开挖引起的隧道位移动态多目标优化反演预测

doi:10.16183/j.cnki.jsjtu.2021.282

摘要/Abstract

摘要：

控制基坑开挖对邻近既有隧道的扰动对设计与施工至关重要.基于多目标优化方法,融合基坑开挖中的多类型监测数据,反演识别关键土体参数,量化修正隧道位移的时间效应.同时,为提高动态代理模型的优化效率,提出一种基于自适应加点准则的动态多目标优化(DMO-AIC)方法.该方法考虑了工程优化中动态代理模型的计算冗余,设计了自适应加点判别策略,能够自主识别寻优路径上代理模型的无效更新.结果表明,该方法在保证算法寻优性能和收敛速度的同时,显著减少了训练代理模型所需的数值模型调用次数,有利于动态代理模型在工程优化中的应用.虚拟数值算例结果表明,DMO-AIC能考虑挡墙侧移、隧道位移等多目标响应并同时对其进行更新.将DMO-AIC方法应用于上海外滩596基坑工程,合理更新了时间效应,准确预测了基坑分步开挖引起的既有隧道竖向位移.

关键词: 动态多目标优化, 代理模型, 自适应加点准则, 基坑, 隧道, 时间效应

Abstract:

Control of the disturbed displacement of adjacent tunnel during excavation is a significant issue for design and construction. Based on the multi-objective optimization method, the multi-type monitoring data in the excavation of the excavation are integrated, the key soil parameters are inverted and identified, and the time effect of the tunnel displacement is quantified and corrected. A dynamic multi-objective optimization method with adaptive infill criterion (DMO-AIC) is proposed to improve the updating efficiency of dynamic surrogate models. The proposed method takes into account the computational redundancy of dynamic surrogate models in engineering optimization, and designs an adaptive point-adding discrimination strategy, which can autonomously identify invalid updates of surrogate models on the optimization path. The results show that the proposed DMO-AIC significantly reduces the invocations of the black-box model during optimization while ensuring the good search performance and the convergence speed of the algorithm. The improved computational efficiency of DMO-AIC is helpful for the application of dynamic surrogate models in engineering optimization. The results of the virtual numerical example show that DMO-AIC can predict and update multiple model responses during excavation, such as wall deflections and tunnel displacements. The engineering practice of Shanghai Bund 596 excavation indicates that the time effect is properly updated, and the staged vertical displacements of the adjacent tunnel are accurately predicted.

Key words: dynamic multi-objective optimization, surrogate model, adaptive infill criterion, excavation, tunnel, time effect

中图分类号:

TU43

何维, 孙宏磊, 陶袁钦, 蔡袁强. 开挖引起的隧道位移动态多目标优化反演预测[J]. 上海交通大学学报, 2022, 56(12): 1688-1699.

HE Wei, SUN Honglei, TAO Yuanqin, CAI Yuanqiang. Dynamic Multi-Objective Optimization Inverse Prediction of Excavation-Induced Tunnel Displacement[J]. Journal of Shanghai Jiao Tong University, 2022, 56(12): 1688-1699.

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测试函数	优化方法	输入参数维度	种群规模	初始代理模型训练样本数	最大迭代次数	折减系数初始值
ZDT2	DMO-AIC	8	50	50	100	0.5
	DMO	8	50	50	100	0.5
FON	DMO-AIC	6	100	100	300	0.5
	DMO	6	100	100	300	0.5

测试函数	优化方法	D^G	Δ	新增样本数
ZDT2	DMO-AIC	8.60×10^-3	0.29	30
	DMO	8.30×10^-3	0.33	93
FON	DMO-AIC	1.23×10^-2	0.40	97
	DMO	1.22×10^-2	0.39	214

指标	M	κ	ν
真实值	1.20	0.020	0.30
更新参数平均值	1.40	0.020	0.31
更新参数标准差	2.9×10^-3	5.0×10^-4	2.3×10^-3

步骤	进度	周期/d	步骤	开挖进度	周期/d
1	S2-A开挖至-1.7 m	1	12	S2-B开挖至-4.8 m	2
2	S1-A开挖至-1.7 m	24	13	S2-B开挖至-7.65 m	2
3	S2-A开挖至-6.0 m	2	14	S2-B开挖至-10.5 m	4
4	S2-A 开挖至-10.1 m	14	15	S2-B开挖至-14.25 m	2
5	S1-A 开挖至-5.9 m	10	16	S2-B开挖至坑底	9
6	S2-A开挖-13.85 m	3	17	S1-B开挖至-1.7 m	3
7	S1-A开挖至-9.9 m	12	18	S1-B开挖至-4.8 m	1
8	S2-A开挖至坑底	13	19	S1-B开挖至-7.5 m	1
9	S1-A开挖-13.7 m	8	20	S1-B开挖至-10.2 m	2
10	S1-A开挖至坑底	6	21	S1-B开挖至-14.0 m	3
11	S2-B开挖至-1.7 m	5	22	S1-B开挖至坑底	3

土层	土质	厚度/m	重度γ/(kg·m^-3)	卸荷模量E_u/MPa	泊松比ν	黏聚力c'/kPa	摩擦角φ'/(°)
1	水泥搅拌桩	0~22, 0~28	22.0	180.0	0.40	50.0	40.0
2	淤泥质黏土	0~10	18.5	18.0(10~50)	0.28	7.0	27.6
3	粉质黏土	10~29	17.5	28.5(15~90)	0.31	15.0(0.22~0.40)	29.5(17~35)
4	杂填土	29~39	18.0	25.2	0.28	10.4	22.0
5	江滩土	39~49	19.3	40.3(30~150)	0.27	5.0	30.4
6	砂质粉土	49~64	18.8	74.8(50~180)	0.27	5.0	33.4
7	粉砂层	64~80	20.2	94.3(60~220)	0.27	4.0	34.3