Supplementary information for 'Intelligent Decision Method for Stability Assessment of Shield Tunnel Based on Multi-objective Data Mining' from Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining

Due to the improper operation of the shield construction process and unknown geological survey, shield construction faces many risks in passing through the complex strata, among which the excavation face instability is the most serious disaster accident. To solve these issues, this research focused on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, the analytical formula of limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is given to assess the excavation face stability quantitatively. Then the rough set algorithm was used to analyze the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The BP neural network was used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10⁻⁴ was established. The prediction performance of BP was verified by comparing the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability.This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.