上海交通大学学报

上海交通大学学报 >

2016 , Vol. 50 >Issue 01: 1 - 7

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2016.01.001

基坑变形数值分析中土体力学参数的确定方法

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  • 上海交通大学土木工程系，上海 　200240
赵香山（1991-)，女，四川省巴中市人，硕士生，主要研究方向为基坑变形预测和反分析．|王建华（联系人），男，教授，博士生导师，电话（Tel.):021-34207002; E-mail: wjh417@sjtu.edu.cn.

收稿日期: 2014-12-07

  网络出版日期: 2016-01-29

基金资助

国家自然科学基金(41330633);上海市科学技术委员会科研项目(13231200602)

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Determination of Soil Parameters for Numerical Simulation of an Excavation

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  • Department of Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China

Received date: 2014-12-07

  Online published: 2016-01-29

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摘要

依托上海地区地铁车站基坑，提出了结合有限元的多目标反分析参数确定理论与算法，以不同开挖工况中的围护墙体水平位移和临近地表沉降作为目标，分析确定典型软土层的修正剑桥模型参数．对基坑开挖施工过程用ABAQUS进行有限元模拟，同时选取AMALGAM算法，利用Matlab对基坑工程中的土体参数进行反分析．结果发现，根据基坑开挖前一步实测变形确定的计算参数能够有效预测下一步开挖变形，开挖到一定深度时所确定的土体参数能够准确地预测后续步开挖的影响．证实了多目标反分析对后续施工步预测的精确度，并且得到了长寿路车站基坑第2～6步的9个土体参数反分析结果，可作为基坑工程的借鉴．

关键词： 土体参数; 基坑工程; 数值分析; Pareto最优; 变形预测

本文引用格式

赵香山, 陈锦剑, 黄忠辉, 王建华 . 基坑变形数值分析中土体力学参数的确定方法[J]. 上海交通大学学报, 2016 , 50(01) : 1 -7 . DOI: 10.16183/j.cnki.jsjtu.2016.01.001

Abstract

It is relatively difficlut to determine the parameters adopted in numerical simulation of excavation due to the non-uniformity and uncertainties of soil. In this paper, an inverse analysis method for deep excavation based on the Pareto multiobjective optimization was proposed. The excavation process was first simulated in ABAQUS. A multialgorithm genetically adaptive multiobjective method (AMALGAM) was then invoked by Matlab to identify the soil parameters in excavation with multiple types of field observations. The proposed method was applied to the analysis of a well-instrumented deep well to predict the wall deflection, as well as the ground surface settlement in following excavation with acceptable accuracy. In addition, the nine soil parameters obtained from the inverse analysis on Changshou Road Station excavation were presented, which probably could be uesd as reference for other excavations.

Key words： soil parameter; excavation engineering; numerical analysis; Pareto optimality; displacement prediction

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