Damage diagnosis of a building structure using support vector machine and modal frequency patterns

Akira Mita; Hiromi Hagiwara

doi:10.1117/12.482705

18 August 2003 Damage diagnosis of a building structure using support vector machine and modal frequency patterns

Akira Mita, Hiromi Hagiwara

Proceedings Volume 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures; (2003) https://doi.org/10.1117/12.482705
Event: Smart Structures and Materials, 2003, San Diego, California, United States

Abstract

A method using the support vector machine (SVM) to detect local damages in a building structure with the limited number of sensors is proposed. The SVM is a powerful pattern recognition tool applicable to complicated classification problems. The method is verified to have capability to identify not only the location of damage but also the magnitude of damage with satisfactory accuracy. In our proposed method, feature vectors derived from the modal frequency patterns are used after proper normalization. The feature vectors contain the information on the location and magnitude of damages. As the method does not require modal shapes, typically only two vibration sensors are enough for detecting input and output signals to obtain the modal frequencies. The support vector machines trained for single damage is also effective for detecting damage in multiple stories.

Citation Download Citation

Akira Mita and Hiromi Hagiwara "Damage diagnosis of a building structure using support vector machine and modal frequency patterns", Proc. SPIE 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures, (18 August 2003); https://doi.org/10.1117/12.482705

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