Machine learning-based improvement of crack localization in concrete structures

Abstract

Acoustic emission (AE) method is useful for damage detection in structures. However, processing the massive amount of AE data with traditional analysis methods is time-consuming. Therefore, machine learning (ML) integration provides a more rational and efficient solution by accelerating the data analysis and improving the damage detection accuracy. This study aims to improve source localization, which provides to identify cracking patterns by signal-based analysis, by training the ML model with the AE dataset for accurate damage diagnosis of concrete structures. Analyzes were conducted on the ML model using AE data collected on a concrete member under laboratory conditions. In this way, the ML model that learned signals with multi-variable wave characteristics and parameters was integrated into the source localization algorithm and thus, AE source location results were enhanced. Furthermore, due to its ability to identify damaged areas, this approach is essential for structural safety by providing early damage detection and accurate localization of cracks. This integration of the machine learning model with AE data will be considered as an advanced tool for the effective monitoring of damages in structures.