Incorporating safety risk into the design process is one of the most effective design sciences to enhance the safety of metro station construction. In such a case, the concept of Design for Safety (DFS) has attracted much attention. However, most of the current research overlooks the risk-prediction process in the application of DFS. Therefore, this paper proposes a hybrid risk-prediction framework to enhance the effectiveness of DFS in practice. Firstly, 12 influencing factors related to the safety risk of metro construction are identified by adopting the literature review method and code of construction safety management analysis. Then, a structured interview is used to collect safety risk cases of metro construction projects. Next, a developed support vector machine (SVM) model based on particle swarm optimization (PSO) is presented to predict the safety risk in metro construction, in which the multi-class SVM prediction model with an improved binary tree is designed. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. Finally, the proposed framework is applied to a case study of metro station construction. The prediction results show the PSO–SVM model is applicable and reasonable for safety risk prediction. This research also identifies the most important influencing factors to reduce the safety risk of metro station construction, which provides a guideline for the safety risk prediction of metro construction for design process.

Capturing the interrelations among risks is essential to thoroughly understand and promote coal mining safety. From this standpoint, 105 risks and 135 interrelations among risks had been identified from 126 typical accidents, which were also the foundation of constructing coal mine risk network (CMRN). Based on the complex network theory and Pajek, six parameters (i.e., network diameter, network density, average path length, degree, betweenness, and clustering coefficient) were employed to reveal the topological properties of CMRN. As indicated by the results, CMRN possesses scale-free network property because its cumulative degree distribution obeys power-law distribution. This means that CMRN is robust to random hazard and vulnerable to deliberate attack. CMRN is also a small-world network due to its relatively small average path length as well as high clustering coefficient, implying that accident propagation in CMRN is faster than regular network. Furthermore, the effect of risk control is explored. According to the result, it shows that roof collapse, fire, and gas concentration exceeding limit refer to three most valuable targets for risk control among all the risks. This study will help offer recommendations and proposals for making beforehand strategies that can restrain original risks and reduce accidents.

With the rapid expansion of prefabricated construction in China, significant changes in safety performance are still unapparent for numerous prefabricated constructions, and safety accidents are constantly exposed in public. The ignorance of interactions among safety risks impedes efficacious improvement, which instructs the need for a thorough analysis of these interactions based on complex network theory. This paper starts with the identification of 37 safety risks refined through literature review and expert interviews, and 90 interrelationships among them verified by virtue of the questionnaire survey, laying a foundation for the establishment of a prefabricated construction safety risk network (PCSRN). The topological analysis results prove that PCSRN is a scale-free as well as a small-world network, which indicates the high-efficiency propagation and diffusion among safety risks in prefabricated constructions. Moreover, eight critical nodes are identified with four different ranking criteria, and corresponding safety strategies are proposed to address them. The developed method not only provides a novel insight to interpret the safety risks of prefabricated construction but also has the potential to advance safety performance of this sector.

Purpose: The purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.Design/methodology/approach: To achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).Findings: The topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.Research limitations/implications: In practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.Practical implications: This study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.Originality/value: This paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.

Vulnerability analysis of network models has been widely adopted to explore the potential impacts of random disturbances, deliberate attacks, and natural disasters. However, almost all these models are based on a fixed topological structure, in which the physical properties of infrastructure components and their interrelationships are not well captured. In this paper, a new research framework is put forward to quantitatively explore and assess the complexity and vulnerability of critical infrastructure systems. Then, a case study is presented to prove the feasibility and validity of the proposed framework. After constructing metro physical network (MPN), Pajek is employed to analyze its corresponding topological properties, including degree, betweenness, average path length, network diameter, and clustering coefficient. With a comprehensive understanding of the complexity of MPN, it would be beneficial for metro system to restrain original near‐miss or accidents and support decision‐making in emergency situations. Moreover, through the analysis of two simulation protocols for system component failure, it is found that the MPN turned to be vulnerable under the condition that the high‐degree nodes or high‐betweenness edges are attacked. These findings will be conductive to offer recommendations and proposals for robust design, risk‐based decision‐making, and prioritization of risk reduction investment.

The “Yangtze River to Huaihe River” project is another large-scale trans-basin water diversion project in China, following the South-to-North Water Division Project. The reservoir project is in the period of China’s rapid development of modernization, informatization and marketization. During this period, reservoir-induced migrants have great differences in resource endowment and development opportunities, so they show different characteristics in the integration process. In order to clarify these characteristics and formulate corresponding policies based on these characteristics to help them integrate into the society of the resettlement area as soon as possible, this paper has completed two aspects of work: First, a method for identifying social integration is proposed. Second, the characteristics of social integration of different migrant groups in different time dimensions are analyzed. This study has two important findings: First, the extent of information sources and understanding of policies has a greater impact on the social integration of migrants. The wider the source of information and the more understanding of policies, the more it helps migrants to integrate into society. Second, the social network relationship established by the female migrant group in the maintenance activities will greatly promote their social integration.