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    张坤勇, 苏政凯, 聂美军, 郭楷文, 沈小锐. 基于ISSA-ELM的土体参数反演与变形预测方法[J]. 应用基础与工程科学学报, 2024, 32(5): 1434-1448. DOI: 10.16058/j.issn.1005-0930.2024.05.017
    引用本文: 张坤勇, 苏政凯, 聂美军, 郭楷文, 沈小锐. 基于ISSA-ELM的土体参数反演与变形预测方法[J]. 应用基础与工程科学学报, 2024, 32(5): 1434-1448. DOI: 10.16058/j.issn.1005-0930.2024.05.017
    ZHANG Kunyong, SU Zhengkai, NIE Meijun, GUO Kaiwen, SHEN Xiaorui. Soil Parameter Inversion and Deformation Prediction Method Based on ISSA-ELM[J]. Journal of Basic Science and Engineering, 2024, 32(5): 1434-1448. DOI: 10.16058/j.issn.1005-0930.2024.05.017
    Citation: ZHANG Kunyong, SU Zhengkai, NIE Meijun, GUO Kaiwen, SHEN Xiaorui. Soil Parameter Inversion and Deformation Prediction Method Based on ISSA-ELM[J]. Journal of Basic Science and Engineering, 2024, 32(5): 1434-1448. DOI: 10.16058/j.issn.1005-0930.2024.05.017

    基于ISSA-ELM的土体参数反演与变形预测方法

    Soil Parameter Inversion and Deformation Prediction Method Based on ISSA-ELM

    • 摘要: 基坑工程有限元模拟中土体参数的选取对最终结果的准确性具有重要影响,快速、准确地获取土体参数及预测结构变形,对工程的建设具有重要意义.基于此,提出一种地下空间开发土体参数反演及地下结构变形预测方法,主要包括以下方面:(1)算法改进.在麻雀算法SSA基础上,通过引入Tent混沌扰动映射函数、Cubic混沌映射初始化种群及精英反向学习初始化种群构建改进的麻雀算法ISSA.对ISSA及其他4种算法的性能评估结果表明,ISSA算法在全局搜索以及收敛速度上有明显优势;(2)参数选取.基于能够反映基坑开挖土体应力状态的修正剑桥模型对离散程度较大的参数进行敏感性分析,得到参数关联性排序为κ>e0>M>λ>ν>K0;(3)模型构建.设计50组正交试验,进行考虑基坑施工过程的有限元模拟,并提取地连墙的水平位移作为学习样本.构建ISSA-ELM网络模型进行土体参数反演,并利用小样本检验网络模型的精度;(4)工程验证.基于监测数据进行土体参数的反演及地下结构的变形预测,结果表明后续工况下的反演分析预测值相对更接近监测值,数据的更新能让反演和预测更具准确性.

       

      Abstract: The selection of soil parameters in finite element simulation of foundation pit engineering has an important influence on the accuracy of final results.It is of great significance to obtain soil parameters quickly and accurately and predict structural deformation.Based on this,a method of soil parameter inversion and underground structure deformation prediction for underground space development is proposed,which mainly includes the following aspects:(1) Algorithm improvement.On the basis of sparrow algorithm SSA,an improved sparrow algorithm ISSA is constructed by introducing Tent chaotic disturbance mapping function,Cubic chaotic mapping initialization population and elite reverse learning initialization population;The performance evaluation results of ISSA and other four algorithms show that ISSA algorithm has obvious advantages in global search and convergence speed.(2) Parameter selection.Based on the modified Cambridge model which can reflect the stress state of soil in foundation pit excavation,sensitivity analysis was conducted on the parameters with greater dispersion degree,and the correlation order of parameters was κ>e0>M>λ>ν>K0.(3) Model construction.50 groups of orthogonal tests were designed to carry out finite element simulation considering the construction process of foundation pit,and the horizontal displacement of the connecting wall was extracted as the learning sample;The ISA-ELM network model was constructed to invert soil parameters,and the precision of the network model was tested with small samples.(4) Engineering verification.Soil parameter inversion and deformation prediction of underground structure are conducted based on monitoring data.The results show that the predicted value of inversion analysis under subsequent working conditions is relatively closer to the monitoring value,and the update of data can make the inversion and prediction more accurate.

       

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