risk, and dissatisfied customers. Therefore, facility management firms should pay close attention to the most critical success factors in order to effectively manage and measure performance. This paper presents a multidimensional campus facility management performance framework (CFMPF) that addresses both operational and systematic factors. Facility management companies, owners, contractors, and consultants can use this framework to identify systemic weaknesses using key indicators to specifically assess the effectiveness and success of facility management. The significance of 45 key facility management success factors across seven categories was determined via a literature review, expert interviews, and the Delphi method. The seven process groups are: Campus Facility Work Force Management, Campus Facility Communication Management, Campus Facility Systems Management, Campus Facility Organizational Management, Campus Facility Financial Management, Campus Sustainability & Environment Management, and Campus Facility Assets Management. To verify the overall agreement via the Delphi rounds, the score percentage and standard deviation to mean ratio were examined. Mean value and interrater agreement (IRA) analyses were conducted to evaluate the correlation between the factors and facility performance. This research adds to the body of knowledge by presenting a systematic operational framework for campus facility management that identifies the underlying factors influencing performance and fills the gaps left by previous studies.

The construction industry has made significant efforts to address the issues of low productivity caused by inefficient construction planning and scheduling. This issue led to more focus on integrating scheduling methods in existing studies without comprehensively analyzing the integration’s goals and objectives. In this regard, this study aims to present and analyze a holistic list of integrated scheduling methods’ functionalities in the construction sector. In this paper, the underlying concept and main objective of scheduling methods are considered as their functionality. A multistep methodology is adopted for this research. First, the authors identified integrated scheduling methods and their functionalities. Second, social network analysis (SNA) and Pareto analysis were used to assess and analyze the significance of the identified functionalities. The findings indicated that improving planning reliability, increasing transparency, identifying and eliminating wastes, and considering the continuous flow of work that follows the lean principles are the most important employed functionalities in the integrated scheduling methods. Also, the last planner system, building information modeling, simulation methods, and location-based management systems are mainly used common scheduling methods in the integrated scheduling methods. Ultimately, this study will aid project stakeholders in better understanding of integrated scheduling methods by defining the functionality concept of scheduling methods.

Contractors tend to challenge the enforceability of liquidated damages (LDs), claiming they are unreasonable, excessive, penalty statements, or concurrently caused. States customarily assert that the LD rates are a genuine reflection of the expenses expected to be suffered when a project gets delayed due to noncompletion. While there are common practices among the states for articulating LD specifications, which generally follow the Federal Code of Regulations, there are no published studies that assist states in comparing their LD rates to those of other states so that the LD rates might be defended. Further, there are no studies that offer models that would uncover the relationship between the LD rates and the contract sizes so that the LD rates might be justified. This work addresses such gaps in the body of knowledge (BOK) in LDs. With emphasis on the working-days (WD) LD rate schedules, the objectives of this work are to characterize the LD rate schedules across the states and to model a formula(s) that would represent the relationship between the WD LD rates and the contract amounts. The data set for the work represents the LD schedules in the standard specifications of all departments of transportation in the United States. Descriptive and cluster statistical analyses were used for the LD rate characterization. For model development, several linear and nonlinear regression models were employed. The results highlighted considerable LDs variability in the smaller contract sizes and exceptional LD rates stability in the larger sizes. Despite the economic differences among the states, it is found that the LD rate is, on average, 0.02 ¢/$ for projects $20 million or above. Below that, the rate increases between 0.03 ¢/$ and 0.18 ¢/$ until the contracts reach $750,000. LD rates tend to decrease sharply with the increase in contract sizes, forming an L or reverse J shape. This pattern proved complex, and only nonlinear regression with transformed variables successfully modeled it. Credible models were obtained after satisfying the least-squares regression assumptions. The work contributes to the BOK by adding a new statistical dimension to understanding LDs and developing regression model(s) that explain the relationships between the LD rates and the contract sizes. The work should help SHAs create, evaluate, and justify their LD rates.

Given that a strong owner has been identified as an important factor in the success of megaprojects, there has been a growing recognition of the importance of megaproject owner organizational capabilities. However, the questions of what organizational capabilities do owners need to foster successful megaprojects, and whether these capabilities are the stronger the better, have yet to be addressed. To answer these questions, this study identified the dimensions of owner organizational capabilities based on literature and interviews, and developed several sets of parallel hypotheses. Based on the 229 valid questionnaire data collected, the relationships between owner organizational capabilities and megaproject success were analyzed through hierarchical multiple regression, and the results were discussed through expert interviews. The findings show that owner coordination, dynamic, and system integration capabilities have positive impacts on megaproject success, with coordination capabilities being the most significant. While the impact of motivational capabilities on megaproject success is inverted U-shaped. This study provides an actionable dimensional framework for megaproject owner capabilities, moving the field of owner organizational capabilities beyond the conceptual level. This study provides empirical evidence for the importance of megaproject owner organizational capabilities and takes a more dialectical view of a strong owner. The empirical results can provide insights and guidance on the configuration and development of megaproject owner capabilities.

As construction projects become more complex and challenging to execute, project managers need to develop work schedules that consider as many objectives as are critical to project success. A work schedule designed to optimize all these objectives is likely to enhance project performance. However, achieving a balance among these objectives is difficult because they frequently conflict with each other. Many optimization models have been developed over the years that can factor in different objectives. Although previous optimization studies have contributed to the body of knowledge by individually adding one or two objectives to the classical time–cost trade-off (TCT) problem, none of these studies simultaneously optimized more than three or four objectives at a time. Clearly, there is a need for a multiobjective optimization model to generate work schedules that increase the likelihood of achieving as many project objectives as necessary. This study was motivated by the need to fill this research gap. The objective of this study was to develop a mathematical multiobjective optimization model for the time–cost–quality–schedule flexibility-resource fluctuation trade-off problem in small-scale construction projects. The proposed model is novel because it (1) considers five important objectives of construction project scheduling at the same time, (2) quantifies all objectives to provide an accurate reflection of construction site conditions, and (3) objectively determines the optimal compromise solution using a distance-based method rather than subjectively from a set of Pareto-optimal solutions. The applicability of the proposed model is demonstrated with an illustrative example. The key results of this study are as follows: (1) the optimal solution obtained depends on the project manager’s priorities relative to the objectives; and (2) in optimization problems with more than two objectives, considering the objectives one at a time may result in misleading solutions, because the effects of the other objectives are ignored in this optimization. Consequently, multiple objectives must be considered simultaneously rather than one at a time. The proposed model is expected to provide project managers with a work schedule that balances five or more objectives, increasing the chances of successful project performance.

Several studies have analyzed heart rate variability (HRV) using nonlinear methods, such as approximate entropy, the largest Lyapunov exponent, and correlation dimension in patients with cardiovascular disorders. However, few studies have used nonlinear methods to analyze HRV in order to determine the level of physical fatigue experienced by construction workers. As a result, to identify and categorize physical fatigue in construction workers, the current study examined the linear and nonlinear approaches of HRV analysis. Fifteen healthy construction workers (mean age, $33.2\pm 6.9\mathrm{years}$) were selected for this study. A textile-based wearable sensor monitored each participant’s HRV after they completed 60 min of bar bending and fixing tasks. At baseline, 15, 30, 45, and 60 min into the task, participants were given the Borg-20 to measure their subjective levels of physical fatigue. Nonlinear [e.g., R-R interval (RRI) variability, entropy, detrended fluctuation analysis] and linear (e.g., time- and frequency-domain) HRV parameters were extracted. Five machine learning classifiers were used to identify and discern different physical fatigue levels. The accuracy and validity of the classifier models were evaluated using 10-fold cross-validation. The classification models were developed by either combining or individualized HRV features derived from linear and nonlinear HRV analyses. In the individualized feature sets, time-domain features had the highest classification accuracy (92%) based on the random forest (RF) classifier. The combined feature (i.e., the time-domain and nonlinear features) sets showed the highest classification accuracy (93.5%) using the RF classifier. In conclusion, this study showed that both linear and nonlinear HRV analyses can be used to detect and classify physical fatigue in construction workers. This research offers important contributions to the industry by analyzing the variations in linear and nonlinear HRV parameters in response to construction tasks. This study demonstrates that HRV values changed significantly in response to physical work, indicating a change in the relative activity of cardiac autonomic functions as a result of fatigue. Using the ways in which HRV parameters vary in response to increased workloads provides a sensitive marker for contrasting construction workers with and without cardiovascular disease. It also allows the site manager to track how quickly workers fatigue, so that they can switch up their workload to reduce the likelihood that any one worker would get severely exhausted, or to suggest that workers who are already severely fatigued take a break to prevent further injury.

Public-private partnerships (PPPs) have become an important option for financing urban rail transit (URT) projects. To ensure the effective operation of URT projects and better serve the public, governments must select suitable operators as part of private-sector consortiums to achieve successful PPP implementations, but they often face a dilemma in choosing between local URT groups and nonlocal operators. In this study, we examine the key factors that determine governments’ operator choices for URT PPP (URT3P) projects with a two-stage approach. A four-dimensional (4D) indicator system containing 20 factors was first developed, and least absolute shrinkage and selection operator (LASSO) regression was applied to analyze one of the largest regional URT data sets in China. Five key factors that significantly influence operator selection are identified, including project operation duration, government-business relationships, private-sector entity organization type, private-sector entity ownership type, and whether the private-sector entity owns an operating firm. This study contributes to the body of knowledge on URT3P project management, and this systematic approach can be used by local governments to select suitable operating partners in similar exploratory contexts.