DAMAGE DETECTION OF TRUSS STRUCTURES BY APPLYING MACHINE LEARNING ALGORITHMS

Abstract

Infrastructures including bridges constructed in the period of high economic growth are getting older. For the damage detection of truss structures, this study assumes to utilize vibration signals obtained from sensors installed into the bridges. By preparing damaged and non-damaged bridge structures, large quantities of response data are generated. AR (Auto-Regressive) model is then applied to the time signals to extract the structure's soundness characteristics. Here, AR coefficients are values in which damaged structural characteristics are reflected. Then, the machine learning technique is applied to the AR coefficients to classify the structures into damaged and non-damaged ones. Results showed that the machine learning method successfully detected the damage of truss members. This kind of SHM (Structural Health Monitoring) technology is expected to contribute to early damage detection and preventive maintenance of bridges leading to increase the accuracy of the damage detection of truss structures with low costs and fewer efforts for maintenance.