Development of an intelligent structural testing system utilizing neural networks

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The on-line or pseudo dynamic test has been considered as a powerful tool for the earthquake simulation of building. The method computes the displacements of the structural system based on the dynamic analysis and drives the actuators with that response to measure the restoring force directly from the test specimen. This criteria is perfectly applied to middle or high rise buildings with concrete stiff slabs or shear wall panels. In such a case the masses of the system are concentrated at the floor levels and the displacement of the elements are considered as a function of the mass associated lateral displacement. But in the case of steel frames composed of flexible beam-columns unbalanced forces appear in the non mass assosiated degree of freedom, and the representation of displacements as function of the lateral ones is not valid. In the present report a general scheme for on-line hybrid substructuring simulation is presented, to achieve the deformed position of testing steel beam-column. This scheme uses a predictor of the incremental forces on the specimen. Four predictors are presented, the elastic beam element predictor, the two component bilinear beam element predictor, the artificial neural network predictor, and the multi-spring inelastic beam-column element predictor. A brief description of the applicability of the scheme in the hybrid analysis is presented. Also the transformations required to reach the general deformed configuration are shown. Three series tests are presented to evaluate the different predictors and the applicability of the scheme, tests under controlled rotation and varying or constant axial load are described. From the report is conclude : the neural netwark predictor is applied successfully in the removal scheme of the unbalance moment, which is reduced to a negligible level.