Globally, monitoring productivity, occupational health, and safety of construction workers has long been a significant concern. To address this issue, there is an urgent need for efficient methods to continuously monitor construction sites and recognize workers’ actions in a timely manner. Recently, advances in electronic technology and pose estimation algorithms have made it easier to obtain skeleton and joint trajectories of human bodies. Deep learning algorithms have emerged as robust and automated tools for extracting and processing 3D skeleton information on construction sites, proving effective for workforce action assessment. However, most previous studies on action recognition have primarily focused on single-stream data, which limited the network’s ability to capture more comprehensive worker action features. Therefore, this research proposes a Spatial-Temporal Multi-Feature Network (STMF-Net) designed to utilize six 3D skeleton-based features to monitor and capture the movements of construction workers, thereby recognizing their actions. The experimental results demonstrate an accuracy of 79.36%. The significance of this work lies in its potential to enhance management models within the construction industry, ultimately improving workers’ health and work efficiency.

In recent years, despite the continuous improvement of China’s construction safety management systems and the adoption of advanced technologies, safety accidents remain frequent. This shift highlights the growing importance of human factors in construction safety. As the main labor force, the new generation of construction workers differs significantly from previous generations in values and motivation, reducing the effectiveness of traditional safety management models. This study investigates the direct effect of transformational leadership on the safety behavior of new-generation construction workers. Using survey data collected from construction enterprises in Guangdong Province, China, and applying structural equation modeling (SEM), the results reveal that transformational leadership has a significant positive impact on safety behavior. All four dimensions—idealized influence, inspirational motivation, idealized influence (charisma) and individualized consideration—positively influence both safety compliance and participation, with inspirational motivation exerting the strongest effect (β = 0.509 for compliance; β = 0.446 for participation). These findings indicate that leaders who articulate a compelling shared vision can effectively internalize safety norms and motivate proactive safety participation. This study enriches theoretical understanding of safety leadership mechanisms and provides practical guidance for construction enterprises to enhance safety performance through cultivating transformational leadership among managers.

Fire risks during the construction phase remain one of the most critical challenges in the construction industry, often leading to property losses, casualties, project delays, and long-term reputational damage. To address these issues, this study proposes a risk-informed emergency optimization framework for construction fire scenarios. Utilizing a disaster chain network framework derived from previous case analyses, including 25 secondary events and 59 causal connections, the study focuses on identifying high-risk transmission paths and optimizing emergency response. Through risk-based edge evaluation, high-risk transmission pathways—particularly those linked to casualties—were detected, forming the basis for targeted intervention strategies. An optimized multi-agency collaborative rescue process was designed to address these critical links. Using Colored Petri Net (CPN) simulation, the proposed process was validated on a representative major fire case, demonstrating a 36.3% reduction in overall emergency response time and a 19.5% decrease in firefighting duration. The results highlight that integrating disaster chain analysis, risk-weighted edge disruption, and CPN-based simulation can significantly enhance emergency coordination and operational efficiency. This study provides actionable insights for policymakers and project managers to strengthen fire risk management strategies and build more resilient emergency systems for the construction sector.

Architectural heritage faces multiple degradation risks, while traditional preventive conservation often falls short in information integration, dynamic analysis, and regional management. Moreover, existing HBIM research lacks systematic digital approaches for hybrid Sino-Western vernacular heritage. This study addresses these gaps by constructing a preventive conservation HBIM framework for Kaiping Diaolou. A customized parametric component library and a three-level coding system are developed, enabling systematic HBIM application in non-standardized contexts. Moving beyond digital archiving, a closed-loop workflow of “data acquisition → HBIM → multi-physics simulation → targeted intervention” is established, shifting HBIM’s core function from passive documentation to proactive risk forecasting. A value- and risk-driven principle is proposed to define LOD/LOI levels, allowing appropriate modeling resource allocation according to heritage significance and risk grades. A direct data-interaction workflow is developed between HBIM and multi-physics simulations (structural FEM, wind/CFD), enabling quantitative identification of structural weak points and high-risk environmental degradation areas. Enhanced visual expression methods, including 3D defect mapping and a “shadow-element and displacement-decomposition” structural exploded-view technique, are introduced to overcome the limitations of conventional 2D representations. The proposed framework and methods are validated through the case study of Ruishi Tower, demonstrating the feasibility of HBIM in heritage preventive conservation and providing a replicable pathway for the digital preservation of similar vernacular heritage.

The construction industry is characterized by high physical demands, where workers are regularly engaged in manual handling and labor-intensive tasks, making fatigue a common occupational challenge. This study aims to quantify the combined effects of working height and task duration on physical fatigue among construction workers and to establish dynamic fatigue thresholds based on surface electromyography (sEMG) signals. A two-way repeated-measures field experiment was conducted on an actual construction site with eight healthy male workers. Each participant completed four working-height conditions (9, 20, 33, and 46 m), and at each height, physiological and subjective measurements were obtained at four repeated time points (0.5, 1.0, 1.5, and 2.0 h). This yielded 32 participant-by-height trials and 128 repeated observations in total. The results demonstrated a significant interaction between working height and task duration, with the increase in heart rate from 0.5 h to 2 h at 46 m being approximately 2.2-fold higher than that at 9 m during a two-hour working period. Frequency-domain analysis of sEMG signals revealed significant declines in median frequency (MDF) and mean power frequency (MPF) across all monitored muscles over time (p < 0.001), with cubic regression models showing excellent goodness-of-fit (R² up to 0.947). Based on MDF and MPF trends, fatigue progression was classified into three phases: development (0–30 min), stability (30–75 min), and instability (after 75 min), identifying approximately 75 min as an exploratory transition point from compensation to decompensation. These preliminary results provide exploratory empirical evidence that may inform the future optimization of work–rest schedules and real-time fatigue monitoring strategies in high-rise construction operations, pending validation in larger, more diverse populations.