Neural Network Approach for Parametric Identification of a Building from Seismic Response Data

S.L. Hung, C.S. Huang, and C.M. Wen (Taiwan)


System identification, neural network, seismic responses


This work presents a novel procedure for identifying the dynamic characteristics of a building, using a back propagation neural network approach. The dynamic characteristics are directly evaluated from the weighting matrices of the neural network trained by observed acceleration responses and input base excitations. It is the first study that used neural network approach for parametric identification of a structure. The feasibility of the approach is demonstrated through processing the dynamic responses of a five-story steel frame, subjected to different strengths of the Kobe earthquake, in shaking table tests. Besides the system identification purpose, the proposed approach can be further applied to system identification-based damage detection or health monitoring of structures.

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