Mohammad R. Ghasemi

Work place: Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran



Research Interests: Neural Networks, Data Structures and Algorithms, Combinatorial Optimization, Detection Theory


Mohammad R. Ghasemi received his BSc degree from The City University of London, London, UK in 1989. He completed then his MSc and PhD degrees in Structural Engineering in 1992 and 1996 from University of Wales-College of Cardiff and Swansea, respectively. He currently serves as a Full Professor at the Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran. His research interests include Structural Optimization, Reliability Analysis, Damage Detection and Neural Networks. He is the author or co-author of more than 150 journal papers and some papers published in national and international conferences.  

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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