4.8 Article

A Multiobjective Evolutionary Framework for Formulation of Nonlinear Structural Systems

期刊

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 18, 期 9, 页码 5795-5803

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3126702

关键词

Mathematical models; Earthquakes; Complexity theory; Computational modeling; Informatics; Seismic measurements; Buildings; Evolutionary computation (EC); feature selection; formulation; genetic programming (GP); multiobjective; self-centering concentrically braced frame (SC-CBF)

资金

  1. Australian Government through the Australian Research Council [DE210101808]
  2. Australian Research Council [DE210101808] Funding Source: Australian Research Council

向作者/读者索取更多资源

In this article, an evolutionary framework for seismic response formulation of self-centering concentrically braced frame (SC-CBF) systems is proposed. Multiple SC-CBF systems were designed, and an evolutionary feature selection strategy and a hybrid multiobjective genetic programming and regression analysis were used to find the best model. The results show that the evolutionary procedure is highly effective for designing the SC-CBF system using a simple and accurate model for such a complex system.
In this article, an evolutionary framework is proposed for seismic response formulation of self-centering concentrically braced frame (SC-CBF) systems. A total of 75 different SC-CBF systems were designed, and their responses were recorded under 170 earthquake records. To select the most important earthquake intensity measures, an evolutionary feature selection strategy is introduced, which tries to find the highest correlation. For the formulation of the SC-CBF response, a hybrid multiobjective genetic programming and regression analysis is implemented, considering both model accuracy and model complexity as objectives. In the hybrid approach, regression tries to connect multiple genes. Non-dominated models are presented, and the best model is selected based on the practical approach proposed here. The best model is compared with four other genetic programming models. The results show that the evolutionary procedure is highly effective for designing the SC-CBF system using a simple and accurate model for such a complex system.

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