Journal of Construction Engineering and Management

Current precast production scheduling methodologies often have limited applicability in practice largely due to the limited consideration of human factors and interactions between the scheduling and workforce development. This study systematically integrates human factors in the form of worker competence into production scheduling. More specifically, the factors of worker learning rate, professional skills, experience in each type of operation, and operation specifications are all formulated to determine the duration of each process in precast production. A two-hierarchy scheduling optimization model (WCSO_PP) considering worker competence is proposed to synchronize the production scheduling with workforce development and to optimize the scheduling from both the short-term (time-oriented) and long-term (competence-oriented) views. Finally, the performance of the proposed model is validated using a real case study. The WCSO_PP model can achieve 74.09% and 73.01% cost savings in short-term and long-term scenarios, respectively. The developed model addresses the research gap in modeling the impact of human factors on the production, contributing to more reliable scheduling and enhancing the long-term human resource development.

Prefabrication of steel reinforcement (rebar) assemblies can reduce construction costs and time while improving fabrication quality and safety. However, changes in rebar spacing (i.e., shifting of rebar from their original tied positions) can occur during the transportation and final placement of these assemblies, potentially leading to violations of code-required spacing tolerances and changes in structural behavior. The primary objective of this paper is to investigate these spacing changes. Many factors and conditions can contribute to the shifting of rebar during the transportation and final placement of prefabricated rebar assemblies. Specifically, this paper describes the effect of tripping of horizontally prefabricated rebar assemblies on rebar spacing, focusing on heavy construction in nuclear safety-related reinforced concrete (RC) shear walls. Two-dimensional rebar mats and three-dimensional rebar cages are prefabricated horizontally at grade by professional ironworkers, laterally translated, and tripped to a vertical position to simulate the construction sequence based on the results from an industry survey. Relative movements between the bars are measured using the photographic measurement technique digital image correlation and compared to code-required tolerances. As a main finding, the largest movements are measured in horizontal bars directly involved in lateral translation and tripping. Additionally, it is observed that diagonal bracing is more effective in restricting bar movement than is increasing the percentage of tied intersections. Number of rebar layers, type of transverse reinforcement, and rebar cage thickness are found to have no significant effect on bar spacing changes. As a major contribution to construction engineering practice, this is the first study to investigate the movement of rebar in prefabricated rebar assemblies during tripping and to make quality control recommendations for these assemblies.

Project progress and performance assessment is critically important to the successful delivery of capital facility projects. However, there is no standardized approach for the selection and use of project control metrics, making it difficult to analyze project progress and performance for transforming data into meaningful insights. This research identified core predictive and diagnostic metrics that may provide actionable insights into a project’s actual progress, performance, and forecast at completion. The methodology used for identifying these metrics included a literature review, surveys, expert evaluation utilizing the Delphi method, and statistical validation. The researchers analyzed 44 surveys and collected multiple rounds of responses from 16 subject matter experts to validate the findings. Results indicated there are 20 core metrics, seven validation metrics, seven innovative metrics, and 14 other significant metrics, which can be used for multiple project types, sizes, and contracting strategies. Statistical analyses of the survey data were used to further validate the core metrics and demonstrated that use of more core metrics corresponded with project cost performance and using more diagnostic metrics in projects led to better schedule performance.

The issue of low productivity has remained a very serious and chronic problem in construction projects. While previous studies have identified several factors that cause low construction productivity, factors related to site amenities and labor welfare measures have not received enough attention. This paper encompasses factors related to site amenities and labor welfare that directly or indirectly affect productivity of the construction workforce. Factor analysis conducted on 151 responses associated with 38 identified site amenities and labor welfare attributes identified eight factors: (1) health and medical provisions, (2) site services, (3) labor camp facilities, (4) hygiene and sanitation, (5) leave and benefits, (6) social welfare and employment policies, (7) remuneration, and (8) accommodation facilities. One-way ANOVA was performed to compare the views of the engineers and supervisors. Furthermore, data were collected from five different construction sites to assess the quality of the prevailing conditions of site amenities and labor welfare and its implications for labor productivity. It was found that the major reasons leading to poor quality of site amenities and labor welfare are related to improper letters of intent (LOIs) and delays in land acquisition for setting up proper labor camps. The outcome of this study is likely to direct more research and organizational efforts in this direction, especially in the context of developing countries such as India, where work and living conditions of construction workers have yet to receive the required attention of both policymakers and project stakeholders.

A conceptual cost estimation is prepared to assess the feasibility of a project or establish the project’s initial budget at the early stages of the project. The main objective of the paper is automating the cost estimate at the conceptual stage with the highest accuracy. The key contribution of this paper is developing a quadratic regression model with a prediction accuracy of 9.12% and 7.82% for training and validation, respectively. This research has identified the model’s key parameters to establish a reliable conceptual cost estimate model for field canal improvement projects (FCIPs). Two machine learning models were developed utilizing multiple regression analysis (MRA) and artificial neural networks (ANNs). Searching for a better model, several data transformations have been conducted to improve the model performance. The quadratic regression model has shown the highest performance based on the correlation and the mean absolute percentage error (MAPE) criteria. A parametric model has been presented in this paper to predict the conceptual cost of FCIPs. This research maintains the importance of identifying key parameters and conducting data transformation and sensitivity analysis for developing a reliable parametric cost prediction model.

Construction project managers (CPMs) of contractors experience a great deal of stress. In this study, the authors used job burnout as a core indicator of the chronic psychological stress of CPMs. Based on the job demand-resource model, a theoretical framework was proposed to investigate the causal path of job burnout of CPMs, especially the role of organizational justice. A questionnaire with a presurvey was developed referencing various psychometric and organizational behavior scales. Then, in 2017, a survey was conducted throughout China through face-to-face interviews, e-mails, and an online questionnaire system. The validity of the measurement was tested with confirmatory factor analysis, and the hypothetical model was validated with structural equation modeling. Subsequently, 14 mediating effects and 2 moderating effects of organizational justice on job burnout were identified. The results showed that different organizational justice dimensions have different pathways in the causal chain of job burnout of CPMs. Temporal and spatial injustice was most prominent and led to exhaustion of CPMs. Through mediating paths, distributive justice had the most significant impact on the cynicism and low professional efficacy of CPMs, while procedural and interactional injustice resulted in their exhaustion. In addition, procedural and interactional justice buffered the impact of job stress on job burnout.

The construction industry utilizes many materials that go through different manufacturing and transportation processes. Selecting and using environmentally friendly or green building materials is one way to reduce the inimical environmental effects associated with the construction industry. However, environmental preference cannot be used as the only criterion in selecting a building material. As cost is an important factor, environmental performance needs to be balanced against the economic performance. The decisions on material selection, therefore, need improvement. This study analyzed and compared the environmental and economic performance of asphalt shingle and clay tile roofing materials using the building for environmental and economic sustainability (BEES) model. The results were then used to develop a combined performance score using the technique for order preference by similarity to ideal solution (TOPSIS) to assist decisions pertaining to the selection of the best material based on the performance score. To present another decision criteria to compare and select a roof material, the study utilized the contingent valuation method (CVM) to estimate the environmental cost associated with these alternative materials. The simulation results showed that clay tile had a better environmental performance score of $0.3525\mathrm{points}(\mathrm{pts})/\mathrm{unit}$ compared to the score of $0.4135\mathrm{pts}/\mathrm{unit}$ for asphalt shingles; however, asphalt shingles had a better overall performance score of 0.5985 pts against 0.4153 pts for the clay tile. Merging environmental and economic considerations to a common score as presented in this study will help improve and contribute to the quality of decision making in that it provides a substantial basis to select the most suitable, cost-effective roofing material.

BIM has been undergoing continuous growth in the global architecture, engineering, and construction (AEC) industry. However, knowledge development within BIM management is lagging behind its implementation. This study initiates a BIM management–based framework involving BIM climate, which is measured by individual BIM practitioners’ perceptions. Subgroup comparison is highlighted in measuring perceptions. Regional variance in BIM climate is addressed in applying the framework by adopting an empirical case study within the context of China’s AEC industry. The case study uses Shanghai and Wenzhou, which represent a BIM-leading metropolitan city and a BIM-developing counterpart, respectively, for the comparative analysis of BIM climate. Based on data collected from a questionnaire survey sent to BIM practitioners from these two cities, it is revealed that Shanghai, as the BIM-leading city in China, has somewhat significant differences in BIM climate compared with Wenzhou. For example, Shanghai BIM practitioners perceive fewer challenges in BIM training, but higher risk in adopting BIM technology. This study contributes to both academic work and practice in BIM based on its initiation of the concept of BIM climate and the case study of BIM-climate comparison. Academically, this holistic study proposes the BIM management–related knowledge framework aiming to fill the knowledge gap in BIM climate and culture, and it could be further applied in subclimates and subcultures within BIM. Practically, the case study provides insights to stakeholders regarding regional variations in BIM climate when promoting BIM practice or establishing BIM guidelines.

The majority of qualitative risk assessment studies on construction projects assess manageability indicators (e.g., detectability, preventability, and controllability) independently of risk probability and impact, two main risk parameters. This study develops a model for assessing the risk manageability indicators by linking manageability to the chain of risks. Such risk manageability indicators may improve risk response plans. The chain of risks in the change-formation phase is used as the basis to determine why any change in a construction project is generally caused by a chain of risks. Focus is directed to the chain of risks in the change-formation phase that determine part of the probability of change. In this phase, final risks (causes) that correspond to uncertain events occur because of the presence of sources and drivers. This model defines a risk scenario as all paths between a source and a risk (cause). Drivers in the paths between sources and final risk are the signs of risk scenarios. This study evaluates the manageability indicators by using the observability of drivers of the chain of risks to define and assess two indices of detectability and trackability as two main components of the developed framework. These indices are used to improve the response plan to mitigate risk. Finally, to demonstrate the framework capabilities, a real construction project as an application example is analyzed. This research contributes to the existing body of knowledge through evaluating the manageability of risk scenarios by defining the relationship between risk sources and risk signs (drivers). It also helps to expand existing knowledge by developing an approach for identifying and mitigating risk causes after the observation of risk signs. This evaluation may alert project management teams in advance of risk occurrence so that they can take necessary actions to reduce the probability of the risk. The results indicate that a project team should be aware that after observing a driver (risk sign) in a project, parts of previous paths in which no driver is observed may lose their importance, and that the importance of some paths in which observed drivers exist intensifies. Moreover, a project team’s observations may sometimes reduce the importance of remaining paths and direct the team to concentrate on taking appropriate measures to enable immediate prevention from a scenario’s endpoint (cause).

Unbalanced bidding has presented a significant issue in the construction industry. The award of a contract to an unbalanced bid poses a significant threat to the construction cost, as well as construction activities. The nature of an unbalanced bid, irrespective of the form, consists of quotations higher or lower than the reasonable unit price. In this paper, the engineer’s estimated price was chosen as the evaluation criterion. Consequently, a model was developed in order to identify unbalanced bids based on unascertained model. The unbalanced bid can be identified, which provides useful inputs for bid evaluation. As a result, unbalanced bids can be prevented. The feasibility of the method was verified via a case study. The method proposed in this paper facilitates an effective and efficient quantitative assessment of unbalanced bids. This model not only assists owners in effectively and efficiently identifying unbalanced bids, but also discourages bidders from unbalancing their bids.