Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites

被引：12

作者：

Lin, Shibin ^{[1
,2
]}

Ashlock, Jeramy C. ^{[3
]}

Zhao, Guochen ^{[1
,2
]}

Lai, Qinghui ^{[1
,2
]}

Xu, Longjun ^{[1
,2
]}

Zhai, Changhai ^{[4
,5
]}

机构：

[1] Jianghan Univ, State Key Lab Precis Blasting, Wuhan 430056, Peoples R China

[2] Jianghan Univ, Hubei Key Lab Blasting Engn, Wuhan 430056, Peoples R China

[3] Iowa State Univ, Dept Civil Construct & Environm Engn, Ames, IA 50011 USA

[4] Harbin Inst Technol, Control Minist Educ, Key Lab Struct Dynam Behav, Harbin 150090, Peoples R China

[5] Harbin Inst Technol, Mitigat Civil Engn Disaster Minist Ind & Informat, Key Lab Smart Prevent, Harbin 150090, Peoples R China

来源：

COMPUTERS AND GEOTECHNICS | 2023年 / 160卷

基金：

美国国家科学基金会;

关键词：

Nondestructive testing; Surface waves; Global optimization; Inversion; Shear -wave velocity; PHASE-VELOCITY; ALGORITHM;

D O I：

10.1016/j.compgeo.2023.105525

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A new hybrid genetic-simulated-annealing (GSA) optimization algorithm is introduced to solve the multivariable minimization problem for surface wave inversion. The algorithm is effective for both global and local searches due to its combination of the reproduction and selective generation schemes from genetic algorithms (GA) with the nonlinear scaling fitness function and perturbation scheme from simulated annealing (SA). The hybrid GSA algorithm can reduce the risk of a solution becoming trapped in a local minimum and improve global searching efficiency. A mathematical test function as well as surface wave examples are used to examine the advantages and performance of the GSA algorithm. Comparisons of GA, SA, and GSA inversion results demonstrates that GSA can yield the smallest uncertainty and greatest efficiency, and improve the statistical confidence of using surface wave testing for shear-wave velocity profiling.

引用

页数：10

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