四川轻化工大学学报（自然科学版）2025，Vol.38Issue(2)：91-99,9.DOI:10.11863/j.suse.2025.02.11
基于KPCA-ISOA-KELM的冷缝检测分类研究
Research on Cold Joint Detection and Classification Based on KPCA-ISOA-KELM
摘要
Abstract
The cold joint detection classification method,named KPCA-ISOA-KELM,is proposed to address several existing issues in current cold joint detection techniques,such as low detection efficiency,high data parsing requirements,and limited exploitation of waveform information,which combines Kernel Principal Component Analysis(KPCA),Improved Seagull Optimization Algorithm(ISOA),and Kernel Extreme Learning Machine(KELM).Firstly,data sets are collected using the impact elastic wave method in suspected cold joint areas within tunnels,and KPCA is utilized to perform dimensionality reduction on the data.Then,ISOA is employed to optimize the parameters of KELM.Finally,the optimized KELM classifier is utilized to classify the detection data.Comparative validation is conducted against KPCA-SOA-KELM,KPCA-FOA-KELM,and KPCA-CNN-LSTM models.The results indicate that the KPCA-ISOA-KELM model provides valuable guidance for on-site detection and achieves a higher accuracy rate of 96.00%and a weighted F1-score of 0.9598 compared to the other three models.关键词
冷缝/冲击弹性波/核主成分分析/改进海鸥优化算法/核极限学习机/数据分类Key words
cold joint/impact elastic wave/kernel principal component analysis/improved seagull optimization algorithm/kernel extreme learning machine/data classification分类
交通工程引用本文复制引用
朱洪谷,吴佳晔..基于KPCA-ISOA-KELM的冷缝检测分类研究[J].四川轻化工大学学报（自然科学版）,2025,38(2):91-99,9.基金项目
四川省科技成果转移转化示范项目(2023ZHCG0020) （2023ZHCG0020）
