applied a counter-propagation NN to locate damage in beams and frames. KuZniar and Waszczyszyn [11] simulated the dynamic response for prefabricated building using ANN. Elkordy et al.[12] used a back-propagation neural network with modal shapes in the input layer to detect the simulated damage of structures. Muhammad [13] gives certain ANN applications in concrete structures. Pandey and Barai [14] detected damage in a bridge truss by applying ANN of multilayer perceptron architectures to numerically simulated data. Some studies such as [15]-[17] used artificial neural network for structural damage detection and system identification. In the present paper, the Chamoli earthquake ground acceleration at Barkot (NE) and Uttarkashi earthquake ground acceleration recorded at Barkot (NE and NW) have been considered based on the authors' previous study [18]. From their ground acceleration the responses are computed using the usual procedure. Then the ground acceleration and the corresponding response are trained using Artificial Neural Network (ANN) with and without damping. After training the network with one earthquake, the converged weight matrices are stored. In order to show the power of these converged (trained) networks other earthquakes are used as input to predict the direct response of the structure without using any mathematical analysis of the response prediction. Similarly, the various time periods of one earthquake and its corresponding maximum responses are trained. Then the converged weights are used to predict the maximum response directly to the corresponding time period. Various other results related to use of these trained networks are discussed for future / other earthquakes. 

## 2 Artificial Neural Network

Artificial neural systems are present day machines that have great potential to improve the quality of our life. Advances have been made in applying such systems for problems found difficult for traditional computation. A neural network is a parallel, distributed information processing structure consists of processing elements called neurons, which are interconnected and unidirectional signal 