Risk assessment of loess tunnel collapse during construction based on an attribute recognition model

Loess tunnels are prone to collapse during construction. The risk assessment of loess tunnel collapse is therefore of great significance to prevent collapse accidents. An attribute recognition model of loess tunnel collapse risk assessment is established based on attribute mathematical theory. Several risk assessment indexes, including surrounding rock lithology, topography, excavation span, burial depth, groundwater, rainfall, and construction technology and management level, are selected according to the statistical analysis of 62 cases of loess tunnel collapse. The indexes are described using qualitative and quantitative methods, and the grading standards for each index are determined. The frequency statistic method is used to compute the evaluation index weights. Attribute measurement functions are established to calculate the single-index and synthetic attribute measures, and a confidence criterion is adopted to determine the loess tunnel collapse risk level. The constructed attribute recognition model is used to evaluate the collapse risk of four loess tunnel projects. The results are consistent with the actual construction conditions, which verifies the reliability of the evaluation method and provides a new method to evaluate the collapse risk of loess tunnels during construction.