Improved Risk Assessment of TBM Tunneling Collapse Based on Nonlinear-Cloud Model

Abstract

An innovative nonlinear enhancement technique that integrates the cloud model with Fuzzy Analytic Hierarchy Process (FAHP) is presented in this study. For the first time, this paper introduces TBM tunnelling parameters as evaluation indicators for tunnel construction collapse risk. Precise risk level thresholds are set for each indicator, establishing a comprehensive TBM construction collapse risk assessment system. Within this system, the cloud model is applied to accurately depict membership degrees, transcending the limitations of traditional reliance on functional formulas. Furthermore, the AHP is utilized to precisely calculate the weight vectors of the assessment indicators. Most significantly, this study introduces a nonlinear operator to achieve an efficient integration of the weight vectors with the fuzzy relation matrix. Based on the principle of maximum membership degree, the ultimate collapse risk level for TBM construction is determined. This method overcomes the shortcomings of traditional FAHP, which neglects the randomness in calculating membership degrees and the potential dilution of the influence of key risk factors when using l inear operators in comprehensive risk assessment. The model was applied and validated in a hydraulic tunnel construction project, demonstrating its innovation and reliability, thus providing new theoretical foundations and technical support for the field of tunnel construction risk assessment.