日本地震工学会論文集
Online ISSN : 1884-6246
ISSN-L : 1884-6246
論文
INTERNAL DAMAGE DETECTION FOR RUBBER BEARINGS USING MACHINE LEARNING METHODS
Kohei MORIKAWAYuma KAWASAKIYasutoshi NOMURAKazuyuki IZUNO
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2025 年 25 巻 8 号 p. 8_1-8_11

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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.

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© 2025 Japan Association for Earthquake Engineering
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