基于粒子群算法优化小波支持向量机的岩土力学参数反演  

Inversion of rock and soil mechanics parameters based on particle swarm optimization wavelet support vector machine

作　　者：阮永芬[1] 高春钦 刘克文 贾荣谷 丁海涛 RUAN Yong-fen;GAO Chun-qin;LIU Ke-wen;JIA Rong-gu;DING Hai-tao(Faculty of Civil Engineering and Mechanics,Kunming University of Science and Technology,Kunming,Yunnan 650500,China;Pinggao Group International Engineering Co.,Ltd.,Zhengzhou,Henan 450018,China;Yunnan Construction Investment First Investigation and Design Co.,Ltd.,Kunming,Yunnan 650031,China)

机构地区：[1]昆明理工大学建筑工程学院,云南昆明650500 [2]平高集团国际工程有限公司,河南郑州450018 [3]云南建投第一勘察设计有限公司,云南昆明650031

出　　处：《岩土力学》2019年第9期3662-3669,共8页Rock and Soil Mechanics

基　　金：云南省重点研发计划（社会发展领域）（No.2018BC008）~~

摘　　要：常用的确定岩土力学参数的方法有原位测试和室内试验两种,但都存在一定的局限性,参数选择的合理与否,对设计计算及数值模拟分析结果的有效性影响很大。支持向量机法在理论基础和求解算法方面都具有明显优势,为确保岩土力学参数取值的合理性,采用支持向量机法对岩土力学参数进行反演。先通过小波分析理论构造出支持向量机的核函数,再用粒子群算法(PSO)分别优化Morlet小波、Mexico小波和RBF函数的支持向量机模型参数,通过小波支持向量机模型建立反演参数与沉降值间的非线性映射关系。根据正交试验和均匀试验对需反演的岩土力学参数进行设计,结合有限元软件进行计算分析,得到学习样本和测试样本。分别采用Morlet小波、Mexico小波和RBF函数得出的预测结果和原始数据进行对比分析,发现采用Morlet小波核函数预测效果更佳。使用Morlet小波核函数预测的参数输入到Midas模型中计算建筑物最终沉降量,比较计算值与实际监测值,其相对误差不超过8.1%。研究结果表明,该方法在岩土工程参数的反演中具有良好的应用价值,对今后岩土力学参数的确定及校核提供了一种新方法。In-situ testing and laboratory testing are two common methods for determining rock and soil mechanics parameters, but there are certain limitations of both methods. The rationality of the parameter selection greatly affects the effectiveness of design calculations and numerical simulation results. The support vector machine method shows obvious advantages on the theoretical basis and solving algorithm. To guarantee the rationality of rock and soil mechanics parameters, the support vector machine method is applied to conduct invert calculations of the rock and soil mechanics parameters. Firstly, the kernel function of the support vector machine is constructed using wavelet analysis theory, and then the support vector machine model parameters of Morlet wavelet, Mexico wavelet and RBF function are optimized using particle swarm optimization(PSO). Finally, the nonlinear mapping relationship between the inversion parameters and the displacement values is established through the wavelet support vector machine model. Based on the orthogonal test and uniform test, this study designs the rock and soil mechanics parameters, which need invert calculation. Meanwhile, by combining the calculation and analysis results through finite element software, the learning samples and the test samples are obtained. After the initial data is compared with the predicted results from the calculations of the Morlet wavelet, the Mexico wavelet, and the RBF function, respectively, it is found that the prediction result of the Morlet wavelet kernel function is more reliable and effective than those of the other two methods. The relative error between the calculated value and the actual monitoring value is no more than 8.1%, when parameters predicted by the Morlet wavelet kernel function are input into the Midas model to calculate the final settlement of the building. The research results show that this method presents good application value in the inverting calculation of geotechnical engineering parameters, and provides a new idea for the det

关 键 词：粒子群算法 小波核函数 支持向量机 反演 

分 类 号：TU452[建筑科学—岩土工程]