Work

Internal Damage Detection for Rubber Bearings Using Machine Learning Methods

Year: 2025

Type: article

Abstract: Rubber bearings are now commonly used to prevent seismic damage to bridges; however, they can be destroyed by a major earthquake. Even if the bearing is not completely destroyed, it may sustain internal damage. Such internal damage is difficult to detect because they have a rubber coating. By conducting a numerical analysis, this study verified the propagation of elastic waves in a laminated rubber bearing, and used the data to detect damage using a machine learning anomaly detection technique. The results showed that damage inside a rubber bearing can be detected using the one-class support vector machine anomaly detection method.

Source: Journal of Japan Association for Earthquake Engineering

DOI: 10.5610/jaee.25.8_1

Authors: Kohei MORIKAWA, Yuma KAWASAKI, Yasutoshi NOMURA, Kazuyuki IZUNO

Institution: Ritsumeikan University

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