江淮水利科技2026，Vol.21Issue(1)：31-35,5.DOI:10.20011/j.cnki.JHWR.202601006
用于基桩缺陷智能识别模型的低应变法数据分析方法研究
Study on low-strain method data analysis for intelligent identification model of pile defects
韩剑飞1
作者信息
- 1. 安徽省·水利部淮河水利委员会水利科学研究院,安徽 合肥 230088
- 折叠
摘要
Abstract
The establishment of an intelligent identification model for pile defects requires a comprehensive and effective sample data set to exert its nonlinear regression and prediction capabilities.Obtaining sample data sets for machine learning is a key step in the process of model establishment.An improved data analysis method based on kernel principal component analysis(Pearson-KPCA)was proposed to provide understandable and sufficient sample data for the intelligent identification model of pile defects.The research results showed that the Pearson-KPCA algorithm had a significant effect on reducing the dimension of sample data,and the performance of the sample data set optimized by the algorithm in the intelligent recognition model of pile defects was obviously better than that of the traditional data analysis method.The research provided a reliable data processing idea and method for the intelligent analysis of low strain method detection results.关键词
基桩缺陷识别/低应变法/Pearson-KPCA模型/特征提取/核主成分分析/数据分析Key words
pile defect identification/low-strain method/Pearson-KPCA model/character extraction/kernel principal component analysis/data analysis分类
信息技术与安全科学引用本文复制引用
韩剑飞..用于基桩缺陷智能识别模型的低应变法数据分析方法研究[J].江淮水利科技,2026,21(1):31-35,5.
