首页|火灾后盾构隧道衬砌结构残余承载力评估模型研究

火灾后盾构隧道衬砌结构残余承载力评估模型研究

扫码查看
火灾后如何准确、迅速地评估隧道结构残余承载力,直接影响着应急处置及修复工作的可靠性和经济性.以盾构隧道衬砌结构为研究对象,结合层次分析法和数值模拟确定火灾后结构损伤指标体系,研究各指标对隧道结构残余承载力的影响规律;利用聚类分析理论将损伤程度划分为轻度损伤、中度损伤、严重损伤、极度损伤、破坏5个等级,并基于神经网络建立盾构隧道结构火灾后力学性能评估模型.研究结果表明:损伤面积、剥落深度、混凝土劣化深度、混凝土强度折减和螺栓强度折减是影响盾构隧道火灾后残余承载力的主要因素,随着各因素劣化程度的增加,结构力学性能下降,但下降幅度与表现形式有较大差异;BP神经网络可有效用于盾构隧道火灾后性能评估,预测结果平均误差在10%以内.
Model for Evaluating Residual Bearing Capacity of Shield Tunnel Lining Structures After a Fire Event
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%.

tunnel engineeringshield tunnelnumerical simulationresidual bearing capacityanalytic hierarchy processBP neural network

陆尧亮、姜健、王波、李海锋、陈伟、叶继红

展开 >

中国矿业大学力学与土木工程学院,江苏徐州 221116

华侨大学土木工程学院,福建厦门 361021

隧道工程 盾构隧道 数值模拟 残余承载力 层次分析法 BP神经网络

国家重大科研仪器研制项目国家消防救援局科技计划项目中国矿业大学研究生创新计划项目中央高校基本科研业务费专项资金项目江苏省研究生科研与实践创新计划项目

521278142023XFCX182023WLKXJ0522023XSCX014KYCX23_2733

2024

中国公路学报
中国公路学会

中国公路学报

CSTPCD北大核心
影响因子:1.607
ISSN:1001-7372
年,卷(期):2024.37(9)