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Abstract: In the present study, the performance of reinforced concrete tunnel (RCT) under internal water pressure is evaluated by using nonlinear finite element analysis and surrogate models. Several parameters, including the compressive and tensile strength of concrete, the size of the longitudinal reinforcement bar, the transverse bar diameter, and the internal water pressure, are considered as the input variables. Based on the levels of variables, 36 mix designs are selected by the taguchi method, and 12 mix designs are proposed in this study. Carbon fiber reinforced concrete (CFRC) or glass fiber reinforced concrete (GFRC) is considered for simulating these 12 samples. Principal component regression (PCR), Multi Ln equation regression (MLnER), and gene expression programming (GEP) are employed for predicting the percentage of damaged surfaces (PDS) of the RCT, the effective tensile plastic strain (ETPS), the maximum deflection of the RCT, and the deflection of crown of RCT. The error terms and statistical parameters, including the maximum positive and negative errors, mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination, and normalized square error (NMSE), are utilized to evaluate the accuracy of the models. Based on the results, GEP performs better than other models in predicting the outputs. The results show that the internal water pressure and the mechanical properties of concrete have the most effect on the damage and deflection of the RCT.

内部水压作用下钢筋混凝土隧道的结构损伤预测替代模型

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