Abstract
The paper presents a simple yet efficient optimal design method based evolutionary structural optimization (ESO) to determine the minimum member sizes complying with an AISC-LRFD (an acronym for American Institute of Steel Construction – Load and Resistance Factor Design) specification. The influences of nonlinear (second-order approximation) geometry are realistically incorporated using an AISC 2010 direct analysis method. The optimization minimizes the total weight of structures subjected to the challenges associated with the simultaneous presence of nonlinear design formulations and discrete design variables realistically considering the availability of standard steel sections. This leads to a non-convex and/or non-smooth optimization program. The ESO algorithm overcomes the problem by performing iterative elimination procedures of ineffective sections, where the feasible design domain progressively converges to the minimum distribution of steel members satisfying limit state (ultimate strength and serviceability) criteria. The ESO design process is encoded within a visual basis application (VBA) framework having a direct interface with the commercial analysis and design software, called SAP2000. An illustrative example motivated by practical engineering applications has been successfully proceeded and hence highlighted robustness of the proposed approach in obtaining the optimal design of steel structures under applied forces.
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Chaiwongnoi, A., Van Thu, H., Tangaramvong, S., Van, C.N. (2021). An ESO Approach for Optimal Steel Structure Design Complying with AISC 2010 Specification. In: Huang, YP., Wang, WJ., Quoc, H.A., Giang, L.H., Hung, NL. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2020. Advances in Intelligent Systems and Computing, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-62324-1_36
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DOI: https://doi.org/10.1007/978-3-030-62324-1_36
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