Risk Assessment Method of Railway Engineering Technology Innovation in Complex Areas

Yuefeng Shi, Shiyu Tian, Yongjun Chen (), Chaoxun Cai, Honghao Guan and Xiaojian Li ()
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Yuefeng Shi: National Key Laboratory of High Speed Railway Track System, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Shiyu Tian: School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Yongjun Chen: School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Chaoxun Cai: National Key Laboratory of High Speed Railway Track System, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Honghao Guan: School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Xiaojian Li: School of Civil Engineering, Central South University, Changsha 410075, China

Mathematics, 2025, vol. 13, issue 12, 1-23

Abstract: Extreme climatic conditions and active geological factors posed challenges in evaluating disaster risk trends in railway construction projects and identifying key influencing factors. Traditional technology is difficult to adapt to the exploration and unknown construction process of railway engineering in complex and difficult areas. Therefore, there is an urgent need for technological innovation. The study used the Vague set theory method to screen and determine a list of risk factors for railway engineering technology innovation in complex areas, including 5 primary risk indicators and 26 secondary risk indicators. Based on this, an evaluation system for risk factors of railway engineering technology innovation in complex areas was established. Secondly, this study combined the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) with a cloud model to comprehensively evaluate risks and create cloud maps. Based on the calculation results, the risk level of railway engineering technology innovation risk factors in complex areas is obtained as environmental factors > technological factors > social factors > management factors > resource factors. The “combination weighting-cloud model” framework adopted in this study effectively overcomes the problem of insufficient representation of traditional single weighting method by integrating subjective and objective weight optimization and dynamic risk coupling analysis and significantly improves the multidimensional adaptability and dynamic evaluation accuracy.

Keywords: complex areas; technological innovation; cloud model; risk assessment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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