Damage Detection of Seismically Excited Buildings Based on Prediction Errors

Abstract

Buildings may suffer serious damage when subjected to extreme loadings such as strong winds and earthquakes. In seismic events, the error time histories between measured and estimated responses should contain the information of the structural deterioration, i.e., the locations, levels, and time of occurrences. Therefore, this study presents a new damage detection method based on prediction errors using a bank of Kalman estimators. A representative model of a building was derived from a frequency-domain multiinput, multioutput system identification method under ambient vibration prior to earthquakes. This model was then converted into a bank of estimators that calculate estimation errors. Damage was interpreted by statistical indices from these errors and allowed determining the occurrence, levels, and locations of damage. A numerical example is presented to demonstrate the proposed damage detection method as well as to exhibit the damage detection performance. A series of experimental tests were carried out with this damage detection method implemented in various scenarios. The experimental verification shows that this proposed method is quite effective for seismic damage detection.