Model for Evaluating Residual Bearing Capacity of Shield Tunnel Lining Structures After a Fire Event

被引：0

作者：

Lu, Yao-Liang ^{[1
]}

Jiang, Jian ^{[1
]}

Wang, Bo ^{[1
]}

Li, Hai-Feng ^{[2
]}

Chen, Wei ^{[1
]}

Ye, Ji-Hong ^{[1
]}

机构：

[1] School of Mechanics and Civil Engineering, China University of Mining and Technology, Jiangsu, Xuzhou

[2] School of Civil Engineering, Huaqiao University, Fujian, Xiamen

来源：

Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2024年 / 37卷 / 09期

基金：

中国国家自然科学基金;

关键词：

analytic hierarchy process; BP neural network; numerical simulation; residual bearing capacity; shield tunnel; tunnel engineering;

D O I：

10.19721/j.cnki.1001-7372.2024.09.005

中图分类号：

学科分类号：

摘要：

The accuracy and speed of evaluating the residual bearing capacity of the tunnel structure after a fire event directly affects the reliability and economy of emergency disposal and repair work. Herein, the shield tunnel was regarded as the research object and the structural damage index system after a fire event was determined by combining the analytic hierarchy process and numerical simulations. The influence of each index on the residual bearing capacity of tunnel structure was studied. The damage degree was divided into five grades: mild, moderate, severe, extreme, and damage, based on cluster analysis. The mechanical performance evaluation model of the shield tunnel structure after a fire event was based on a neural network. The results show that the damage area, spalling depth, concrete deterioration depth, concrete strength reduction, and bolt strength reduction are the main factors affecting the residual bearing capacity of shield tunnel after a fire event. Following the increase in the deterioration degree of each factor, the mechanical properties of the structure decrease, but the decrease range and manifestation differ significantly. The BP neural network can be effectively used to evaluate the performance of the shield tunnel after a fire event, and the average error is less than 10%. © 2024 Chang'an University. All rights reserved.

引用

页码：55 / 67

页数：12

共 25 条

[1] Review on China's traffic tunnel engineering research: 2022 [J], China Journal of Highway and Transport, 35, 4, pp. 1-40, (2022)
[2] YAN Zhi-guo, A study on mechanical behaviors and fireproof methods of tunnel lining structure during and after fire scenarios [D], (2007)
[3] YE Ji-hong, LI Jun-jie, Fire experiments of shield tunnel segments and joints [J], China Journal of Highway and Transport, 35, 9, pp. 331-339, (2022)
[4] ZHANG Li, HE Chuan, FENG Kun, Et al., Influence of bolts on the compression-bending capacity of segmental joints of shield tunnels [J], China Journal of Highway and Transport, 35, 11, pp. 195-203, (2022)
[5] ZHANG Dong-mei, LIU Jie, LI Bao-jun, Et al., Shearing behavior of circumferential joints with oblique bolts in large diameter shield tunnel [J], China Journal of Highway and Transport, 33, 12, pp. 142-153, (2020)
[6] GENG Ping, WANG Qi, GUO Xiang-yu, Et al., Pull-out test of longitudinal joints of shield tunnel [J], China Journal of Highway and Transport, 33, 7, pp. 124-134, (2020)
[7] ZHANG Gao-le, ZHANG Wen-jun, YU Guo-lun, Et al., Model test on thermomechanical coupling of shield tunnel lining under high fire temperature [J], China Journal of Highway and Transport, 32, 7, pp. 120-128, (2019)
[8] XIAO Ming-qing, FENG Kun, ZHANG Li, Et al., A calculation model of flexural bearing capacity of segmental joint for shield tunnels [J], China Civil Engineering Journal, 52, 11, pp. 108-119, (2019)
[9] XU Ben, WANG Hua-lao, XIA Cai-chu, Study on comprehensive evaluation of shield tunnel structural defections [J], Chinese Journal of Underground Space and Engineering, 6, 1, pp. 201-207, (2010)
[10] DENG Lu, CHU Hong-hu, LONG Li-zhi, Et al., Review of deep learning-based crack detection for civil infrastructures [J], China Journal of Highway and Transport, 36, 2, pp. 1-21, (2023)

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