Pavement defect detection requires both high accuracy and real-time performance in complex road environments, yet existing lightweight models often struggle with blurred textures, background interference, and small cracks. To address these limitations, this study proposes YOLOv11n-CDL, an enhanced lightweight detector integrating three targeted improvements. First, the ConvSmart module expands the receptive field and strengthens multi-scale feature extraction, improving the representation of defects of varying sizes. Second, a Double-Stage Attention (DSA) mechanism, embedded at the deepest backbone stage, iteratively highlights discriminative crack patterns while suppressing shadows, markings, and texture noise. Third, a P2-level small-object detection path provides high-resolution features that significantly improve sensitivity to fine cracks and micro-potholes. Experiments on IRRDD show that YOLOv11n-CDL achieves 75.3% mAP@0.5 and 44.6% mAP@0.5:0.95, outperforming the baseline by 3.0 and 1.1 percentage points, and exceeding YOLOv8n and YOLOv7-tiny in both precision and recall. Additional results on RDD2022 and low-power devices confirm strong generalization and real-time deployability. These improvements demonstrate that YOLOv11n-CDL offers an effective balance between accuracy, robustness, and efficiency for practical pavement inspection applications.

A Few Lean Six Sigma (LSS) papers were studied during the construction project’s progress. This study proposes the integration of LSS, system dynamics (SD), and earned value management (EVM) as a comprehensive toolkit for datadriven decision-making. Significant waste- and quality-related interdependencies were identified using DEMATEL techniques with 27 Saudi construction experts. SD with two interdependent models of waste causes (WCs) and quality causes (QCs) were utilized to assess the project’s performance. Two metrics were employed: the sigma rating and the value-added ratio in the EVM generated by the developed SD model. The findings indicated that eliminating WCs had little impact on enhancing the project performance in the early stages of the project. The impact increased with the progress of the project. The improvement of the project quality was minimal for “increases of errors and omissions in design documents” and maximum for “increasing morale and attitude affect the quality of the project”.

The concepts of environmental impact index and spatial conflict degree have gained prominence in enhancing the controllability of collision accidents and mitigating the likelihood of collisions during construction processes. Nevertheless, prior studies predominantly focused on exploring collisions in terms of proximity and congestion between pairs of entities, thereby overlooking a comprehensive consideration of workers, construction machinery, environmental factors, and the spatial interaction of specific activities.To this end, this study aims to propose an integrated vision-based dynamic collision risk assessment framework by setting workers and mobile machinery as targeted research objectives and embedding a comprehensive risk assessment model in the proposed framework, thereby comprehensively assessing four types of risk factors (i.e., proximity, congestion, environmental impact index, and spatial conflict), and visualize the hierarchy of risk warnings. Firstly, a comprehensive risk assessment model was developed by using the fuzzy comprehensive evaluation method. This is followed by developing a dynamic risk assessment framework to extract the spatial information of the monitored objects by using computer vision as the underlying data of the risk factors. Finally, the proposed integrated framework was validated by an experimental study. This experiment’s safety risk assessment results are consistent with expectations, which largely illustrates the effectiveness of the evaluation model constructed in this paper. For the vision module, the accuracy of classification and monitoring is more than 95%, and the speed of the object detection algorithm to process the video is about 10 frames per second, which shows the feasibility of this study.

With the emergence of blockchain technology, knowledge sharing has become increasingly important for gaining a competitive advantage in any industry. However, it has not been fully implemented in the construction industry. This paper examines the factors influencing knowledge sharing in blockchain technology, using the Chinese construction industry as a case study. First, the study identified the key factors affecting knowledge sharing through a literature review and construction market research, classifying them based on the technology–organization–environment (TOE) framework. Next, the study employed focus group discussions to explore the relationships between pairs of factors and applied the interpretive structural modeling-matrix of cross-impact multiplication applied to classification (ISM-MICMAC) method to evaluate and prioritize these key factors. Finally, based on the empirical analysis, the paper discusses measures to promote knowledge sharing in the construction industry. This study contributes to the academic understanding of the factors influencing knowledge sharing and offers a fresh perspective for future research. Additionally, it provides practical insights to help the construction industry gain a competitive advantage and achieve sustainable development.

The potential of Modular Integrated Construction (MiC) to reduce carbon emissions (CEs) has led to increased attention on developing rigorous monitoring systems. Existing methods predominantly capture CEs of isolated MiC stages, overlooking nuances of arrangeable and reusable activities, thus hindering effective CE control measures. To overcome this limitation, this study develops an Internet of Things (IoT)-enabled framework for monitoring CEs of MiC, specifically focusing on the materialization phase, integrating across various MiC stages and facilitating detailed monitoring of CEs associated with arrangeable and reusable activities. This study builds up a CE measurement model tailored to the MiC materialization phase, providing a computational basis for a subsequent monitoring framework. By leveraging IoT technology, the framework is evaluated through case studies to confirm its feasibility and efficacy. The results indicate that the framework enabled improved monitoring capabilities, and the CEs of many MiC activities are successfully integrated into the system. The case study analysis demonstrates that the system's feedback-driven adjustments achieved a CE reduction of 732.04 metric tons.

Globally, several countries have begun to promote the application of building information modeling (BIM) technology and implement BIM-based building e-Permit systems. Despite the relatively rapid updates in the field of BIM-based building e-Permit systems, currently a lack of systematic sorting and summarization exists. This review evaluates the situation of seven representative countries from three aspects: the current adoption status of the BIM-based building e-Permit system, adoption background (BIM-related policy, roadmap, standards, and guidelines), and a future development plan for digital construction. These three aspects are analyzed, common measures across these countries are summarized. Four viewpoints are extracted for promoting BIM-based building e-Permit system adoption, which are mandatory policy orientation, standardization, internationalization, and intelligence. Finally, as the integration of AI into BIM-based building e-Permit systems is a recent research trend, this study also investigates existing research, highlights bottlenecks and points out future directions for AI integration.

Modular construction offers significant advantages including faster construction time, higher quality control and less environmental impact. To further enhance its advantages, advanced robotic construction technologies are being developed. This research develops an automated modular construction framework that incorporates the robotic kinematics, deep learning and deep reinforcement learning using a robotized crane. The proposed modular construction strategy utilizes YOLOv5-S for modular container identification and localization. An improved proximal policy optimization (PPO-I) is developed and implemented in this strategy for collision-free three-dimensional (3D) lifting path planning and modular container transportation. States and rewards of the PPO-I and robot kinematics design of a real mobile crane are developed. The feasibility of the proposed modular construction strategy is verified through four case studies in 3D virtual environments. More than 97% success rate is observed meaning that the proposed strategy can be implemented in the robotized crane to localize the modular container and transport it to the target position with collision avoidance. The results indicate the potential of the proposed robotic-assisted modular construction strategy in the field of automated construction.

As construction projects adopt increasingly interconnected digital technologies, their cyber-attack surface expands, making comprehensive cyber risk management essential to prevent incidents, mitigate risks, and minimize potential losses resulting from such attacks. However, the necessary risk factors for this purpose are lacking. Therefore, the study aims to develop a comprehensive set of project-level cyber risk factors tailored to the complexities of construction projects, identified through a systematic and flexible seven-step methodological framework: (1) a literature review of construction and cybersecurity sources to identify initial factors; (2) initial definition of risk categories; (3) internal evaluation and expert input to refine these factors; (4) distribution of a detailed expert questionnaire for rating; (5) expert evaluations through meetings and feedback sessions to enhance validity; (6) elimination of lower-scoring factors; and (7) establishment of quantitative scales for precise risk assessment. The findings include the 32 identified risk factors into five groups: project information, project structure, information technology (IT), operational technology (OT), and management and human aspects. The contributions include providing a set of risk factors that serve as cybersecurity management references and inputs for future quantitative risk assessments, offering a checklist used for proactive risk management, and introducing a framework adaptable for identifying factors of other risks.

Digital transformation is a pathway to improve productivity in the construction sector, but organizational structures still need more adaptation. There are no fully developed models at the level of factors and evidence between organizational structures and digital transformation. This paper analyzes the impact of digital transformation on organizational structures within the construction sector, utilizing a semi-structured interview and field inspection methodology to validate key organizational factors and evidence influencing this process. The research focuses on Colombia’s medium and large construction companies and aims to validate an integrated conceptual model of digital transformation and organizational structures. Results indicate that while technological tools have been adopted, the full potential of digital transformation still needs to be explored. The study highlights the factors and evidence that affect the variables of digital transformation and organizational structures. While technological tools are essential, they are unlikely to create a long-term competitive advantage. This research advances the understanding that the integrated implementation of digital transformation and organizational structures, grounded in validated factors and evidence, can drive enhanced decision-making, reduce inefficiencies, and improve productivity within the construction industry.

High-rise building construction has long been considered one of the most dangerous jobs globally. However, there is currently a lack of comprehensive systematic reviews that approach the implementation of safety practices in high-rise construction. This study aims to fill the following research gaps: first, to understand the significance and identify the barriers faced to implementing safety practices in high-rise construction; second, to propose a framework to improve safety performance. A total of 109 articles from databases were thoroughly reviewed in this study. The research identified six categories of benefits, five types of safety barriers, and 15 strategies proposed to overcome these barriers. In terms of the number of sources, “reduced accident rates” and “improved organizational reputation” were identified as the top benefits in terms of the significance of safety practices. “Lack of health and safety training” was deemed the largest barrier. To address these challenges, this paper explores the strategies and risk mitigation measures from the perspectives of six stakeholders: regulatory agencies, government, owners, contractors, consultants, and workers. The findings suggest that establishing strict market entry mechanisms are fundamental while strengthening worker safety training and conducting regular safety inspections and real-time site monitoring are the most effective methods.