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Machine-learning-based prediction of structural defects using acceleration response waveforms from hammering test

Year: 2025

Type: article

Abstract: In this study, we employed machine learning to predict the presence or absence of defects in structures, using acceleration response waveforms from a hammering test. The loss function was defined by the binary cross-entropy error, and several numerical experiments were performed to predict defects by changing the acceleration response waveforms. These acceleration response waveforms were used as input data, enabling the supervised machine learning model to output the presence or absence of defects.

Source: JSIAM Letters

DOI: 10.14495/jsiaml.17.21

Authors: Takahiko Kurahashi, Cao Minh Quoc Nguyen, Akihiro Takemori, Keita Kambayashi, Fujio Ikeda, Yuki Murakami

Institutions: Nagaoka University of Technology, Nagaoka National College of Technology, National Institute of Technology

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Japan Society for the Promotion of Science