Technical Papers
Apr 29, 2024

Multiobjective Structural Layout Optimization of Tall Buildings Subjected to Dynamic Wind Loads

Publication: Journal of Structural Engineering
Volume 150, Issue 7

Abstract

The tall building design process typically goes through a time-consuming iterative procedure to ensure the cost efficiency of the proposed structural system, especially in the conceptual design stage where the layout is designed. Despite that, this procedure does not guarantee to yield an optimal layout. Consequently, an automated layout optimization procedure will result in a more economical and sustainable design. This paper presents a novel multiobjective lateral load resisting system (LLRS) (i.e., shear walls) layout optimization framework provided for dynamically sensitive tall buildings subjected to a wind load time history. The developed framework relies on an artificial neural network (ANN) surrogate model for constraints and objective function evaluation to reduce the computational time of the optimization process. The adopted surrogate model is built based on an automated finite element models-generated database using MATLAB code via the Open Application Program Interface of the ETABS software. The ANN surrogate model proved its efficiency in capturing complex variations in the structural response with a correlation coefficient that ranges between 90% and 98%. A nongradient optimization algorithm (NSGA-II) is adopted to identify the optimal shear wall layout to resist the applied dynamic wind load. In order to reduce the number of optimal layout solutions on the Pareto front, a pruning algorithm is used to limit the optimal solutions to 24 layouts. This will enable designers to use the direct selection method to choose an appropriate layout that fits the project’s objectives. Also, a case study building is presented where the optimized results are analyzed and discussed in the numerical example to verify the effectiveness of the proposed optimization framework.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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Go to Journal of Structural Engineering
Journal of Structural Engineering
Volume 150Issue 7July 2024

History

Received: Jan 9, 2023
Accepted: Jan 8, 2024
Published online: Apr 29, 2024
Published in print: Jul 1, 2024
Discussion open until: Sep 29, 2024

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Ph.D. Student, Dept. of Civil Engineering, Lakehead Univ., 955 Oliver Rd., Thunder Bay, ON, Canada P7B 5E1. ORCID: https://orcid.org/0000-0001-6866-7061. Email: [email protected]
Tristen Brown [email protected]
Master’s Student, Dept. of Civil Engineering, Lakehead Univ., 955 Oliver Rd., Thunder Bay, ON, Canada P7B 5E1. Email: [email protected]
Assistant Professor, Dept. of Civil Engineering, Lakehead Univ., 955 Oliver Rd., Thunder Bay, ON, Canada P7B 5E1 (corresponding author). ORCID: https://orcid.org/0000-0003-3530-2511. Email: [email protected]

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