Detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion approach

被引:53
作者
Chen, Dongdong [1 ]
Montano, Victor [2 ]
Huo, Linsheng [1 ]
Fan, Shuli [1 ]
Song, Gangbing [2 ]
机构
[1] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian 116023, Liaoning, Peoples R China
[2] Univ Houston, Dept Mech Engn, Smart Mat & Struct Lab, Houston, TX 77004 USA
关键词
Concrete-filled steel tubular structures (CFST); Void detection; Support vector machine (SVM); Machine learning; Percussion; DEBONDING DETECTION; IMPACT-ECHO; BEHAVIOR; PERFORMANCE;
D O I
10.1016/j.conbuildmat.2020.119761
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Concrete-filled steel tubular (CFST) structures are essential load-bearing components in many civil engineering structures. Subsurface voids between the contacting surface of the concrete and steel in a CFST structure reduce the load-bearing capacity of the CFST structure. This paper presents a novel, non-destructive, percussion-based approach to detect subsurface voids in CFST structures. In our approach, we exploit the contrasting sound produced by the percussion of surfaces with and without subsurface voids. Percussive acoustic signals in non-void and void zones are recorded. By analyzing the power spectrum density (PSD) of the percussion sound, nine features can be extracted. Two specimens (A and B) were fabricated in our experiment. The features of the sound signal extracted from the specimen A are used as the database for training and testing the support vector machine (SVM) model. Then, the trained SVM is applied to specimen B to determine whether or not a void between the concrete core and the outer steel tube exists. The experimental results show that the prediction precision is 94.17%. Therefore, the percussion-based approach is a simple, efficient, and accurate method to detect the void defects. (C) 2020 Published by Elsevier Ltd.
引用
收藏
页数:11
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