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On-line Access: 2021-08-20

Received: 2020-06-28

Revision Accepted: 2020-10-18

Crosschecked: 2021-07-22

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Aydin Shishegaran


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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.8 P.632-656


Surrogate models for the prediction of damage in reinforced concrete tunnels under internal water pressure

Author(s):  Alireza Bigdeli, Aydin Shishegaran, Mohammad Ali Naghsh, Behnam Karami, Arshia Shishegaran, Gholamreza Alizadeh

Affiliation(s):  School of Civil Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran; more

Corresponding email(s):   aydin_shishegaran@civileng.iust.ac.ir

Key Words:  Gene expression programming (GEP), Taguchi method, Finite element (FE) analysis, Effective tensile plastic strain (ETPS), Deflection, Damage

Alireza Bigdeli, Aydin Shishegaran, Mohammad Ali Naghsh, Behnam Karami, Arshia Shishegaran, Gholamreza Alizadeh. Surrogate models for the prediction of damage in reinforced concrete tunnels under internal water pressure[J]. Journal of Zhejiang University Science A, 2021, 22(8): 632-656.

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author="Alireza Bigdeli, Aydin Shishegaran, Mohammad Ali Naghsh, Behnam Karami, Arshia Shishegaran, Gholamreza Alizadeh",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Surrogate models for the prediction of damage in reinforced concrete tunnels under internal water pressure
%A Alireza Bigdeli
%A Aydin Shishegaran
%A Mohammad Ali Naghsh
%A Behnam Karami
%A Arshia Shishegaran
%A Gholamreza Alizadeh
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 8
%P 632-656
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000290

T1 - Surrogate models for the prediction of damage in reinforced concrete tunnels under internal water pressure
A1 - Alireza Bigdeli
A1 - Aydin Shishegaran
A1 - Mohammad Ali Naghsh
A1 - Behnam Karami
A1 - Arshia Shishegaran
A1 - Gholamreza Alizadeh
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 8
SP - 632
EP - 656
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2000290

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.


创新点:1. 开发替代模型,例如主成分回归分析(PCR)、多元自然对数方程回归(MLnER)和基因表达编程(GEP);2. 预测RCT的受损表面百分比(PDS)、有效拉伸塑性应变(ETPS)、RCT的最大挠度以及RCT的顶部挠度.
方法:1. 开发可模拟内部水压作用下RCT性能的有限元模型,采用线性和非线性模型来预测PDS、最大ETPS、RCT的最大挠度以及RCT的顶部挠度.2. 考虑48种混凝土配合比设计,其中36种是由田口方法提出的,剩下的通过作者建议给出.输入变量包括混凝土的抗压和抗拉强度、纵向钢筋的尺寸、横向钢筋的直径和内部水压.
结论:1. 内部水压对PDS、最大ETPS、RCT最大挠度和RCT顶部挠度影响最大.2. 抗压和抗拉强度对PDS、最大ETPS、RCT最大挠度和RCT顶部挠度值有显著影响.3. GEP方法能高精度预测结构损伤、最大ETPS、RCT的最大挠度和RCT顶部挠度.4. 安全系数应被应用于GEP模型的方程以提高其可靠性,尤其是使用这些公式来预测PDS和最大ETPS时.


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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