نوع مقاله : پژوهشی
1 دانشکده مهندسی عمران- دانشگاه علم و صنعت ایران
2 دانشکده مهندسی عمران - دانشگاه علم و صنعت ایران
عنوان مقاله [English]
As a region with a high level of seismicity, Iran has been exposed to a prodigious number of destructive seismic events during its history, causing considerable loss of life and financial damage. Improvements in engineering knowledge about the nature of seismicity and its concomitant effects on structures have been behind the development of numerous analytical methods regarding both structural behavior and related damage induced by seismic events. Nowadays, concomitant with introducing the concept of performance based design methods and increasing the importance of using structural inelastic displacement parameters instead of forces, the need to apply non-linear rather than linear analysis has grown. In this regard, the non-linear static method has recently become increasingly popular for practical use. This method is simpler than dynamic methods and needs less computational time. However, this method is comprised of limits and problems. Due to the mentioned necessity, the substantial growth in application of performance based design methods in Iran requires equations which provide estimates for a structures inelastic response. Thus, the main objective of this paper is to develop a method to represent an estimate of the inelastic response for structures of single degree of freedom reductions. To this end, Artificial Neural Networks have been used, and responses for single degree of freedom structures with different characteristics and three types of reduction behavior have been represented under different kinds of seismic record. From the resulted outputs, 70% to 80% of data were used for training the neural network model and the remaining 20% to 30% were used for validation. Due to the relative high scattering of available records, and in order to minimize the error in estimating inelastic response, the outputs used in the training stage of developing the neural networks have been classified based on prevalent periods of seismic records. Finally, using the trained neural networks, software has been developed capable of predicting the response of single degree of freedom reduction structures under different seismic records.