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基于位移反演的隧道初期支护可靠度分析

Reliability analysis of tunnel initial support based on displacement inversion
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摘要 为研究龄期变化和围岩应力释放对隧道初期支护强度和可靠度的影响。采用白阳山非机动车隧道的数据,对其支护可靠度进行计算及对比分析。先考虑实际隧道开挖过程中应力释放和混凝土硬化的影响,根据现场监控量测数据,基于BP神经网络算法进行反演,得到围岩力学参数和开挖过程中隧道监测点的位移量,并将反演计算得到的位移值与现场实测值进行对比验证,并据此对围岩应力变化情况进行分析。再根据衬砌极限抗拉与抗压方程,计算得到隧道开挖过程中隧道初期支护可靠度的变化规律。研究结果表明:衬砌应力增长主要发生在施加完成后的初始阶段,完成施加衬砌2~7 d后可靠度最低,该期间衬砌最有可能遭到破坏,且最小可靠度出现在拱腰位置;如果考虑应力释放及混凝土硬化过程的影响,衬砌可靠度会更低。该分析结果可为隧道安全施工提供借鉴。 In the study,the effects of concrete age and stress release of surrounding rocks on the initial support strength and reliability of concrete were investigated.Taking the non-motor vehicle tunnel of baiyangshan as the engineering background,the supporting reliability was calculated and compared.Firstly,considering the influences of stress release of surrounding rock and concrete hardening,the mechanical parameters of surrounding rock and the displacement of monitoring points during excavation process were obtained based on the in-situ monitoring data and BP neural network algorithm.The inversion results were compared to the monitoring data,based on which the variation of stress in surrounding rock was analyzed.Then,the variation of reliability of initial support during excavation was calculated based on the ultimate tensile and compressive formulas of the lining.Test results show that the stress rise of lining mainly occurs in the initial phase after construction,the lowest reliability of lining occurs at the arch waist position in 2-7 days,when the lining is most likely to be destroyed.When considering the effects of stress release and concrete hardening,the reliability will be further lower.This analysis results can provide reference to the safe construction of tunnel.
作者 陈叶辉 凌同华 曾婉琳 刘芳 CHEN Yehui;LING Tonghua;ZENG Wanlin;LIU Fang(School of Civil Engineering,Changsha University of Science&Technology,Changsha 410114,China)
出处 《交通科学与工程》 2022年第2期68-74,108,共8页 Journal of Transport Science and Engineering
基金 国家自然科学基金项目(52078061)。
关键词 隧道 初期支护 BP神经网络 可靠度分析 tunnel primary support BP neural network reliability analysis
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