As urbanization continues to grow around the world, the risks associated with construction are increasing. Scientific and practical risk assessments help reduce safety risks and achieve healthy, long-term growth, so there has been much research in this field. Through a review of the literature, this study aims to reveal the state and trends of research in the field of safety risk assessment. We searched 473 articles on construction risk assessment from the Web of Science (WoS) in the last decade, bibliometrically analyzed them, and then uncovered their significance using CiteSpace software (6.1. R6 (64-bit) Basic). The primary topics of conversation are countries, institutions, authors, and keywords, followed by references. According to the co-authorship analysis, the current research in this field is mainly from China, the USA, and Australia. Most influential authors currently have teaching or research positions at educational institutions; the most notable of which include Huazhong University of Science and Technology, Hong Kong Polytechnic University, and Tsinghua University. They form a relatively close network of institutional cooperation. Based on the results of the co-term analysis, this study found that the current research hotspots are mainly focusing on “multi-objective optimization”, “risk management”, “mechanical characterization”, “mental fatigue”, “accident prevention”, and many others. Data-driven, AI-assisted, and multi-stakeholder participation are the future trends in this field.

Net-zero energy building (NZEB), an initiative to address energy conservation and emission reduction, has received widespread attention worldwide. This study aims to systematically explore recent challenges in NZEB retrofit research through a mixed-method approach and provide recommendations and future directions. A review of 106 documents (2020–2024) retrieved from the Web of Science and Scopus databases found that the globalization of NZEB retrofit research is unstoppable. Assessment methods are diverse, ranging from modeling energy efficiency (using different software such as DesignBuilder 7.0, PVsyst 7.4, EnergyPlus 24.1.0, etc.) to multi-attribute decision-making methods (e.g., DEMATEL-AHP/ANP-VIKOR) and comparative analysis. Current assessment metrics are dominated by economic benefits (e.g., net present value, dynamic payback period, and total operating cost) and energy consumption (e.g., electricity consumption and generation), with less consideration of environmental impacts (e.g., carbon reduction), as well as comfort (e.g., thermal comfort and indoor comfort). The study found that current challenges mainly include “Low economic feasibility of retrofitting”, “Building retrofit energy code irrationality”, and “Insufficient understanding, communication, and trust between stakeholders”. To overcome these challenges, the study also proposes a framework of strategies to address them, including (1) maximizing natural space, (2) introducing a tenant equity system, (3) upgrading waste management, (4) strengthening energy monitoring, (5) establishing complete life cycle mechanisms, (6) providing systemic solutions; (7) promoting the use of low-carbon building materials, and (8) increasing policy support.

Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on “ergonomics”, “machine learning”, “computer simulation”, and “wearable sensors”. Secondly, the previous 80 factors contributing to hoisting risks were summarized from a “human–equipment–management–material–environment” perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT’s interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research.

Hoisting is an essential aspect of Industrial Building System (IBS) construction. Although research on hoisting safety in China has made strides to focus on "worker," "data," "task," "site," and "accident," there still needs to be more approaches based on multi-dimensional social system thinking. Therefore, the paper aims to fill this gap. We investigated 105 hoisting accidents in China and found that hoisting accidents occurred most frequently in China's southeast coastal region; truck-mounted cranes and tower cranes were the most common types of machinery involved in accidents; hoisting load off, capsizing of crane machinery, and workers falling from height are the three most common accident types; the average impact of a single hoisting accident is approximately RMB 2.43 million direct economic loss, 1.543 deaths and 0.829 injured. This study used three algorithms (Rindge regression, Lasson regression, and partial least squares regression) to explore the impact of deaths and injuries on direct economic losses. By combining Rasmussen's risk framework with the characteristics of hoisting construction, six risk domains and thirty-six safety risk factors were identified. Finally, we used AcciMap technology to construct a qualitative IBS hoisting management model, which exhaustively presents the systematic levels and propagation paths of the influencing factors by the PDCA method. The research helps academics explore strategies to improve the safety of hoisting construction in IBS. Moreover, the study outcomes can inform the policy-making process towards promoting healthy and sustainable construction development.

Machine learning, a key thruster of Construction 4.0, has seen exponential publication growth in the last ten years. Many studies have identified ML as the future, but few have critically examined the applications and limitations of various algorithms in construction management. Therefore, this article comprehensively reviewed the top 100 articles from 2018 to 2023 about ML algorithms applied in construction risk management, provided their strengths and limitations, and identified areas for improvement. The study found that integrating various data sources, including historical project data, environmental factors, and stakeholder information, has become a common trend in construction risk. However, the challenges associated with the need for extensive and high-quality datasets, models’ interpretability, and construction projects’ dynamic nature pose significant barriers. The recommendations presented in this paper can facilitate interdisciplinary collaboration between traditional construction and machine learning, thereby enhancing the development of specialized algorithms for real-world projects.

Occupational injuries in the construction industry have plagued many countries, and many cases have shown that accidents often occur because of a combination of project participants. Assembled construction (AC) projects have received extensive attention from Chinese scholars as a future trend, but few studies have explored the interrelationships and potential risks of various stakeholders in depth. This study fills this research gap by proposing a multi-stakeholder AC risk framework. The study surveyed 396 stakeholders, then analyzed the collected data and created a risk framework based on Structural Equation Modelling (SEM) and the CRITIC weighting method. The results revealed that factors like "regular supervision is a formality," "blindly approving the wrong safety measures," and "failure to organize effective safety education and training." are vital risks in AC of China. Finally, the study validates the risk factors and the framework with 180 real-life cases, which shows that the proposed framework is theoretically grounded and realistic. The study also suggests multi-level strategies such as introducing AI-based automated risk monitoring, improving the adaptability of normative provisions to technological advances, and advancing the culture of project communities of interest to ensure AC’s safe practices.