Earthquake resistant design of framed reinforced concrete building using artificial intelligence model

Design of efficient earthquake (EQ) resistant building is challenging task to earthquake engineers. The available methods for earthquake resistant design are complex and computationally expensive. Most accurate method for analysis is Time history analysis of the intended structure using real time ground motion data. This method is also difficult in practical cases as no realistic data for ground motion and sufficient computational facility are unavailable. Presently the application of Artificial Intelligence (AI) model in solving complex real type problem like earthquake analysis and design of structure. Available literature indicates a positive growing trend for EQ resistant design using AI. The application of AI model can reduce time and effort for making EQ resistant design efficiently. In this paper, some building with the validation for different type of EQ are considered and data generation has been done using ETABS software and EQ ground motion (EQGM) are considered in development of AI model for EQ resistant design, some important parameter of real EQGM such as Peak ground acceleration (PGA), Peak ground velocity (PGV), Peak ground displacement (PGD) and time duration as well as building parameter such as maximum displacement, base shear and story drift are considered. The developed feed forward back propagation ANN model could successfully predict design parameters of a typical target structures within a reasonable less time of computation. The save in computation time and less effort for this type of meta-heuristic approach may have huge research interest and application in EQ resistant design of structure.