Risk Assessment of High-Speed Rail Projects: A Risk Coupling Model Based on System Dynamics

Xue, Yutong; Pan, Xiang; Jia, Fuyuan; Liu, Zhaowen

doi:10.3390/ijerph17155307

Cited by 33 publications

(23 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Zhang et al [ 34 , 35 ] used the N-K model to analyze the coupling relationship among risk factors of a gas explosion, and concluded that the probability of gas explosion increases with the increase of the number of risk factors. Xue et al [ 36 ] built a risk coupling model for the risk assessment of high-speed railway projects based on the system dynamics method, which can identify the key coupling effects that increase risks, although their research applied the coupling model for analysis and studied the coupling mechanism of risk accidents. However, the analysis was based on a single risk factor, and the correlation between risks was not analyzed.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

Liu

Meng

Xing

et al. 2021

Sensors

View full text Add to dashboard Cite

Safety management in hoisting is the key issue to determine the development of prefabricated building construction. However, the security management in the hoisting stage lacks a truly effective method of information physical fusion, and the safety risk analysis of hoisting does not consider the interaction of risk factors. In this paper, a hoisting safety risk management framework based on digital twin (DT) is presented. The digital twin hoisting safety risk coupling model is built. The proposed model integrates the Internet of Things (IoT), Building Information Modeling (BIM), and a security risk analysis method combining the Apriori algorithm and complex network. The real-time perception and virtual–real interaction of multi-source information in the hoisting process are realized, the association rules and coupling relationship among hoisting safety risk factors are mined, and the time-varying data information is visualized. Demonstration in the construction of a large-scale prefabricated building shows that with the proposed framework, it is possible to complete the information fusion between the hoisting site and the virtual model and realize the visual management. The correlative relationship among hoisting construction safety risk factors is analyzed, and the key control factors are found. Moreover, the efficiency of information integration and sharing is improved, the gap of coupling analysis of security risk factors is filled, and effective security management and decision-making are achieved with the proposed approach.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

Liu

Meng

Xing

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…As mentioned above, the reasons for the occurrence of risks are as important as their impact and lead to a full understanding of the risks. This concept exists in many risk assessment methods, but the majority of the methods usually focus on the assessment of an individual risk for the simplification of the model and do not pay attention to the causes of a risk, part of which can be due to the occurrence of other risks; this is especially effective when accurate risk assessment is important [14]. For instance, the approach of failure modes and effect analysis (F.M.E.A) includes a qualitative analysis of failure modes and seeks a quantitative analysis of their effects, in terms of the probability and effect, or the cause-and-impact tree just addresses the event points and determines the relationships between them [15].…”

Section: Literature Surveymentioning

confidence: 99%

“…Xue et al [14] developed a risk coupling model of the risk assessment of a high-speed railroad (HSR) project based on a system dynamics method. Sayyadi and Awasthi [27] described an integrated method for evaluating sustainable transport policies which was based on the system dynamics simulation and the analytic network process (A.N.P).…”

Section: Literature Surveymentioning

confidence: 99%

A Framework for Project Risk Assessment in Dynamic Networks: A Case Study of Oil and Gas Megaproject Construction

et al. 2021

View full text Add to dashboard Cite

Conventionally, risk assessment methods suffer from ignoring the dynamic nature of risks. Therefore, this study demonstrates an interactive approach to risks and prioritizes risks based on their overall cyclic events acting on the attainment of the goals of a project during its life cycle. Hence, the risks of the project of gas condensate storage tank construction (G.C.S.T.C) are first identified and evaluated by using a fuzzy inference system (F.I.S); then, the created risk network is simulated over the project life cycle by using the results of the F.I.S; finally, the impacts of the risks are assessed by applying purposeful indicators. Based on the results, the risk of the increase in the economic sanctions most significantly impacts on achieving the project goals, and lack of the governmental support, the variation of the fiscal governmental policy, high inflation rates, and sharp changes in the exchange rate are considered as the important risks. The results also demonstrated that these five risks improved the attainment of the project goals by more than 55%, while the total improvement on the 21 other risks enhanced the achievement of the project goals by less than 45%.

show abstract

“…One primary goal of SD is to capture how components in a system interact with one another and how changes in one variable influence another over time [28,29]. Xue et al [30] proposed an SD-based risk model to assess risks of high-speed rail (HSR) projects. However, their proposed model has certain limitations, such as ignoring unavoidable subjectivity in experts' risk evaluations.…”

Section: Reviewing and Comparing Fuzzy System Dynamics And Fuzzy Cognitive Maps Capabilities To Model Construction Risk Management Systemmentioning

confidence: 99%

An Adaptive Hybrid Model for Determining Subjective Causal Relationships in Fuzzy System Dynamics Models for Analyzing Construction Risks

2021

View full text Add to dashboard Cite

Modeling risk management systems in construction projects is a complex process because of various internal and external factors and their interrelationships. Fuzzy system dynamics (FSD) have been commonly employed to model and analyze construction risk management systems. To run FSD simulation models, all hard (objective) and soft (subjective) causal relationships between variables must be quantified. However, a research gap exists regarding structured methods for constructing soft causal relationships in FSD models. This paper proposes an adaptive hybrid model consisting of fuzzy analytical hierarchy process, weighted principle of justifiable granularity, and fuzzy aggregation operators to determine crisp values of causality degree for soft (subjective) causal relationships in FSD modeling of construction risk analysis. The proposed model is implemented in analyzing construction risks of a windfarm project to illustrate its applicability. The proposed model generates two results: (1) optimized membership functions for linguistic terms representing the causality degree of soft relationships and (2) the crisp value for the causality degree of soft relationships. The contribution of study is to propose a structured model to improve efficiency and effectiveness of developing FSD quantitative modeling by addressing soft causal relationships between different variables in FSD models and considering multiple risk expertise of heterogeneous experts in construction risk assessment.

show abstract

Risk Assessment of High-Speed Rail Projects: A Risk Coupling Model Based on System Dynamics

Cited by 33 publications

References 48 publications

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

A Framework for Project Risk Assessment in Dynamic Networks: A Case Study of Oil and Gas Megaproject Construction

An Adaptive Hybrid Model for Determining Subjective Causal Relationships in Fuzzy System Dynamics Models for Analyzing Construction Risks

Contact Info

Product

Resources

About