Natural hazards bring significant influences on and socioeconomic loss to cities and communities. Historic events show that fire following earthquake (FFE) is the most influential uncertain disturbance on the urban infrastructure system. Under the FFE scenario, the concept of resilience is widely implemented to make up the shortcomings derived from the traditional disaster management methodology. Resilient cities and communities are required to improve the systemic performance in responding to the FFE. To fulfill these goals, measuring community resilience is an essential work for municipal policy makers. Therefore, this study conducted a comprehensive assessment on community resilience adapted to the FFE scenario. The systematic literature review (SLR) was employed to identify the indicators, and the analytic network process (ANP) technique was implemented to determine their weights. 20 indicators were extracted, and 4 communities that encountered FFE in China were selected for the empirical analysis. Thereafter, the preference ranking organization method for enriched evaluation (PROMETHEE) II technique was selected through using the multicriteria decision analysis (MCDA) methods selection framework to fulfill the comprehensive assessment. The results were discussed and demonstrated with graphical analysis for interactive aid (GAIA) technique. The findings revealed that the G Community won the highest score and had the strongest performance. However, H Community had the lowest score and the weakest performance. The proposed comprehensive methods could benefit the decision-makers and the policy executors achieving the community resilience adapted to the FFE scenario by improving the effective indicators.

Railway engineering generates large amounts of construction and demolition waste (CDW). To quantify the amount of CDW generated from railway engineering projects throughout the whole life cycle, a process-based life cycle assessment model is proposed in this paper. The life-cycle CDW is divided into four parts: CDW from off-site transportation of construction materials (OSTCM), CDW from site operation wastage of construction materials (SOWCM), discard ballast from roadbeds, stationyard, bridges and tunnels (DB), and CDW from reparation and renewal of aging components (RRAC). Yun-Gui Railway is selected as a case study to validate the developed model, and an uncertainty analysis is conducted with Oracle Crystal Ball software. The results show that between 175 and 311 million tons of CDW is generated throughout the whole life cycle of Yun-Gui Railway. DB is the largest component of the life-cycle CDW from railway engineering projects. This indicates the negative environmental impacts of railway construction can be significantly mitigated by optimizing the location of ballast disposal sites and developing suitable landfill proposals. Also, the CDW generated by wastage of construction materials during off-site construction and site operation is important in waste management in railway engineering projects, in which rubble, sand, and cement have the high potential for waste reduction. Findings from this study can contribute to the knowledge body as well as the engineering practice in green railways.

Safety risk identification is the premise and foundation of safety risk management for railway construction. However, due to some characteristics of railway projects, which include large volumes of work, complex construction environments, and long construction cycles, etc., the risk factors of railway projects are often hidden in all stages of engineering construction. It results in the comprehensive identification of safety risks of railway projects being usually difficult, and this problem is more serious when the railway is constructed in complex and dangerous areas. Therefore, to identify the safety risks comprehensively, this paper constructs a safety risk identification method applicable to railway construction in complex and dangerous areas. This method studies the spatial and temporal distribution of risks and their relationship with subprojects by using a work breakdown structure (WBS), a risk breakdown structure (RBS), grid-based management, and forming a safety risk identification matrix, which can help researchers analyze the characteristics of risks. In order to verify the effectiveness of the method, the A Railway, which is located in the western of China, was selected as a case study, and risk identification for its civil engineering was carried out. The research results show that in the construction process of the A Railway, the main types of safety risks suffered by various branch projects were different. In addition, some risk factors only appeared at specific times in space, and there is a strong interaction between these risk factors. Based on this method, safety risk identification can intuitively discover the spatial and temporal distribution of risk factors and analyze the interaction between risk factors, which can provide help for the formulation of targeted risk control measures.

High-speed railways are critical infrastructure in many countries, but their construction generates substantial spoil, particularly in mountainous regions dominated by tunnels and slopes, necessitating the establishment and monitoring of spoil disposal areas. Inadequate monitoring of spoil disposal areas can lead to significant environmental issues, including soil erosion and geological hazards such as landslides and debris flows, while also hindering the recycling and reuse of construction spoil, thereby impeding the achievement of circular economy and sustainable development goals for high-speed railways. Although the potential of geographic information systems, remote sensing, and global positioning systems in waste monitoring is increasingly recognized, there remains a critical research gap in their application to spoil disposal areas monitoring within high-speed railway projects. This study proposes an innovative framework integrating geographic information systems, remote sensing, and global positioning systems for monitoring spoil disposal areas during high-speed railway construction across three key scenarios: identification of disturbance boundaries (scenario 1), extraction of soil and water conservation measures (scenario 2), and estimation of spoil volume changes (scenario 3). In scenario 1, disturbance boundaries were identified using Gaofen-1 satellite data through processes such as imagery fusion, unsupervised classification, and spatial analysis. In scenario 2, unmanned aerial vehicle data were employed to extract soil and water conservation measures via visual interpretation and overlay analysis. In scenario 3, Sentinel-1 data were used to analyze elevation changes through the differential interferometric synthetic aperture radar method, followed by the estimation of spoil volume changes. The effectiveness of this integrated framework was validated through a case study. The results demonstrate that the framework can accurately delineate disturbance boundaries, efficiently extract soil and water conservation measures, and estimate dynamic changes in spoil volume with an acceptable error margin (15.5%). These findings highlight the framework’s capability to enhance monitoring accuracy and efficiency. By integrating multi-source data, this framework provides robust support for sustainable resource management, reduces the environmental impact, and advances circular economy practices. This study contributes to the efficient utilization of construction spoil and the sustainable development of high-speed railway projects.

To effectively mitigate service slope disaster risks in mountainous areas and enhance the overall safety of highway operations, based on the geological and structural characteristics of slopes, considering slope technical conditions, overall stability, and potential disaster consequences, 25 important influencing factors are systematically identified. The identification process integrates insights from the relevant literature, expert opinions, and historical disaster maintenance records of such slopes. An integrated approach combining Interpretive Structural Modeling (ISM) and Bayesian Networks (BNs) is utilized to conduct a quantitative analysis of the interrelationships and impact strength of factors influencing the disaster risk of mountainous service highway slopes. The aim is to reveal the causal mechanism of slope disaster risk and provide a scientific basis for risk assessment and prevention strategies. Firstly, the relationship matrix is constructed based on the relevant prior knowledge. Then, the reachability matrix is computed and partitioned into different levels to form a directed graph from which the Bayesian network structure is constructed. Subsequently, the expert’s subjective judgment is further transformed into a set of prior and conditional probabilities embedded in the BN to perform causal inference to predict the probability of risk occurrence. Real-time diagnosis of disaster risk triggers operating slopes using backward reasoning, sensitivity analysis, and strength of influence analysis capabilities. As an example, the earth excavation slope in the mountainous area of Anhui Province is analyzed using the established model. The results showed that the constructed slope failure risk model for mountainous operating highways has good applicability, and the possibility of medium slope failure risk is high with a probability of 34%, where engineering geological conditions, micro-topographic landforms, and the lowest monthly average temperature are the main influencing factors of slope hazard risk for them. The study not only helps deepen the understanding of the evolutionary mechanisms of slope disaster risk but also provides theoretical support and practical guidance for the safe operation and disaster prevention of mountainous highways. The model offers clear risk information, serving as a scientific basis for managing service slope disaster risks. Consequently, it effectively reduces the likelihood of slope disasters and enhances the safety of highway operation.

Construction workers’ unsafe behaviors (CWUBs) are a primary cause of construction safety accidents in railway tunnel engineering (RTE). Understanding the causation mechanism between construction safety accidents, CWUBs, and their influencing factors is crucial for improving safety management. However, research in this area remains insufficient. This study systematically identifies 9 types of construction safety accidents, 11 types of CWUBs, and 35 influencing factors, covering three core dimensions: organizational management, individual safety capacity, and safety environment. Using the 24model, this study qualitatively elucidates the causation mechanism and identifies the primary and secondary causation relationships among 55 factors. On this basis, a network model of CWUBs in RTE is developed and quantitatively analyzed using social network analysis from the perspectives of the overall network, block network, and individual network, resulting in the identification of a critical network comprising 27 key factors. Based on the findings, nine targeted intervention measures are proposed, encompassing pre-emptive prevention, on-site control, and emergency management. This study innovatively integrates the 24model and social network analysis, systematically analyzing the causation mechanism of CWUBs in RTE from both qualitative and quantitative perspectives. This research not only provides a systematic and innovative analytical framework for CWUBs in RTE, addressing a critical gap in the study of unsafe behaviors and accident causation in complex systems, but also offers practical guidance for safety risk management. Additionally, it enriches the theoretical framework of unsafe behavior research, providing valuable insights for further studies in related fields.