Using Interpretative Structural Modeling to Identify Critical Success Factors for Safety Management in Subway Construction: A China Study

Liu, Ping; Li, Qiming; Bian, Jing; Song, Liangliang; Xiahou, Xiaer

doi:10.3390/ijerph15071359

Cited by 56 publications

(38 citation statements)

References 60 publications

(60 reference statements)

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“…Hydrogeology, engineering geology and surrounding environment are important basic data in the process of metro construction. The uncertain factors such as complex surrounding environment, hydrogeological conditions, and engineering geological conditions constitute the dangerous source environment of the construction safety risk, which is the original factor impact of the safety risk of metro construction [76]. Because the dangerous source environment provides technical parameters for design scheme, the uncertainty of environment of dangerous sources will have an impact on the engineering design and the construction scheme design.…”

Section: Determination Of Influencing Factors Of Safety Risk In Metromentioning

confidence: 99%

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Liu

Xie

Bian

et al. 2020

IJERPH

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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.

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Section: Determination Of Influencing Factors Of Safety Risk In Metromentioning

confidence: 99%

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Liu

Xie

Bian

et al. 2020

IJERPH

Self Cite

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“…Strong subjectivity, the relationship between the elements in the system depends on people's experience [28] Safety risk analysis…”

Section: Construction Safety Risk Managementmentioning

confidence: 99%

A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project

Jiang

Wang

et al. 2020

IJERPH

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Early decision-making and the prevention of construction safety risks are very important for the safety, quality, and cost of construction projects. In the field of construction safety risk management, in the face of a loose, chaotic, and huge information environments, how to design an efficient construction safety risk management decision support method has long been the focus of academic research. An effective approach to safety management is to structuralize safety risk knowledge, then identify and reuse it, and establish a scientific and systematic construction safety risk management decision system. Based on ontology and improved case-based reasoning (CBR) methods, this paper proposes a decision-making approach for construction safety risk management in which the reasoning process is improved by integrating a similarity algorithm and correlation algorithm. Compared to the traditional CBR approach in which only the similarity of information is considered, this method can avoid missing important correlated information by making inferences from multiple sources of information. Finally, the method is applied to the safety risks of subway construction for verification to show that the method is effective and easy to implement.

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“…Under the principle of associative matrix in graph theory, the ISM decomposes the complex system into several subsystems, creating an intuitive and well-structured hierarchical model. This approach boasts good adaptability in analysis on the factors, safety, and risk of complex systems [30]. In general, the ISM is implemented in four steps:…”

Section: The Ismmentioning

confidence: 99%

Interpretative Structural Modelling on Generation Mechanism of Train Operation Conflicts in High Speed Railway

Wang¹,

Kang²,

Song³

et al. 2020

IJSSE

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To detect and mitigate the operation conflicts of high-speed trains, it is critical to clarify the generation mechanism of train operation conflicts (TOCs) in high speed railway (HSR). Taking train delays as the precondition of the TOCs, this paper sorts out the main causes of the TOCs into four aspects, namely, equipment facilities, human behaviour, external environment, and organization management, and obtains a total of direct and indirect impact factors for HSR TOCs. Then, the interpretative structural modelling (ISM) was adopted to construct the adjacency matrix between these factors, and calculate the reachability matrix. On this basis, a directed hierarchical graph was plotted for the TOC causes based on the hierarchical relationship between the factors. The results show that HSR TOCs are directly caused by equipment facilities, transport organizations, and dispatcher professionality, and indirectly induced by natural environment, equipment operating environment, unexpected passenger flow, as well as the psychological quality, educational level, and years of service of dispatchers; in addition, the working environment, and the management of equipment and dispatchers are the deep-seated reasons for the TOCs. The research results provide new insights into the intelligent dispatching command of the HSR.

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Using Interpretative Structural Modeling to Identify Critical Success Factors for Safety Management in Subway Construction: A China Study

Cited by 56 publications

References 60 publications

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project

Interpretative Structural Modelling on Generation Mechanism of Train Operation Conflicts in High Speed Railway

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