基于MLS-SVM的结构整体可靠度与全局灵敏度分析  

ANALYSIS OF GLOBAL RELIABILITY AND SENSITIVITY OF STRUCTURES BASED ON MLS-SVM

作　　者：吕大刚[1,2] 李功博 宋彦 LÜDa-gang;LI Gong-bo;SONG Yan(Key Lab of Structure Dynamic Behavior and Control of China Ministry of Education,Harbin Institute of Technology,Harbin 150090,China;Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology,Harbin 150090,China)

机构地区：[1]哈尔滨工业大学,结构工程灾变与控制教育部重点实验室,哈尔滨150090 [2]哈尔滨工业大学,土木工程智能防灾减灾工业和信息化部重点实验室,哈尔滨150090

出　　处：《工程力学》2022年第S01期92-100,共9页Engineering Mechanics

基　　金：国家重点研发计划项目(2021YFB2600500);国家自然科学基金面上项目(52078176)。

摘　　要：作为一种有效的代理模型,支持向量机(SVM)以统计学习中的结构风险最小化原则为基本原理,在具有隐式极限状态函数的结构可靠度分析中得到了广泛的应用。然而,传统的支持向量机在核函数的选择、全局基本变量空间建模、计算效率等方面还存在许多不足。针对这些不足,该文提出一种新的基于移动最小二乘(MLS)技术的支持向量机模型(MLS-SVM),可以在全局基本变量空间中具备自适应能力。该文将MLSSVM应用于复杂结构的整体可靠度和全局灵敏度分析,并将该模型与基于再生核函数的支持向量机(RPKSVM)及基于最小二乘的支持向量机(LS-SVM)进行比较分析,结果表明:该文提出的模型相较其他两种模型具有更高的精度和计算效率。As one of efficient surrogate models,the support vector machine(SVM),which is based on the principle of structural risk minimization in statistical learning,has been widely used in structural reliability analysis with implicit limit state functions.However,the traditional support vector machines still have many shortcomings,such as the selection of kernel function,global basic variable space modeling,computational efficiency,etc.In order to overcome these shortcomings,this paper proposes a new support vector machine model based on moving least squares(MLS)technology named MLS-SVM.With this model,the training sample sets can be adaptive in the global basic variable space.Then this model is applied to global reliability and sensitivity analysis of reinforced concrete(RC)frame structures,and then is compared with the support vector machines based on the regenerative kernel function(RK-SVM)and the least square technique(LS-SVM).It is shown by the numerical results that,compared with the two comparative models,the MLS-SVM model has higher accuracy and better computational efficiency.

关 键 词：支持向量机 移动最小二乘 钢筋混凝土框架 整体可靠度 全局灵敏度 

分 类 号：TU311.2[建筑科学—结构工程]