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To estimate the serviceability and safety of high-rise buildings under various dynamic-loading conditions, a reliable analytical model is needed. In this study, an analytical-model calibration process is proposed to obtain a reliable analytical model for high-rise buildings. A high-rise residential complex building is selected as a test bed, and the natural frequency and mode shape are derived from vibration measurement. On the basis of the measurement, analytical-model calibration using artificial neural network is performed to update the initial analytical model. Through this process, the calibrated analytical model exhibits remarkable coherence with the measurement at the principal natural frequency and mode shape at an acceptable tolerance level.

System Identification and Calibration of an Analytical Model of High-Rise Residential Complex Buildings based on Artificial Neural Network