基于支持向量机上海地区土体物理力学指标相关性研究  

Correlations between physical and mechanical property indexes of Shanghai soil based on support vector machine

作　　者：李志国 白瑞涵 刘旭进 王庶懋 Li Zhiguo;Bai Ruihan;Liu Xujin;Wang Shumao(East China Electric Power Design Institute Co.,LTD.of China Power Engineering Consulting Group,Shanghai 200063,China;Key Laboratory of Minstry of Education for Geomechanics and Embankment Engineering,Hohai University,Jiangsu Nanjing 210024,China;Geotechnical Engineering Research Center of Jiangsu Province,Hohai University,Jiangsu Nanjing 210024,China)

机构地区：[1]中国电力工程顾问集团华东电力设计院有限公司,上海200063 [2]河海大学岩土力学与堤坝工程教育部重点实验室,江苏南京210024 [3]河海大学江苏省岩土工程技术工程研究中心,江苏南京210024

出　　处：《地震科学进展》2023年第2期66-76,共11页Progress in Earthquake Sciences

基　　金：“基于数据挖掘技术的岩土工程勘察设计管理及优化平台研究”(GSKJ2-G02-2019)资助。

摘　　要：针对上海地区土体物理力学指标开展相关性分析,结合多个工程场地获取的土体室内试验数据,采用支持向量机算法构建了土体塑性指数、液性指数与压缩系数的相关性分析模型,并结合误差指标对模型参数进行优化。将支持向量机模型与传统的线性、多项式拟合方法结果对比分析,表明该模型预测结果与实际结果较为吻合,且该模型另一优势在于能够从更多的数据中进行更深度的挖掘来提升自身的鲁棒性。考虑到不同土体的工程性质差异较大,进一步研究该模型的预测性能与适用性,就每个测试样本点预测偏差与其物理指标建立二者的关系曲线,结果表明可塑性小的中压缩性土体相较于高压缩性土体的预测偏差更小,模型更加稳定与准确,可为上海地区土体压缩性相关研究提供参考。Correlation analysis of physical and mechanical indexes of Shanghai soil was carried out. Using the support vector machine algorithm,the authors constructed a correlation analysis model of soil plasticity index,liquidity index and compressibility coefficient based on the soil indoor test data obtained from several engineering sites. Then the model parameters were optimized by combining the error indexes. Comparing the results of support vector machine model with those of traditional linear and polynomial fitting methods,it was shown that the prediction results of the model are basically consistent with the actual results,and another advantage of model is that it can carry out deeper mining from more data to improve its robustness. Considering the engineering properties of different category soils are quite different,the authors further analyzed the performance and applicability of model,and established the relationship curve between the forecast bias of each testing sample and its physical indexes. The results indicate that the error of medium compressible soil is smaller than high compressible soil,and the model is more stable and accurate,which could provide a reference for the research of soil compressibility in Shanghai.

关 键 词：支持向量机 相关性分析 压缩系数 液性指数 塑性指数 

分 类 号：TU431[建筑科学—岩土工程]