Wind-resistant structural optimization of irregular tall building using CFD and improved genetic algorithm for sustainable and cost-effective design

Abstract

Tall buildings with irregular shapes and considerable heights are gaining popularity in creating the vertical cities around the world. They also considered one of the major energy consumers with little regards to sustainability. Tall building is wind-sensitive structure and shape plays major role in determining wind loads, which usually govern the design of its lateral resisting system. Thus, evaluating wind loads properly and designing an optimal lateral system accordingly are the main challenges attributed to the design process of irregular tall building. This paper presents a computational procedure for the optimal design of wind-resistant irregular tall building to minimize the total weight of structure within design requirements in single digital environment. That is achieved firstly by creating a digital system of computational fluid dynamic (CFD) analysis that is coupled with pressure-load translation (PLT) algorithm to evaluate the wind motions on irregular tall buildings and generate the design wind loads accordingly. Genetic Algorithm (GA) with enhance design constrains function of lateral displacements, inter-story drifts and top acceleration is then developed to perform structural optimization. A numerical example using 70-story twisting reinforced concrete building is implemented to verify the feasibility of the developed computational procedures. Steady and incompressible flow applied at (0°) angle of attack was implemented in the CFD model to simulate the wind flow on the studied building. Genetic algorithm with improved design constraints of static and dynamic design requirements was developed to optimize the structure effectively and efficiently. The numerical example demonstrates its effectiveness by achieving 35.71% reduction of concrete volume from the original lateral structural system design. This is also translated into a sustainability value by lessening the embedded carbon dioxide by 4,400 tons.

Applying modified coot optimization algorithm with artificial neural network meta-model for building energy performance optimization: A case study

“…The evaluation of multi-variable problems by automated computer-based technologies has enabled investigators to identify optimum multiple-criteria solutions in vector space with n dimensions [ 9 ]. Then, genetic algorithms (GA) extensively have been employed, which enable obtaining further optimized and effective solutions [ 10 ]. However, genetic algorithms have the main constraint [ 11 ].…”