INTERNAL DAMAGE DETECTION FOR RUBBER BEARINGS USING MACHINE LEARNING METHODS

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.