Journal of Construction Engineering and Management

Construction project managers (CPMs) are highly in demand in the construction industry, but this has not yet comprehensively been studied from a demand-side perspective. This study investigated the real-world demand for CPMs’ competences by mining the big data of job advertisements. Using a large data set of 243,521 job advertisements that covered nearly the whole online job market for CPMs in China over 1 year, we tracked the demands for CPM competences at the industry level. A structural topic model (STM), a text-mining method, was used to analyze the descriptions of the CPM competences requirements to identify the major competences dimensions emphasized in the advertisements. The results suggest that there are eight major competences dimensions that recruiters expect from CPMs. Compared with relatively smaller firms, larger firms place more emphasis on construction site management, project objective monitoring, and organizational management capability. Compared with public firms, private firms prefer coordination and communication, external stakeholder management, and organizational management capability. Cluster analysis of job advertisements further suggested that there are five major types of CPMs, each of which specializes in only a few competences instead of being competent in all aspects. These findings revealed the real-world demands for CPMs and provided theoretical implications for developing a CPM competences framework from a realistic demand-side perspective. The findings also can be utilized to assist construction firms in formulating more-informed recruiting strategies and to help practitioners benchmark their own competences with industry demands.

Despite intensive research efforts that have been devoted to discrete time-cost optimization of construction projects, the current methods have very limited capabilities for solving the problem for real-life–sized projects. This study presents a new activity uncrashing heuristic with noncritical activity rescheduling method to narrow the gap between the research and practice for time-cost optimization. The uncrashing heuristic searches for new solutions by uncrashing the critical activities with the highest cost-slope. This novel feature of the proposed heuristic enables identification and elimination of the dominated solutions during the search procedure. Hence, the heuristic can determine new high-quality solutions based on the nondominated solutions. Furthermore, the proposed noncritical activity rescheduling method of the heuristic decreases the amount of scheduling calculations, and high-quality solutions are achieved within a short CPU time. Results of the computational experiments reveal that the new heuristic outperforms state-of-the-art methods significantly for large-scale single-objective cost minimization and Pareto front optimization problems. Hence, the primary contribution of the paper is a new heuristic method that can successfully achieve high-quality solutions for large-scale discrete time-cost optimization problems.

The pursuit of modern product sophistication and production efficiency has bolstered design for manufacture and assembly (DfMA) around the world. Being both a design philosophy and a methodology, DfMA has existed in manufacturing for decades. It is coming into vogue in construction as a potential solution to the industry’s lackluster productivity amid enduring exhortation of cross-sectoral learning. However, many studies of DfMA in construction are still simply following the DfMA guidelines developed from manufacturing without adequately considering important differences between the two sectors of construction and manufacturing. This study aims to develop a series of construction-oriented DfMA guidelines by adopting a mixed-method approach. It critiques existing DfMA guidelines in relation to the characteristics of construction and further argues that construction-oriented DfMA should consider five fundamental aspects: contextual basis, technology rationalization, logistics optimization, component integration, and material-lightening, either individually or collectively. A case study is then conducted to substantiate and verify the feasibility of these guidelines. This research sheds new light on the cross-sectoral learning of DfMA from manufacturing to construction. The guidelines can be used as the benchmark for the evaluation of manufacturability and assemblability in practice. It also opens up a new avenue for further DfMA studies in construction.

Using deep learning to recover depth information from a single image has been studied in many situations, but there are no published articles related to the determination of construction site elevations. This paper presents the research results of developing and testing a deep learning model for estimating construction site elevations using a drone-based orthoimage. The proposed method includes an orthoimage-based convolutional neural network (CNN) encoder, an elevation map CNN decoder, and an overlapping orthoimage disassembling and elevation map assembling algorithm. In the convolutional encoder-decoder network model, the max pooling and up-sampling layers link the orthoimage pixel and elevation map pixel in the same coordinate. The experiment data sets are eight orthoimage and elevation map pairs ($\mathrm{1,536}\times \mathrm{1,536}\mathrm{pixels}$), which are cropped into 64,800 patch pairs ($128\times 128\mathrm{pixels}$). Experimental results indicated that the $128\times 128\text{-}\mathrm{pixel}$ patch had the best model prediction performance. After 100 training epochs, 21.22% of the selected 2,304 points from the testing data set were exactly matched with their ground truth elevation values; and 52.43% points were accurately matched in $\pm 5\mathrm{cm}$ and 66.15% points in $\pm 10\mathrm{cm}$, less than 10% points exceeded $\pm 25\mathrm{cm}$. This research project advanced drone applications in construction, evaluated CNNs’ effectiveness in site surveying, and strengthened CNNs to work with large-scale construction site images.

The productivity of an engineer could be used as a factor in decision-making in construction management and the coordination of work execution. In this study, an empirical assessment tool is proposed to be used as means to measure the individual productivity of an engineer in a fair and objective manner. The method involves the implementation of a point system for work tasks as well as the definition of repetitive work and different types of delays, these values are then input into a mathematical equation that results in a productivity measure in a percentage format. The final productivity value can be used to assess the engineer, build effective teams, and recalculate engineers’ wages based on their monthly productivity. Moreover, the productivity values can be used to determine more accurately the time estimate for the completion of construction tasks. The proposed assessment tool was developed based on construction site observations. Through conducted trial cases, this tool was found to be applicable to different engineering departments; the point system used for the trials was suggested by the authors and can be adjusted to each firm’s preferences because no standard exists. Although the method is proposed in the context of construction, the authors hope to further develop the implementation of this methodology in other industries. This proposed novel methodology should be further validated and adjusted as required; this paper serves as an initial step to advance the search for a more wholesome method of productivity measurement.

New approaches are provided for urban water environment treatment projects (UWETP) with the development and application of the public-private partnership (PPP) model. However, an asymmetry of information and conflicts of interest exists between the government and the private sector. The moral hazard and the opportunistic behaviors of the private sector present significant challenges to the operational efficiency of UWETP-PPP. To encourage the private sector to operate UWETP-PPP with its real capability and further improve and supplement the incentive contracts, the reputation theory is introduced in this study to design the incentive mechanism of UWETP-PPP in a contract to integrate short-term incentives with long-term incentives and finally to realize a win-win situation between two parties. The main contributions of this paper are as follows: (1) A two-period dynamic incentive model for UWETP-PPP is constructed by introducing a reputation mechanism. (2) The effective conditions for exerting the reputation incentive effects are explored to realize the government’s effective incentive to the private sector. (3) The incentive effects of the explicit and implicit reputation incentive on the private sector are analyzed, and the incentive models with and without reputational concerns are compared. (4) The change rules of the private sector’s effort level and marginal incentive coefficient with the change in related variables that affect the model are revealed. The incentive contract of UWETP-PPP can be optimized using the proposed dynamic reputation incentive mechanism. This paper will be of interest to academics and practitioners concerned with the design of incentive contracts.

In industries in which organizations work in decentralized and distributed structures such as construction, workers are more likely to be influenced by their workgroup supervisors than upper-level management. A study was conducted to quantitatively examine the relationships among supervisory safety communication, group safety climate, and safety behaviors, as well as to qualitatively explore effective ways for supervisors to communicate safety expectations and information to workers. A survey was conducted among workers engaged in rail construction work in Melbourne, Australia. Survey data were collected from 20 workgroups across 11 different worksites. Participant observation was also performed on these worksites to observe safety-related interactions and communication between supervisors and workers. The quantitative research results show that supervisory safety communication practices play a critical role in shaping safety climate within workgroups, which subsequently affects workers’ self-reported safety compliance and safety participation behaviors. The on-site participant observations identified various characteristics of effective safety communication between supervisors and coworkers, including regular engagement, consistent messages and actions, active listening, approachability, personal messages, and respectful delivery. The research results provide important practical implications for construction organizations to dedicate resources to developing supervisors’ leadership capabilities and personal skills, which could contribute to improved safety communication and safety performance in the workplace.

Although out-of-sequence work (OOS) is often cited as a recurring challenge that impacts project performance, it has not been fully addressed in the existing literature as a standalone topic. This paper addresses this gap, defining OOS work as an activity or series of activities that was not performed according to baseline planned logical productive sequencing. By developing a project-based survey and contacting 300 professionals, extensive data were gathered from 42 projects. Using these data, the impacts of OOS were examined. It was found that, on average, approximately 15% of project activities are performed as OOS, resulting in 33% growth in construction schedule and 25% additional construction cost. Applying statistical analysis verified that, at 95% confidence level, OOS has a statistically significant inverse correlation with the productivity index, and a significant direct correlation with cost growth, schedule growth, and value of punchlist items. On average, a 5% increase in OOS was shown to be associated with an 8.5% drop in productivity, 10% increase in construction cost, and 11% increase in construction schedule. Regarding safety, no conclusive statistically significant relationships were found between OOS and the examined safety metrics, possibly because safety measures were followed regardless of sequencing. Investigating 19 project factors, the study highlighted statistically significant relationships between OOS and 15 leading indicators, at 95% confidence level. For example, implementing unplanned overtime, trade stacking, rework, as well as request for information (RFI) enumeration and processing time have significant direct correlation with OOS, while overall collaboration among project team members is inversely correlated with OOS. Project complexity, construction pace (traditional versus phased), percent design completion prior to construction, and the use of second shifts were not found to be statistically correlated with OOS. On investigating the level to which projects used five commonly recommended practices (alignment, front-end planning, constructability, planning for startup, and 3D modeling), the study found that the five practices are inversely correlated with OOS, especially when collaboratively implemented early and often throughout the project. Industry practitioners should use the presented findings to address and mitigate OOS and its negative impacts, ultimately enhancing overall project performance.

Construction site accidents can be reduced if hazards leading to accidents are correctly and promptly detected by employees. Proactive safety measures such as safety perception and safety detection capability of employees play an important role in improving the safety performance. This study was initiated by three research questions related to (1) the measurement indicators of employees’ cognitive load in recognizing safety hazards; (2) site condition factors (e.g., brightness) that can affect subjects’ cognitive load; and (3) the quantification of the effects of these site factors on cognitive load. An eye-tracking experimental approach was adopted by recruiting a total of 55 students from construction management or other civil engineering disciplines to visually search hazards in 20 given site scenes. These site scenes were defined by a combination of three different categories, namely distinctiveness of hazards, site brightness, and tidiness. Quantitative measurements of experimental participants’ visual search patterns were obtained from data captured by the eye-tracking apparatus. Based on metrics related to experimental participants’ fixation, visual search track, and attention map, these measurements were computed to evaluate participants’ cognitive load in detecting hazards. Descriptive statistical comparisons analyzed these metrics under predefined categories of site conditions, i.e., distinctness versus obscurity/blurriness, brightness versus darkness, and tidiness versus messiness. The findings revealed that distinct site conditions reduced participants’ time in saccades to search hazards but did not improve the accuracy rate of first fixation; messy sites with disorganized items increased participants’ cognitive load in detecting hazards in terms of all five measurement items (i.e., accuracy rate of first fixation, fixation count, intersection coefficient, fixation duration, and fixation count in the attention center); the effect of increased brightness on-site needs further studies to determine the optimal balance of brightness level and allocation. Recommendations based on the findings were provided to enhance safety education in terms of site hazard distinctiveness, brightness, and housekeeping best practice. This study extended a few prior studies of adopting eye-tracking technology for safety monitoring by evaluating the impacts of site conditions on participants’ cognitive load, which was linked to their hazard detection performance. The study provided insights for evaluating construction employees’ hazard detection capabilities to enhance safety education. Future work is proposed to evaluate employees’ safety hazard detection pattern under dynamic construction scenarios.

The uncertain construction of hydropower projects and fierce market competition put forward more stringent requirements for contractors with regard to accuracy and competitiveness of cost estimation. However, previous probabilistic cost estimation models are mostly devoid of loss-sharing considerations. To fill this gap, this paper proposes a novel probabilistic cost estimation model integrating risk allocation and claim. The bargaining game between the owner and the contractor is first used to determine the proportion of risk allocation in the price fluctuation and the claim amount in the engineering quantity variation. Then, these two key parameters are incorporated into a Monte Carlo-based probabilistic cost risk simulation. The proposed model is applied to an actual hydropower project, and the results show that the risk reserve fund of the proposed model only accounts for 2.66% of the budget under 80% completion probability, and the average cost risk sensitive coefficient of the proposed model has more than tripled. From the results, the cost estimation value calculated by the novel model is more competitive and accurate in bidding; in addition, to reduce additional cost expenditures, the contractor should maintain a high level of patience during negotiations. This research contributes to improving the probability of winning a contract without sacrificing the contractor’s profit and to extending the current theory of probabilistic cost estimation.

Closed-circuit television (CCTV) is widely used in North America for sewer pipe inspection due to several benefits, such as easy operation and lower upfront costs. To be useful, video footage needs to be collected according to specific standards, which makes the video recording process a time-consuming operation, especially when pipes have operational issues like debris or tree roots. As a result, because city managers are usually limited by the available budget, a good understanding of the overall requirements for CCTV sewer pipe inspection is necessary for efficient resource planning. In this respect, a framework is proposed to model the productivity of the CCTV video recording process by predicting the duration of the recording process based on selected variables. In order to predict the CCTV recording duration, a type of machine learning algorithm and a linear regression model are developed. To be more specific, the random sample consensus (RANSAC) algorithm has been used to extract the benchmark for the CCTV recording process. This algorithm is adopted to screen the data automatically, arriving at a function of the CCTV recording time with two variables (i.e., the total length of the pipe segment and the number of taps in the pipe). As a result, the original dataset that records the CCTV collection process is segmented into three parts: benchmark dataset and two types of outlier datasets. Subsequently, two linear regression models are developed on the outliers to predict the recording duration. Finally, all the developed models are integrated into a simulation model to mimic the recording duration components. The framework is validated by historical data. For the convenience of implementation of the model, the parameters within the model are adjustable to adapt to different situations (such as different seasons, regions, and countries). The contribution of the research lies in two-folds: (1) the CCTV recording process is thoroughly investigated and well-understood, which provides a decision-making basis for the future CCTV collection process; and (2) the proposed simulation model development procedure can be applied to other studies that require data segmentation operation to improve the performance of the simulation model.

A renovation project generally requires time, money, or costly equipment to examine the existing conditions of a structure. When unforeseen existing conditions are not properly examined, unexpected costs and/or schedule impacts can adversely affect the successful completion of a renovation project. This paper examines whether the cost impact of change orders due to unforeseen existing conditions is significant compared with the cost impacts of all other change orders, and quantifies the impact on the total project costs and the construction trade. Statistical analyses such as $t$-test and ANOVA were conducted, using information on 517 change orders obtained from 27 building renovation projects. The results show that change orders due to unforeseen existing conditions have significant impacts compared with change orders caused by other reasons, and that the calculated mean cost is higher for change orders stemming from unforeseen existing conditions. The results also show that the concrete trade is most impacted by change orders from unforeseen existing conditions, while the trade with the highest cost impact is heating, ventilating, and air conditioning. This paper investigates the effect of unforeseen existing conditions on the costs associated with change orders. The paper also identifies those construction trades that are more impacted than others and, therefore, require special attention. This knowledge is essential for identifying areas of possible cost overruns and minimizing overruns in building renovation projects.

This paper investigated shield machine operation (SMO) errors involved in shield tunneling construction accidents based on the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). Human errors are classified and identified at a coarse-grained task level in the TRACEr framework, which could cause failures to completely identify and analyze the human errors in a given accident. This motivated us to propose an adapted TRACEr to overcome the limitation. The adapted TRACEr incorporates hierarchical task analysis (HTA) to decompose a task into combinations of activities, which helps describe human errors at a fine-grained activity level. The connection between the added activity level and the cognitive functions was constructed according to the Phoenix method. Based on the adaptation, an activity-oriented structure of human error taxonomies was developed, and a corresponding retrospective analysis procedure that focuses on identifying errors under various construction operational situations was proposed. Based on the adapted TRACEr, SMO errors were identified and analyzed. The error taxonomies of SMO were developed, and 72 accidents were retrospectively analyzed to identify and code the errors. Data mining techniques were applied to analyze the fine-grained SMO error data to reveal the main manifestations of SMO errors and the hidden associated rules for their cognitive failures. Consequently, several targeted cognitive-based human error mitigation strategies were proposed, showing the application potential of the adapted TRACEr as a human error management tool in the construction industry.

Construction automation and robotics have been researched and developed over the last three decades. Although the practical needs keep rising, the on-site application rate remains limited. Therefore, this study aims to explore the right approach to the future development of robotics for construction and maintenance of high-rise buildings in China and the US by identifying development priorities (DPs) and key challenges (KCs) related to this technology application domain based on the perspectives of both experienced users and prominent experts. Questionnaire surveys were conducted among major construction companies in both countries to compile data on the current state of related practices, needs, and factors affecting the users in implementing construction automation and robotics for high-rise buildings. The survey results of the two countries were analyzed and compared to provide initial information for a subsequent international expert workshop. Thirteen experts in construction robotics research and development (R&D) were in attendance at the workshop in Beijing in November 2018 to discuss DPs and related KCs. Six major DPs and related KCs were identified, and their implications for future development were described. This study identified the top DPs and KCs and also proposed an effective approach for identification by considering both the practical needs and technical and economic feasibility. It is expected not only to be a valuable reference for many countries and regions where high-rise buildings are being developed but also to contribute to the knowledge body of automation and robotics for high-rise buildings.

Masons regularly perform physically strenuous and demanding duties that may exceed a safe limit. Such activities can contribute to an early retirement for masons, resulting in a shortage of skilled craft workers. Previous ergonomic studies have observed that workers develop safer and more productive work techniques as they gain experience. This study aims to analyze relationships between body loads, experience, and work methods. Specifically, we expanded a previous pilot study by increasing the number of participants from 21 masons to 66 masons. Participants completed a prebuilt standard concrete masonry unit (CMU) lead wall using 45 CMUs. Motion capture suits were used to capture masons’ motions, and a combined biomechanical-productivity analysis was carried out to determine the loads experienced by major body joints. Exploiting the larger dataset, this study assessed how different experience groups load their joints and adjust their work techniques as the work height changes. The results suggested that experienced journeymen adopt similar work techniques distinct from those of less experienced workers. Further, training apprentices to adopt these work methods can help reduce occupational injuries and improve productivity. The results show that the journeymen with more than 20 years of experience adopt safer and more productive work techniques distinct from those of less experienced workers. The present study contributes to the body of knowledge on masons’ safety and productivity by providing an in-depth understanding of the linkage between body loads, work experience, techniques, and productivity. Additionally, the findings in this study are expected to have a greater impact when they are adopted to apprentice-training methods and applied to other high musculoskeletal-disorders-risk trades.

Studies that have measured construction productivity, analyzed its influencing parameters, and explored enhancement strategies abound in the literature. However, few have quantified the relationship between the total factor productivity (TFP) of the construction industry and its parameters, nor have they provided quantitative evidence supporting the formulation and implementation of construction productivity enhancement strategies (CPES). This paper demonstrates a novel mixed-methods design that develops a systemic framework of construction TFP enhancement. The study measured the TFP of the construction industry, examined the systemic nature of its influencing parameters, explored its relationship with these parameters, and identified the dynamic nature of construction productivity enhancement measures and strategies. The developed research methods were validated and contextualized within the context of the Hong Kong construction industry which faces significant issues such as labor shortage, aging workforce, and high construction costs that beg for urgent productivity improvement. Applying the proposed methods to the Hong Kong construction industry, 15 measures and 4 strategies were identified and formulated, these strategies being to formulate a strategic plan, encourage technological innovation, build a sustainable construction workforce, and improve effective construction management. The measures and strategies form a diamond-shaped framework for systemic construction industry TFP enhancement. The measures under each strategy were illustrated by a causal loop diagram in the framework, indicating that the strategies should be adopted systematically for maximized synergies. The study contributes to the literature with its novel mixed-methods design that provides measurable evidence for developing a systemic construction TFP enhancement framework that supports industry stakeholders in effectively formulating and implementing the CPES.

After 8 years of construction, the Hong Kong–Zhuhai–Macao Bridge (HZMB) came into use in October 2018. The HZMB is currently the world’s longest oversea bridge. The HZMB is technically demanding for both the physical conditions and the stringent environmental protection measures. In addition, the involvement of three regional governments gave rise to complex coordination and communication issues. All these challenges called for supereffective project management. Notwithstanding, notable accomplishments were reported and more than 450 patents were obtained. For example, the rapid island formation technology used greatly shortened the construction period and saved cost. The project can be identified as a showcase of the latest oversea bridge construction technology. To unveil the success DNA of this record-breaking megaproject, an in-depth case study was conducted. While many reported case studies on the HZMB project have focused on the technical aspects, this case study takes a project management perspective. Through structured interviews, the HZMB project team members commented that the project reputation evaluation system (PRES) used in the project incentivized the contractors’ performance through building an interorganizational relationship (IOR). This observation is in line with the proposition advocated in the IOR literature—an enhanced relationship has a positive effect on project performance. The comment of the project team member is further supported by the analyses of 28 sets of quarterly PRES evaluation scores that are reflective of IOR determinants. This study concludes that: (1) the PRES is an instrumental project management tool, and (2) one of the functions of the PRES is building IOR. The theoretical framework and empirical evidence showcase exemplary project management. Accordingly, it is further recommended that for megaprojects: (1) monetary reward cannot be the singular performance motivator, (2) IOR maintenance should be one of the planning foci of incentivization, and (3) there is no substitute for seamless communication in project management.