基于改进GA-PSO的双线隧道开挖地表沉降的智能反分析法  

Intelligent Back Analysis Method for Surface Settlement during Excavation of Double Track Tunnel Based on Improved GA-PSO

作　　者：申庆梦 杨玉坤 李越 李涛[2,3] 王晓龙 李峰[1] SHEN Qingmeng;YANG Yukun;LI Yue;LI Tao;WANG Xiaolong;LI Feng(School of Emergency Management and Safety Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;School of Mechanics and Civil Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;State Key Laboratory for Tunnel Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;Beijing Urban Construction Design and Development Group Co.,Ltd,Beijing 100034,China)

机构地区：[1]中国矿业大学(北京)应急管理与安全工程学院,北京100083 [2]中国矿业大学(北京)力学与土木工程学院,北京100083 [3]中国矿业大学(北京)隧道工程灾变防控与智能建养全国重点实验室,北京100083 [4]北京城建设计发展集团股份有限公司,北京100034

出　　处：《河南科技大学学报(自然科学版)》2024年第6期69-80,M0007,共13页Journal of Henan University of Science And Technology:Natural Science

基　　金：国家自然科学基金项目(51508556);中央高校基本科研业务费专项资金项目(2020YJSLJ14);越崎青年学者资助项目(800015z1166)。

摘　　要：为解决随机介质理论在双线隧道沉降研究中ΔA_(1)、ΔA_(2)(先行隧道和后行隧道的断面收敛面积)和tanβ_(1)、tanβ_(2)(先行隧道和后行隧道的主要影响角的正切值)取值准确性的问题,提出一种基于改进遗传算法-粒子群算法(GA-PSO)的智能位移反分析方法。该方法利用拉丁超立方采样法在多维参数空间内获取初始样本,并采用实数编码和君主选择交叉策略对标准遗传算法(GA)进行改进,进而通过粒子群算法(PSO)对改进GA进化筛选出的优良个体进行进一步的全局搜索和局部搜索,保证高效地搜索最优参数。在北京地铁12号线西坝河站-三元桥站区间断面实例反分析中,比较计算值和实测值,获取合适参数取值,并对后续开挖的隧道沉降进行预测。结果表明:双线隧道多参数更符合工程实际情况,通过对比计算沉降值和实测沉降值,发现其相对误差为1.36%~12.28%。同时,改进GA-PSO混合算法相较于单一改进GA、PSO算法精度有所提高。该研究结果扩大了随机介质理论的适用范围,丰富了双线隧道随机介质理论参数取值方法。To address the value accuracy ofΔA_(1),ΔA_(2)(the sectional convergence areas of the preceding and subsequent tunnels)and tanβ_(1),tanβ_(2)(the tangent values of the main influence angles of the preceding and subsequent tunnels)in the study of double track tunnel settlement under random media theory,an intelligent displacement inverse analysis method was proposed based on an improved genetic algorithm-particle swarm optimization(GA-PSO).The method used Latin hypercube sampling to obtain initial samples in a multi-dimensional parameter space.Real-valued encoding and a monarch selection crossover strategy were applied to improve the standard GA.PSO was then employed to further perform global and local searches in the high-quality individuals which selected by the improved GA,ensuring an efficient search for optimal parameters.In the inverse analysis of the section example between Xibahe station and Sanyuanqiao station of Beijing metro line 12,the calculated values were compared with the measured values.Suitable parameter values were obtained,and tunnel settlement for subsequent excavation was predicted.The results show that the double track tunnel multi-parameters model is better aligns with engineering practice.By comparing the calculated settlement values with the measured settlement values,it is found that the relative error ranges from 1.36%to 12.28%.At the same time,the improved GA-PSO hybrid algorithm show more accuracy compared to the individual improved GA and PSO algorithms.The research results expand the applicability of random medium theory and enriches the method for determining parameter values in double track tunnel random medium theory.

关 键 词：双线隧道 智能算法 随机介质理论 位移反分析 

分 类 号：U45[建筑科学—桥梁与隧道工程]