Generalized neural network based model for structural dynamic identification, analytical and experimental studies

The paper presents simulation and experimental studies of identification of civil engineering structures using neural networks. The identification of structural models by using measured data is an important issue in engineering. Although static function mapping may be achieved using neural networks without knowing the fundamental physics of the system, dynamic model identification is still a challenging topic in neural network applications. A generalized neural network-based technique for structural dynamic model identification is developed based on the dynamics of structure. During the simulation study, structural response records from a 10-storey San Jose apartment building subjected to three different earthquakes are adopted for the dynamic model identification. For the experimental study, a series of experiments were conducted in which a designed scaled model structure, mounted on a shake table, was tested. The neural network is trained and examined using the measured structural responses under different earthquake loading conditions. It is shown that the trained neural network is capable of providing sensible outputs when presented with input data that has never been used during its training