Journal of Construction Engineering and Management

Architecture, engineering, construction, and operation (AECO) organizations must adopt building information modeling (BIM) technology, define workflows, and develop internal policies to deliver BIM projects successfully. AECO projects are known as BIM projects when BIM is used during such projects. Organizations must identify project objectives, critical success factors (CSFs), and operational critical success factors (OCSFs); set performance measures; and perform assessments to verify if they are progressing toward success. OCSFs are the factors that can drive the CSFs such that operational issues are well resolved by the project stakeholders. There are several BIM standards and guidelines to appropriately diffuse BIM technology and workflows. However, there are few holistic approaches to identify OCSFs. To meet this necessity, a step-by-step procedure is proposed with an empirical approach to define project objectives, CSFs, and OCSFs. The framework of influencing factors (IFs) is developed by organizing the wide range of IFs from the literature. As a case study, the proposed approach is used to identify OCSFs for Taiwanese BIM projects. It is not only generally applicable to identify OCSFs in other countries, but also capable of helping a small team of BIM experts to identify OCSFs. The developed framework of IFs can be adjusted to fit the needs of design, construction, and operation organizations by retaining the necessary factor groups in the framework.

On-site transport is common in most construction projects. Confined by site sizes and construction schedules, many construction tasks are implemented simultaneously within limited spaces; therefore, the paths necessary for the transport of site objects are likely to be obstructed. To solve this problem, in this research, we propose a cell-based methodology for path obstruction detection and conduct a test with a prototype to determine its accuracy and time requirements. The results show that, by choosing a proper cell step, the method can determine whether a site object’s transport path is obstructed and can generate a suitable path if there is no path obstruction; otherwise, the potential obstacles can be listed and ranked based on their distances to the site object. The proposed method has the potential to support path obstruction detection in four-dimensional (4D) building information modeling (BIM) to improve the implementation of construction plans.

The resource-constrained project scheduling problem (RCPSP) aims to arrange activities to meet resource restrictions. In real life, not all resource types are constrained, nor do all activities face resource restrictions. The RCPSP may emerge in parts of the project, which could be named a partial RCPSP. A fundamental principle for RCPSPs is to sequence parallel activities to sequential ones under resource restrictions. This paper considers a typical partial RCPSP with generalized precedence relations (GPRs) and transforms it into a sequence problem that specifically sequences $N$ given parallel activities to $H$ sequential activity pairs with minimal impact on the project duration. The characteristics of activity time parameters are used to quantize the impact of sequencing parallel activities on the project duration. Furthermore, a 0-1 formulation model is presented for the partial RCPSP with GPRs, which includes only the $N$ given parallel activities instead of all activities. The performance of this formulation is evaluated, and detailed computational results are presented for the sequence problem that evaluate the efficiency and competitiveness of this procedure. The obtained results may also serve as theoretical tools for other partial RCPSP with GPRs and may further improve project scheduling.

Variation in production is a major cause of producing waste in onsite construction operations, resulting in a workflow that is full of delays and interruptions. To reduce the negative impact of waste in construction, production managers need to address the causes of the variation that results in such waste. This paper explains the way to reduce the effects of variation in construction by changing the task sequence arrangement. The study analyzed the effect of some different task sequence arrangements on the production gap, crew waiting time, and production delay by simulating a group of work tasks and changing the task sequence arrangement from linear to parallel. Accordingly, 100 work activities were simulated in 98 different sequence designs, using a stochastic discrete-event simulation model, during which the number of parallel activities was systematically increased. The main finding from the studied configuration is that arranging tasks in parallel increases waste, whereas it reduces project duration. Moreover, waste resulting from variation was found to be an additional cause of waste when accelerating the production. Finally, it was revealed that the impact of variation on the task sequence highly depends on how often the schedule is updated. This study helps production managers to better understand of how the task sequence arrangement affects production performance in onsite construction operations.

Factors related to team behaviors (TBs) have been recognized as critical success factors (CSFs) of a project. Numerous studies on the topic of CSFs have been conducted, but the results have rarely emphasized developing a TB framework for construction project organizations and examining its relationship with client satisfaction, a key criterion for measuring project success; these less-researched topics are the aims of this study. TB attributes were first developed using questionnaires that collected data on 195 completed construction projects in Vietnam. By performing a principal component analysis, these attributes were organized into a four-factor TB framework: (1) project planning and organizing emphasis (P&OE); (2) coordination emphasis (CE); (3) contractor assurance emphasis (CAE); and (4) empowerment assignment emphasis (EAE). The findings reveal that P&OE, CE, and CAE have significant effects on client satisfaction with project quality (SPQ), whereas CAE and EAE contribute to improved client satisfaction with project schedule (SPS) and project budget (SPB). In addition, CAE is shown to be a relatively significant influencing factor for all criteria within client satisfaction. The study findings suggest a useful tool both for supporting the project management process of construction professionals and for improving client satisfaction.

Construction companies continuously seek to improve risk analysis techniques to determine the contingency of projects. Construction risk assessment relies on a group decision-making (GDM) process, in which a heterogeneous group of experts provides their opinions to determine the probabilities and impacts of project risks. In this paper, risk probabilities and impacts are expressed as linguistic terms, which are then represented by fuzzy sets to account for the uncertainty in these assessments. Current GDM processes help experts to obtain collective agreement through the use of a consensus-reaching process, which has several limitations, such as being a time-consuming procedure. The main contributions of this paper are to introduce a list of criteria and a set of metrics to evaluate risk-assessment expertise. Additionally, this paper discusses the development of a method for weighting the importance of experts’ opinions according to their expertise levels. This research will also serve to improve GDM processes in construction risk assessment by introducing a structured framework that combines assessments from a heterogeneous group of experts through aggregation.

Knowledge management for construction accident cases can identify dangerous conditions and prevent accidents by controlling risks on-site. However, because accident cases are recorded as unstructured text data, significant time and effort are required to retrieve and analyze the knowledge a user wants. To overcome these limitations, this research proposes a knowledge management system for construction accident cases using natural language processing. For this purpose, two models were developed that can retrieve appropriate cases according to user intentions and automatically analyze tacit knowledge from construction accident cases. In the retrieval model, the query is expanded using a construction accident case thesaurus. Ranking is calculated using Okapi BM25 and weighting according to the thesaurus. In the analysis model, knowledge is automatically extracted using rule-based and conditional random field (CRF) methods. The proposed system can retrieve results that are 97% relevant to the accident cases the user intended and can automatically analyze knowledge with accuracies of 93.75% and 84.13% for the rule-based and CRF models, respectively. The results demonstrate the potential of knowledge discovery from accident reports for more-effective safety management.

Safety leading indicators are measures of the safety management system that correlate with injury rates. The literature on the topic is dispersed and equivocal in the definition, categorization, and measurement of candidate indicators, which makes validation and replication difficult. This study includes a comprehensive review of safety leading indicator research, offers a distinction between leading indicators and other methods of safety prediction, and defines a clear method for distinguishing between active and passive indicators. By applying these definitions and leveraging empirical data, a statistical meta-analysis was performed to compute the relative effect sizes and significance for all salient indicators. Although active leading indicator research is rare and relatively recent, the meta-analysis indicates that inspections and pretask safety meetings correlate strongly with near-term project safety performance. Passive leading indicator research is relatively common and has been conducted for several decades. The results of the meta-analysis indicate that implementing safety recordkeeping, safety resource, staffing for safety, owner involvement, safety training/orientation, personal protective equipment, safety incentives program, and safety inspections and observation each improves long-term safety performance. The findings validate suspected leading indicators and serve as a first step toward standardization. Practitioners may use the findings to justify and target resource expenditures using pervasive scientific evidence.

Non-hard-hat use (NHU) is related to many construction accidents, so NHU inspection is crucial to safety management, in which automatic NHU monitoring plays an essential role. Existing computer vision–based NHU inspection methods lack capabilities in identifying workers and helping take real-time action. Previous sensor-based NHU inspection methods require direct skin contact, which would be uncomfortable for workers. In addition, previous sensor-based methods could be deceived by objects other than human heads and could not achieve real-time alarms. This study aims to address these problems by implementing real-time alarming, monitoring, and locating for NHU in construction based on sensor, mobile, web, and cloud techniques. A smart hard-hat system is developed using an Internet of Things (IoT)-based architecture including (1) a hard hat with an infrared beam detector and thermal infrared sensor for nonintrusive NHU detection; (2) radio-frequency identification (RFID) triggers for locating NHU with an average detection error of less than 10 cm; (3) a smartphone application for personalized warnings; (4) a web application for data visualization and alarms for managers; and (5) a cloud sever for data storage and retrieval. The proposed system enables both workers and managers to take timely actions against NHU. The system performance is evaluated in a laboratory test and validated in a field application. It is indicated that the proposed system is accurate and reliable, showing potential to promote safety inspection and supervision in construction.

There are many studies on interorganizational projects (IOPs). Nonetheless, only a small portion of the literature examines the relationships among the different participants. This study investigates how relational characteristics influence the success of wind power generation projects. To do so, five relational characteristics were considered: interdependence, intermediation relations, collaborative relations, control of relations, and volume of relations. The research adopted a single case study approach of a wind power generation project in the Brazilian electricity industry. The results showed the positive effect of relational characteristics on overcoming obstacles that emerged during implementation and thus on the success of interorganizational projects in similar contexts. Implications for management include the importance of broadening the responsibilities of engineers and managers of large projects to extend to the management of relations. New skill sets may be required, including interorganization communication and trust; process integration coordination through synchronizing collaborative activities, information, and decisions; and understanding of interorganizational complexity. For the effective coordination of the project network, actors must have both social and technical legitimacy.

For many years, project delivery methods have been studied in the construction industry to assess their impact on project performance, but few extend that focus to the effect of team composition and organization. However, many factors influence the need for integrated teams, as project needs change and new participants are added to the project. The objective of this study is to identify key organizational practices for the use of cross-functional teams in construction. Using a combination of expert interviews and case study observations, this paper presents the practices captured regarding the design of cross-functional teams and demonstrates how the case study project addressed each of the identified practices. Despite the limitations of generalizing findings inherent to a case study approach, it was necessary due to the limited opportunities to study integrated project delivery (IPD) projects. Cross-functional team performance in the case study has been assessed primarily by the capacity to meet commitments, both schedule and financial. The discussion reviews both the potential and the challenges for cross-functional team application outside of IPD contracts. This study finds that project teams should form cross-functional teams at the very early stages of the design in conjunction with a target value design approach. The cross-functional teams should engage interdisciplinary teams that focus on the main building or facility systems. While the number of teams varies with the project complexity, scope, and scale, the number is typically four to eight on most projects, and rarely more than 10. The suggested cross-functional team size is four to six people, with some variance based on the objectives and tasks they undertake. Leadership that brings a combination of cross-disciplinary knowledge to communicate effectively and strong facilitation skills, as well as the ability to coordinate with other cross-functional teams, is needed. Finally, the cross-functional organization combined with the IPD contract structure creates flexible capacity to allow the teams to evolve with the project needs, and to leverage the full potential of individuals to best suit the changing project dynamics with little administrative burden from contractual constraints inherent in other contractual arrangements.