Many Structural Health Monitoring (SHM) methods have been proposed for the purposes of reducing maintenance costs and/or assuring performance of civil structures. The objective of this research is to propose a damage detection system that can obtain the detailed damage information by use of the minimum number of sensors. The proposed system minimizes the possibility of incorrect judgments. Modal frequencies of a structure are used for pattern recognition in the proposed method. Changes in multiple natural frequencies can be correlated to the spatial information of the location of damaged stories. Typically only two vibration sensors, one on the roof and the other on the ground, detecting a single input and a single output for the structure are needed to determine modal frequencies. Out of many pattern recognition tools, we propose to use the Support Vector Machine (SVM). This technique has been found effective. Our previous studies demonstrated that the proposed damage detection method worked well for simple models such as shear structures and bending structures. However, real buildings have various boundary conditions at their supports. In this study, the SVM technique was applied to damage detection of structures with various boundary conditions. The feature vectors for SVMs are generated based on the model of a structure. Then locations of structural damage are detected by inputting the measured structural vibration data into the SVMs. From simulation, it was found that the influence of the change in boundary conditions on the lower modes is larger. We performed experimental studies on damage detection of power distribution poles that had overhead wires. We proposed a method for determining the boundary conditions of the poles and verified this method based on measured vibration data. We demonstrated the effectiveness of the proposed method in detecting damage in the poles.
A damage detection method utilizing Support Vector Machine (SVM) for bending structures is proposed. The SVM was recently proposed as a new technique for pattern recognition. The SVM is a powerful pattern recognition tool applicable to complicated classification problems and is effectively utilized in the method. Based on the modal frequency changes, the damage location and its severity are defined by the SVM. In our previous studies, it was shown that our proposed method worked very well for structures modeled by shear frames. However, this modeling is only appropriate for low-rise building structures and is not appropriate for tall buildings. Therefore, it is our purpose here to extend the method to bending frames that are appropriate models for tall buildings. In the analytical evaluation, we constructed the finite element models to represent bending structures. Then, we conducted a series of experiments for verification. We could show that the damage detection method using SVM was also possible and effective for bending structures.