PSO-LSSVM模型在位移反分析中的应用  

A model of PSO-LSSVM and its application to displacement back analysis

作　　者：邬凯[1] 盛谦[1] 梅松华[2] 李佳[3] 

机构地区：[1]中国科学院武汉岩土力学研究所岩土力学与工程国家重点实验室,武汉430071 [2]中南勘测设计研究院,长沙410014 [3]四川大学水电学院,成都610065

出　　处：《岩土力学》2009年第4期1109-1114,共6页Rock and Soil Mechanics

基　　金：国家自然科学基金重大研究计划项目(No.90715042);国家自然科学基金资助项目(No.50579071)。

摘　　要：提出了一种基于均匀设计原理、最小二乘支持向量机(LSSVM)和粒子群优化算法(PSO)的快速位移反分析方法。该方法利用均匀设计和有限差分法获得学习样本,再用粒子群算法搜索最优的最小二乘支持向量机模型参数。并用最小二乘支持向量机回归模型建立反演参数与监测点位移值之间的非线性映射关系,最后用粒子群算法从全局空间上搜索与实测位移最吻合的反演参数。该反演模型利用了粒子群算法高效简单、均匀设计构造高质量小样本以及最小二乘支持向量机的小样本、泛化性能好的特点。将该模型应用于龙滩水电站左岸地下厂房区岩体地应力场的反演分析中,计算结果与实测的位移值和地应力值均吻合较好,说明了该模型在岩土工程快速反演分析中具有良好的应用价值。A displacement back analysis method is proposed by combining uniform design method, the least squares support vector machine （LSSVM）and particle swarm optimization （PSO）. The learning samples are produced by uniform design and FLAC^3D. The parameters of LSSVM are determined in global optimal by PSO. Thus, the LSSVM with optimal parameters are used to describe the nonlinear relationship between the back analysis parameters and displacements. The PSO is used again to search for the optimal rock mechanical parameters in global ranges. The displacement back analysis method combines the advantages of three algorithms. The PSO has merits such as easy operation, highly active; the LSSVM has merits such as small sample, good generalization and the uniform design method can produce small sample.The model of PSO-LSSVM is used to make back-analysis of in-situ stress field of the underground powerhouse area of Longtan Hydropower Station. By comparison of measured and calculated displacements and in-situ stresses of rock masses, it is shown that the obtained results are satisfactory. The results also indicate that the model can be well applied to the fast displacement back analysis in geotechnical engineering.

关 键 词：位移反分析 最小二乘支持向量机 粒子群算法 均匀设计 快速反演 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]