测试技术学报2026，Vol.40Issue(4)：515-521,7.DOI:10.62756/csjs.1671-7449.2026047
基于小波特征提取与GBDT的激光致声混凝土缺陷检测
Laser-Acoustic Concrete Defect Detection Based on Wavelet Feature Extraction and GBDT
摘要
Abstract
The quality of concrete directly impacts the safety and durability of modern infrastructure.Traditional manual assessment relies on empirical judgment,which is highly subjective and lacks consistency.A concrete defect detection method is proposed based on gradient boosting decision tree(GBDT)and laser-induced acoustic techniques.The wavelet packet decomposition(WPD)method is employed to decompose the vibration signals generated by laser-induced acoustics into multiple sub-bands,and a set of statistical features is extracted from each frequency band.The GBDT is then utilized to classify and identify these features.The proposed method is validated on a self-built dataset of concrete test blocks with defects.Experimental results demonstrate that the GBDT achieves an accuracy of 90%and a recall rate of 94%.This study confirms the effectiveness of wavelet feature extraction combined with GBDT in laser-induced acoustic detection of concrete defects.关键词
激光致声/小波包分解/机器学习/混凝土缺陷检测/无损检测Key words
laser-induced acoustics/wavelet packet decomposition/machine learning/concrete defect detection/non-destructive testing分类
信息技术与安全科学引用本文复制引用
李丰江,张政,胡振东,江森森,马秆,王思磊,李忠洋..基于小波特征提取与GBDT的激光致声混凝土缺陷检测[J].测试技术学报,2026,40(4):515-521,7.基金项目
国家自然科学基金资助项目(62305112,61735010) （62305112,61735010）
河南省自然科学基金资助项目(252300423425) （252300423425）
河南省重点研发与推广专项(科技攻关)资助项目(242102210156,252102211097) （科技攻关）
河南省高等学校重点科研项目计划资助项目(25A510010) （25A510010）
