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This paper reports a 6-year experience teaching building information modeling (BIM) methodology in the undergraduate/graduate architecture, engineering, and construction (AEC) program at the Pontificia Universidade Catolica do Rio de Janeiro (PUC-Rio), Brazil. This work is part of the long-term goal of developing a new BIM AEC-based curriculum track for engineering and architecture students at this university. The paper also reports the results of a survey on the student's perception on how this knowledge has affected their way of working with other disciplines and how it has influenced their professional lives. The adopted approach involved theoretical lectures, computer laboratory practice, student oral presentations, and the complete project of an enterprise. The lectures delivered by specialists with practical experience in the subject exposed the students to BIM concepts. In the laboratory, students were trained in the software tools that enables BIM activity. To develop the project, multidisciplinary teams of students were formed and each team was challenged to emulate the change in process that BIM implies. The task was to develop the complete project of an enterprise considering all different specialties involved in its design, detailing, and planning. More specifically, these specialties consisted of architecture, structural engineering, mechanical and electrical systems, and BIM management. The teams made oral presentations to the client (the professor) and consultants roughly on a weekly basis. In each year, the students' performance was evaluated in several ways. In particular, a 360° evaluation tool was used, which was crucial to provide feedback to each team member during the exercise. The culmination of this experience is the elaboration of this article that aims to describe the challenges and the successes of the adopted approach.

This paper presents a case study of a hands-on exercise to improve students' project management skills on repetitive infrastructure/modular projects. The exercise was to construct a large foam-board model of the University of Waterloo campus, involving over 40 buildings. The exercise design involved training on the integrated life-cycle decisions of a project, including quantity take-off, bidding, repetitive scheduling, assigning workers to single-skill trades, site organization, coordination, progress follow-up, and quality control. Student groups bid against each other in developing least-cost and least-waste execution plans under time and resource constraints. After execution, students gained firsthand experience on reasons for deviations and areas for improvement. Overall the exercise was fun, complemented theoretical concepts, and closely simulated how trades interact. Students also gained a better understanding of the challenges in managing repetitive projects and the role of efficient planning to reduce execution problems. The paper presents the design and implementation of this exercise, discusses its contributions to student learning, and provides guidelines to make hands-on exercises a success, particularly when a large number of participants are involved.

With the rapid advancement of Unmanned Aerial Vehicle (UAV) technologies in recent years, their uses have been increasingly adopted in the architecture, engineering, and construction industries. To satisfy the needs of various types of construction projects, a considerable amount of research work has been performed to implement and refine the operations, safety, and accuracy of UAVs. This paper presents the findings of a comprehensive literature review that focuses on UAV research in construction management during the timeframe of 2016 to 2021. A total of 95 papers were identified and collected from a list of 21 relevant journals and conference proceedings, and were then categorized by their research topic, sensor types, and targeted structures. The results of 47 exemplary studies were reported in two categories, namely UAV uses and construction uses. The research topics identified for UAV uses include algorithm, applications, operations, framework, and training, while research topics identified for construction use include inspection, surveying, safety, and monitoring. The connection between the research topics, sensor types, targeted structures, and other advanced technologies were also discussed. This paper summarizes the current results of UAV research in construction management, reviews the methodology, benefits, and limitations of the reviewed literature, and provides valuable knowledge for the future trend of UAV applications in the civil, infrastructure, and construction industries. [ABSTRACT FROM AUTHOR]

With the digital transformation of the construction industry, the need to improve construction business process collaboration and automation is increasing. However, as construction projects usually involve many stakeholders with complex relationships and insufficient mutual trust, the existing technologies cannot fulfill the requirements of the construction industry. This paper explores the integration of blockchain, smart contracts, and process automation technologies into construction business processes, shedding light on both managerial and technical challenges. The study introduces a comprehensive technical framework meticulously designed to align with the intricate nature of construction practices while focusing on the resolution of legal complexities associated with smart contract applications and the facilitation of process collaboration and automation. The framework comprises three core modules: (1) action definition and extension to process models, (2) standardized mapping for automatic smart contract generation, and (3) smart contract visualization for reliable process collaboration and automation. This multifaceted approach caters to the specific needs of construction management, standardization, interoperability, and visualization. The framework's practicality is further evaluated through real-world testing within a construction payment case, effectively showcasing its efficacy and applicability in tangible business scenarios. While this paper represents a significant step forward in addressing construction business process collaboration and automation challenges, it acknowledges the necessity for ongoing research and development to refine and expand these innovative solutions to meet the evolving demands of construction management. Practical Applications: Currently, there is a significant escalation in the severity of disputes within the construction industry. The adoption of blockchain technology allows for the redistribution of trust within a construction project, shifting reliance from individuals to the system itself for better collaboration and fewer disputes. Although blockchain can help foster a more secure and transparent environment, at the process level, an efficient workflow engine is needed to activate processes in this environment. Therefore, this study proposed an approach to generating smart contract codes based on construction business processes in a standardized way and further visually executing these codes for the processes with historical transactions stored in blockchain. This approach can reduce the effort of smart contract programming and improve the standardization and credibility of smart contract generation. It can also streamline the procedure from construction business process design to final execution via predefined and visual modules. Meanwhile, the generation prototype is open sourced to provide transparency and further enhance the approach. To indicate practical implications in the real world, this study provided a case of construction payment, but the solution is not limited to the payment scenario. [ABSTRACT FROM AUTHOR]

Practitioners in construction management primarily focus on two key indicators of project success: total cost and completion time. Heavy equipment and machinery play pivotal role in determining these measures, representing significant cost elements in various heavy construction projects such as road construction. Consequently, there is a pressing need for an efficient approach to determining the optimal scheduling of these heavy resources to minimize costs and shorten completion times. This paper proposes an innovative approach to address this challenge by introducing a mixed integer linear programming (MILP) model. The aim is to identify the optimal configuration for heavy equipment in earthmoving operations. The dynamic nature of the configuration process is adopted, enabling daily updates to the schedule based on the contractor's available resources. Moreover, environmental considerations are integrated into the decision-making process, ensuring a comprehensive approach to project optimization. To demonstrate the superiority of the developed model, three case projects from the literature have been solved. The proposed model led to a significant improvement in project cost, with an average enhancement of 25%, and in completion time, with an average improvement of 50% compared with the literature case studies. Practical Applications: This paper presents a novel MILP model designed to optimize earthmoving operations, focusing on dynamic fleet configurations and emission costs. Unlike existing models, this approach provides daily fleet setups for multiple cut and fill sites, considering the contractor's available resources. It calculates optimal soil quantities to be moved, monitors soil levels at sites, and estimates daily trips between them. In the realm of project bidding and management, this model offers valuable insights and practical applications. It empowers project managers with a robust tool for optimizing fleet configurations during bid preparation, enabling contractors to determine the most cost-effective and time-efficient setups, enhancing their competitiveness. Moreover, the model facilitates the assessment of different parameters' impacts, such as resource additions or equipment upgrades, on project cost and completion time. This dynamic approach enables contractors to make informed decisions during project execution, ensuring projects remain on track and within budget. Additionally, by considering emission costs, the model aligns with the growing emphasis on sustainability in construction projects. Optimizing fleet configurations to minimize emissions not only reduces environmental impact but also helps contractors comply with stringent regulations and meet client demands for eco-friendly practices. [ABSTRACT FROM AUTHOR]

The current paper presents a comprehensive review analysis of Multi-agent control methodologies for Integrated Building Energy Management Systems (IBEMSs), considering combinations of multi-diverse equipment such as Heating, Ventilation, and Air conditioning (HVAC), domestic hot water (DHW), lighting systems (LS), renewable energy sources (RES), energy storage systems (ESS) as well as electric vehicles (EVs), integrated at the building level. Grounded in the evaluation of key control methodologies—such as Model Predictive Control (MPC) and reinforcement learning (RL) along with their synergistic hybrid integration—the current study integrates a large number of impactful applications of the last decade and evaluates their contribution to the field of energy management in buildings. To this end, over seventy key scholarly papers from the 2014–2024 period have been integrated and analyzed to provide a holistic evaluation on different areas of interest, including the utilized algorithms, agent interactions, energy system types, building typologies, application types and simulation tools. Moreover, by analyzing the latest advancements in the field, a fruitful trend identification is conducted in the realm of multi-agent control for IBEMS frameworks, highlighting the most prominent solutions to achieve sustainability and energy efficiency. [ABSTRACT FROM AUTHOR]

To measure the effectiveness of learning activities, intensive research works have focused on the process of competency building through the identification of learning stages as well as the setup of related key performance indictors to measure the attainment of specific learning objectives. To organize the learning activities as per the background and skills of each learner, individual learning styles have been identified and measured by several researchers. Despite their importance in personalizing the learning activities, these styles are difficult to implement for large groups of learners. They have also been rarely correlated with each specific learning stage. New approaches are, therefore, needed to intelligently coordinate all the learning activities while self-adapting to the ongoing progress of learning as well as to the specific requirements and backgrounds of learners. To address these issues, we propose in this paper a new framework for an intelligent management of the competency building process during learning. Our framework is based on a recursive spiral Assess-Predict-Oversee-Transit model that is orchestrated by a multi-agent system. This system is particularly responsible of enabling smart transitions between learning stages. It is also responsible of assessing and predicting the process of competency building of the learner and, then, making the right decisions about the learning progress, accordingly. Results of our solution were demonstrated via an Augmented Reality app that we created using the Unity3D engine to train learners on Air Conditioner maintenance. [ABSTRACT FROM AUTHOR]

Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge. [ABSTRACT FROM AUTHOR]

This paper investigates the Building Information Modeling (BIM) adoption impact on the factors of Contract Cost Variance (CCV) over time. The study considers qualitative and quantitative data to identify the most common causes of CCV through pre-tendering. A partial least square-structure model (PLS-SEM) procedure was used to develop a causal model and rank CCV factors based on their effect, partially based on prior survey raw data conducted in 2022 and the data from 94 projects. Construction industry experts assessed the prior five-year rate of BIM adoption on construction projects to infer the expected trend in BIM adoption in the future (until 2037). Based on the causal model of CCV factors and the future rates of BIM adoption, the dynamic impact of BIM on CCV factors over time was modeled and analyzed. The analysis shows that BIM reduces CCV over time by improving Estimator Performance (EP), Information Quality (IQ), and contractual procedure (CP). The results showed that the CP, EP, and EF have directly impacted CCV, and the PC and IQ indirectly affect the CCV. This paper considers the temporal aspect, examining how the impact of BIM on CCV factors evolves. This dynamic analysis is crucial for long-term strategic planning in construction management. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, the importance of risk management in underground construction projects has become increasingly prominent. In the process of risk assessment for underground construction projects, the uncertainty of subjective factors from experts poses a significant challenge to the accuracy of assessment outcomes. This paper takes a section of the Nanchang Metro Line 2 as the research object, aiming to address the subjectivity issues in the risk assessment of underground construction projects and to enhance the scientific rigor and accuracy of the assessment. The study initially conducts a comprehensive identification and analysis of risk factors in underground engineering through a literature review and expert consultation method. Based on this, this paper introduces the theory of Pythagorean fuzzy sets to improve the Delphi method in order to reduce the impact of subjectivity in expert assessments. Furthermore, this paper constructs a Bayesian network model, incorporating risk factors into the network, and quantifies the construction risks through a probabilistic inference mechanism. The research findings indicate a total of 12 key risk factors that have been identified across four dimensions: geological and groundwater conditions, tunnel construction technical risks, construction management measures, and the surrounding environment. The Bayesian network assessment results indicate that the effectiveness of engineering quality management and the state of safety management at the construction site are the two most influential factors. Based on the assessment results, this paper further conducts a risk control analysis and proposes targeted risk management measures. [ABSTRACT FROM AUTHOR]

Lean practice is recognised for having a great potential in promoting safety risk management in off-site construction (OSC). This paper presents results of a study conducted to assess the impact of lean practice on safety risk management in OSC in a developing country. A quantitative approach using a survey-based questionnaire was adopted. Lean management practices (LMPs) identified from a literature review were empirically tested using a sample survey of 103 OSC contractors. The survey responses were subjected to descriptive and inferential statistics. The top ranked LMPs for safety risk management in OSC included two mistake-proofing practices, i.e., use of personal protective equipment (PPE) and use of hazard warning equipment; two last planner system (LPS) practices, i.e., involvement of workers in safety planning and providing necessary working equipment; and one first run studies (FRS) practice, i.e., critical analysis of work methods. These LMPs are useful in controlling high-consequence safety risks in OSC. Based on evidence found in this study, the paper argues that lean practice can bring great value to safety risk management in OSC in countries where OSC is transitioning. [ABSTRACT FROM AUTHOR]

Construction waste detection under complex scenarios poses significant challenges due to low detection accuracy, high computational complexity, and large parameter volume in existing models. These challenges are critical as accurate and efficient detection is essential for effective waste management in the construction industry, which is increasingly focused on sustainability and resource optimization. This paper aims to address the low accuracy of detection, high computational complexity, and large parameter volume in the models of construction waste detection under complex scenarios. For this purpose, an improved YOLOv8-based algorithm called FE-YOLO is proposed in this paper. This algorithm replaces the C2f module in the backbone with the Faster_C2f module and integrates the ECA attention mechanism into the bottleneck layer. Also, a custom multi-class construction waste dataset is created for evaluation. FE-YOLO achieves an mAP@50 of 92.7% on this dataset, up by 3% compared to YOLOv8n. Meanwhile, the parameter count and floating-point operations are scaled down by 12% and 13%, respectively. Finally, a test is conducted on a publicly available construction waste dataset. The test results demonstrate the excellent performance of this algorithm in generalization and robustness. [ABSTRACT FROM AUTHOR]

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Purpose: Climate change and global warming have increased concerns over the influence of weather on workers' health and productivity in construction projects. A significant number of studies can be found in the weather and productivity interplay area. The purpose of this paper is to review the recently published papers in this area to explore the trends of research and topics discussed and to determine knowledge gaps and directions for future research. Design/methodology/approach: Recent papers published between 2014 and 2019 were synthesized, reviewed and analyzed using bibliometric and text mining analysis. Findings: The results revealed the trends of publications, the main authors contributed to this area and countries that attracted most of the research papers. Based on the review, this study presented a taxonomy of studies consisting of seven clusters, namely productivity management, seasons, weather factors, participants' conditions, uniform and clothing, work time and health and safety. Originality/value: This review paper sheds a light into the topics discussed in this area, the interrelationship between the topics and the significant topics that should be continued in the future. Global warming concerns necessitate the need for more studies in tropical countries and countries that are expected to expose to high temperatures and heat stress, which greatly impact labor productivity. The paper highlighted the need to understand how weather influences workers' psychological conditions and subsequently their productivity. [ABSTRACT FROM AUTHOR]

This study explores the classics that significantly contribute to the research of construction engineering and management (CEM). Previous studies usually simply applied the number of citation to identify the classics, causing some flaws. To overcome the flaws, an advanced approach is developed by integrating scientometric methods (G-index and co-citation analysis) and a social network analysis (SNA) technique (modularity optimization algorithm), thus providing more precise and persuasive results that denote what academic works have made significant inspirations and illuminations on CEM research. This study retrieves 13,273 CEM literature and extracts 336,129 bibliographies from these literature. Based on the G-index, a total of 67 publications are identified as CEM classics. Moreover, this paper measures and maps the structure of the classics by using co-citation analysis and modularity optimization algorithm. The results provide a basic source of academic information representing the foundation of CEM and draw a big picture of CEM to show the underlying associations between the identified classics. This can help researchers recognize the key scientific contributions for improving the academy progress [ABSTRACT FROM AUTHOR]

INTRODUCTION: Objective, accurate and fair development of research and effective performance prediction methodology for the construction of the talent team is the current needs of the new era of innovation and reform and development of university management, as well as the need to improve the quality of scientific research and teaching level of the talent team. OBJCTIVES: To address the problems of irrational principle of indicator selection, incomplete system and imprecise methodology in the current research on performance prediction of talent team building. METHODS: This paper proposes a talent team construction performance prediction method based on intelligent optimization algorithm improving neural network with integrated learning as the framework. First of all, through the analysis of the current talent team construction performance prediction influencing factors selection principles, analyze the talent team construction performance management process, select the talent team construction performance prediction influencing factors, and construct the talent team construction performance analysis system; then, with the integrated learning as a framework, improve the neural network through the internal search optimization algorithm to construct the talent team construction performance prediction model; finally, through the simulation experiments to analyze and verify the effectiveness and superiority of the proposed method. The effective type and superiority of the proposed method are verified. RESULTS: The results show that the proposed method satisfies the real-time requirements while improving the prediction accuracy. CONCLUSION: This paper addresses the lack of precision in forecasting the performance of the talent pipeline and the lack of a sound analytical system. [ABSTRACT FROM AUTHOR]

Determining whether to invest in a project is a complex and challenging decision. This project investment decision relies heavily on its promised strategic goals (i.e., target benefits) stated in its business case and which are expected to be realized following project completion. However, in many cases, the proposed project benefits are far greater than those that the project realistically can achieve, leading to the risk of projects being funded erroneously. The construction management literature has offered approaches such as reference class forecasting (RCF) to rectify unrealistic project estimates. This paper identifies weaknesses in the current approaches, exposes their inefficiency, and proposes a step-by-step stratified approach as an effective alternative to detect, measure, and rectify unrealistic estimates of project benefits, and utilizes the proposed approach on a light rail construction project. The contribution of this approach is that each project benefit target can be rectified differently, based on the measurable levels of deviation from more-realistic estimates. This paper advances the construction management literature by enabling managers to set realistic project goals in the project front end, increase the reliability of estimates in the project business case, and improve the quality of project investment decisions. The decision to fund a construction project is impacted by information included in business cases and proposals, such as the estimated project cost and expected goals. However, such information often is unrealistic or exaggerated (to make the project more attractive for funding), which negatively impacts investment decisions made by senior executives. As a result, projects can be funded erroneously, causing a waste of resources and potential failure to achieve its unrealistic expectations. Because the existing literature offers little guidance for how to ensure that project goals are realistic, this paper developed and illustrates a step-by-step process for measuring and rectifying the gap between proposed and realistic project goals to alert senior executives when a proposed project is unlikely to meet its proposed goals. [ABSTRACT FROM AUTHOR]

In papers published in Journal of Civil Engineering and Management, many applications of fuzzy logic can be found. To commemorate the contributions of the fuzzy set theory proposed by Professor Lotfi Zadeh to the fields of civil engineering and construction management, seven papers were selected for this special issue. These papers combined fuzzy set theory with appropriate methods to solve specialized problems in the fields of civil engineering and construction management, and provided good insights for the applications of fuzzy technology in solving problems related to civil engineering and construction management. [ABSTRACT FROM AUTHOR]

This paper sought to discuss entrepreneurial learning competencies by determining the outcome of entrepreneurial learning on the views of built environment students in the university setting. In this study, three relevant competencies were identified for entrepreneurial learning through literature, namely: entrepreneurial attitude, entrepreneurial skills and knowledge of entrepreneurship. On this basis, questionnaire was designed and administered to graduate students in built environment. In all, a total of 124 questionnaires were administered to respondents. Out this, 84 were retrieved representing a response rate of 68% and were further subjected to analysis using Relative Importance Index (RII). The findings from the study highlighted on competencies factors that have great impact on entrepreneurs in dealing with tasks and problems related to entrepreneurial learning processes. These key entrepreneurial competencies as perceived by the built environment students were ranked as: "entrepreneurial attitude," "knowledge of entrepreneurship" and "entrepreneurial skill." The findings may help stakeholders in the building industry including up-coming graduate students. Thus, it could help in their journey into entrepreneurial terrain affiliated to advancement of their career, as a way to increase private wealth and the pursuit of a more balanced life.