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The main purpose of the present work is to investigate the impact of soil-structure interaction on performance-based design optimization of steel moment resisting frame (MRF) structures. To this end, the seismic performance of optimally designed MRFs with rigid supports is compared with that of the optimal designs with a flexible base in the context of performance-based design. Two efficient metaheuristic algorithms, namely center of mass optimization and improved fireworks, are used to implement the optimization task. During the optimization process, nonlinear structural response-history analysis is carried out to evaluate the structural response. Two illustrative design examples of 6- and 12-story steel MRFs are presented, and it is observed that the performance-based design optimization considering soil-structure interaction decreases the structural weight and increases nonlinear structural response in comparison to rigid-based models. Therefore, in order to obtain more realistic optimal designs, soil-structure interaction should be included in the performance-based design optimization process of steel MRFs.

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The main objective of this paper is to optimize the size and layout of planar truss structures simultaneously. To deal with this challenging type of truss optimization problem, the center of mass optimization (CMO) metaheuristic algorithm is utilized, and an extensive parametric study is conducted to find the best setting of internal parameters of the algorithm. The CMO metaheuristic is based on the physical concept of the center of mass in space. The effectiveness of the CMO metaheuristic is demonstrated through the presentation of three benchmark truss layout optimization problems. The numerical results indicate that the CMO is competitive with other metaheuristics and, in some cases, outperforms them.
 

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The main objective of this study is to predict the maximum inter-story drift ratios of steel moment-resisting frame (MRF) structures at different seismic performance levels using feed-forward back-propagation (FFBP) neural network models. FFBP neural network models with varying numbers of hidden layer neurons (5, 10, 15, 20, and 50) were trained to predict the maximum inter-story drift ratios of 5- and 10-story steel MRF structures. The numerical simulations indicate that FFBP neural network models with ten hidden layer neurons better predict the inter-story drift ratios at seismic performance levels for both 5- and 10-story steel MRFs compared to other neural network models.