Seyed S. Mousaviamjad

Work place: Department of Civil Engineering, Yazd University, Yazd, Iran



Research Interests: Computational Science and Engineering, Computational Engineering, Artificial Intelligence, Engineering


Seyed S. Mousaviamjad was born in 1986. He received his BSc degree in Civil Engineering from Yasouj University, Yasouj, Iran in 2009, and received then his MSc degree in Structural Engineering from University of Sistan and Baluchestan, Zahedan, Iran in 2012. He is currently studying as a PhD student of Structural Engineering at University of Yazd, Yazd, Iran. His research interests include Structural Engineering, Finite Element Methods, Graph Theory and Artificial Intelligence.

Author Articles
Prediction of Performance Point of Semi-Rigid Steel Frames Using Artificial Neural Networks

By Zahra Bahmani Mohammad R. Ghasemi Seyed S. Mousaviamjad Sadjad Gharehbaghi

DOI:, Pub. Date: 8 Oct. 2019

One of the main steps in the performance based seismic analysis and design of structures is determination of performance point where the nonlinear static analysis approach is used. The aim of this paper is to predict the performance point of semi-rigid steel frames using Artificial Neural Networks. As such, to generate data required for the prediction, several semi-rigid steel frames were modeled and their performance point was determined then. Ten input variables including number of bays, number of stories, bays width, moment of inertia of beams, cross sectional area of columns, cross sectional area of braces, rigidity degree of connections and soft story (existence or nonexistence) were considered in the prediction. In addition, the actual results were obtained at the presence of different earthquake intensity levels and soil types. Back Propagation with eleven different algorithms and Radial Basis Function Artificial Neural Networks were used in the prediction. The prediction process was carried out in two steps. In the first step, all samples were used for the prediction and the performance metrics were computed. In the second step, three of the best networks were selected, and the optimum number of samples was found considering a very slight reduction in the accuracy of the networks used. Finally, it was shown that, despite using rather limited number of samples, the generated Artificial Neural Networks accurately predict the performance point of semi-rigid steel frames.

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