Reservoir dam structure is critical to protect public life and property and has always been attention worldwide. However, a systematic approach to assessing the safety risks of reservoir dam structure (RDS) is still required. This study presents a holistic framework for evaluating the safety risk of RDS and develops an evaluation index system. A risk assessment model is constructed based on the cloud and Dempster-Shafer (D-S) evidence theories. The model's validity is confirmed through an empirical analysis of the XY reservoir project. This study offers theoretical insights and practical solutions for managers to facilitate decision-making and supports the advancement of industry standards.

The rapidly evolving domain of artificial intelligence (AI) is significantly influencing the green building (GB) sector, acting as a catalyst for green building technology innovation (GBTI). Notably, unlike AI applications in green buildings (AI-in-GB), AI-driven GBTI positions AI as the central force, promoting and leading novel technological breakthroughs. Although research has been conducted in AI-in-GB, there remains a lack of in-depth analysis on AI-driven GBTI advancements. To address this gap, this study comprehensively reviews the existing research in AI-driven GBTI, systematically organizing and analyzing the knowledge structure, thematic evolution, research paradigms, and potential future research directions. This study conducts bibliometric analyses on 151 research publications sourced from Scopus using VOSviewer and CiteSpace, capturing the temporal characteristics, research hotspots, and frontiers of research in this area. Additionally, based on dynamic topic modeling, this study analyzes 86 representative articles, identifying three key research themes and their evolution trends, systematically elucidating the knowledge framework within the field. Through further discussion, this study reveals four core research paradigms and proposes three potential future research directions, providing theoretical support and guidance for its continued development. This study is the first to focus on AI-driven GBTI, contributing to a comprehensive understanding and expanding the knowledge domain of GBTI.

Due to the dynamic and complex nature of mega construction projects (MCPs), mega construction project risks (MCPRs) have significantly increased in recent years. Technological innovation cooperation (TIC) is accepted as an approach to solve these issues. However, considering the new technological innovation challenges, technological innovation risks (TIRs) have been identified as a limitation of TIC. This study aims to develop a conceptual framework to explain TIC for MCPs. It is based on a review of the literature, engineering practice, and logical reasoning. The conceptual framework describes the interaction between MCPRs and TIC. It points out that MCPRs drive technological innovation, and technological innovation objectives guide the TIC. TIC has a negative effect on solving TIRs, and TIRs positively affect MCPRs. Cooperation performance will mediate the relationship between TIC and MCPRs. The conceptual framework may provide a theoretical basis to guide future empirical studies that validate the relationship between MCPRs and TIC and puts forward reasonable suggestions for MCPs.

In the context of green development and digital transformation, the technological innovation cooperation in megaprojects requires a spanning from policy guidance, technological breakthroughs, and localized pilot projects to driven demand, integrated innovation (i.e., collaborative innovation across sectors, stages, and stakeholders), and comprehensive promotion. Despite the potential benefits, many megaprojects face challenges related to complex relationships, behavioral uncertainties, low performance, and technological innovation risks. A question of practical and theoretical significance is how to facilitate technological innovation cooperation in megaprojects. This study proposes the 3C Theory, which integrates cooperative relationships, behaviors, and performance, and investigates how technological innovation risks moderate these interactions. Using data from 19 megaprojects, we employ a mixed-methods approach involving hypothesis testing through regression analysis. The findings reveal that strong cooperative relationships positively influence cooperative performance through cooperative behaviors and that technological innovation risks play a significant moderating role. This study offers several practical recommendations for megaproject managers, including enhancing cooperative relationships, promoting effective behaviors, managing innovation risks, and developing cooperative innovation platforms. This study introduces the 3C Theory to megaprojects and provides novel insights into how collaboration and risk management can drive sustainable innovation.

Railway cable-stayed bridge construction is characterized by high complexity and substantial safety risk. Deficiencies in safety control may result in serious accidents (e.g., collapse and falls), causing significant casualties and economic losses; therefore, clarifying risk interactions and accident-causing mechanisms is essential. This study proposes a fuzzy DEMATEL–ISM approach in which fuzzy sets capture uncertainty in experts’ linguistic assessments. DEMATEL quantifies influence strengths and causal relationships among factors, and ISM constructs a multi-level hierarchy to explain accident causation. Twenty safety influencing factors are identified and grouped into five categories: management, human, material and equipment, construction technology, and environmental conditions. The obtained accident-causing mechanism comprises seven hierarchical levels: L₁: collapse and fall accidents, L₂: direct factors, L₃–L₅: indirect factors, and L₆–L₇: root factors. This mechanism is a chain of events that leads to an accident, with the nodes improper prestressing, structural deformation and differential settlement. These key nodes can be avoided by reinforcing safety management system implementation, daily supervision and inspection, and education and training on the subject of safety to ensure the safety of railway cable-stayed bridge construction.

As an important part of green building, green housing (GH) has become a strategic priority in many countries. However, the market share of GH remains limited due to conflicting interests and divergent strategic choices among stakeholders. To address this challenge, various low-carbon policy tools, such as financial subsidies (FS), carbon taxes (CT), and carbon emissions trading (CET), have been introduced. Despite these efforts, the influence of low-carbon policies on stakeholders’ strategies remains inadequately understood. This study aims to bridge this research gap by constructing an evolutionary game model that incorporates the government, developers, and consumers while considering relevant policy factors. The model identifies evolutionary stable strategies (ESS) for each stakeholder. Then, based on MATLAB 2021b, several simulations are carried out for initial, development, mature, and stable stage. The simulation results show the dynamic changes in stakeholder behavior over time. This study also analyzes how key parameters affect the system’s evolution and puts forward suggestions from each stakeholder’s perspective. The results show that (1) consumer subsidies are more effective than those for developers. (2) Subsidies for developers cause more significant financial pressure on governments than subsidies for consumers. (3) The CET policy helps developers adopt GH more quickly. (4) Proper CT promotes developers to make greener choices. (5) Reducing the difference between green and traditional housing supports the stable development of the GHM. This study offers theoretical insights to guide stakeholder decision-making and provides practical recommendations for government aiming to play a role in fostering GHM development.