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Indexed by:期刊论文

Date of Publication:2021-02-02

Journal:CONSTRUCTION AND BUILDING MATERIALS

Volume:262

ISSN No.:0950-0618

Key Words:Concrete-filled steel tubular structures (CFST); Void detection; Support vector machine (SVM); Machine learning; Percussion

Abstract: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.