岩土工程学报2026，Vol.48Issue(4)：687-696,10.DOI:10.11779/CJGE20241295
基于自适应采样和代理模型的滑带土参数反演
Parameter inversion of slip zone soil based on adaptive sampling and surrogate models
高玮 1李培远 1赵志浩 1张林 1钱宇晨1
作者信息
- 1. 河海大学土木与交通学院,江苏 南京 210098
- 折叠
摘要
Abstract
To solve shortcomings of sampling methods for constructing training dataset of surrogate models in current inverse analysis,this study introduces the CV-Voronoi adaptive sequential sampling method for generating training samples.This sampling method does not require the determination of the sample size in advance and can adaptively add new samples based on the information of the existing sample points.Moreover,the sparrow search algorithm(SSA)is employed to optimize the twin support vector regression(TSVR)model,and the SSA-TSVR surrogate model is then generated.Based on the adaptive sequential sampling method and SSA-TSVR model,using SSA as the optimization algorithm,a new inversion technique is proposed.Using the shear strength parameter inversion of the Baishuihe landslide slip zone soil as an example,the new inversion method is validated through engineering application.The effects of different sample generation methods(adaptive sequential sampling,orthogonal design,and uniform design)and surrogate models(SVR,TSVR,SSA-SVR,and SSA-TSVR)on the inversion results are compared.The results show that the adaptive sequential sampling method has a significant advantage,reducing the inversion error by more than half.This method not only significantly improves the inversion performance,but also achieves higher accuracy with fewer samples.The SSA-TSVR surrogate model offers higher inversion accuracy and computational speed,providing a new approach for inversion analysis of geotechnical engineering mechanical parameters.关键词
强度参数/自适应序列采样/反演/SSA-TSVR代理模型/滑带土Key words
strength parameters/adaptive sequential sampling/inverse analysis/SSA-TSVR surrogate model/slip zone soil分类
建筑与水利引用本文复制引用
高玮,李培远,赵志浩,张林,钱宇晨..基于自适应采样和代理模型的滑带土参数反演[J].岩土工程学报,2026,48(4):687-696,10.
