基于小波特征提取与GBDT的激光致声混凝土缺陷检测OA

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.