Optimum Design of RC Footings with Genetic Algorithms According to ACI 318-19

Abstract

Engineers usually use trial-and-error approaches for dealing with design problems where they need to find the most economical design of a structural element in terms of its material cost while satisfying all the safety requirements imposed by the design codes. In this study, we employ a genetic algorithm (GA) with a dominance-based tournament selection technique for dealing with this design challenge. The methodology is applied in the design of reinforced concrete rectangular-shaped isolated footings in accordance with the American Concrete Institute ACI 318-19. First, the footing is encoded into a set of decision variables and an objective function is defined to compute the total cost based on the different construction materials. Then, the compliance of the design with the ACI 318-19 code is enforced by a constraint function that takes into consideration all the demand–capacity ratios for the different resistance requirements such as the allowable bearing pressure of the supporting soil, and the shear and flexural capacities of the footing, among others. Two numerical examples are presented where the results show a significant advantage in terms of material-cost and design-time reduction in comparison with the commonly used trial and error approach, proving the applicability of optimization algorithms (OAs) into the everyday design routine of the structural engineer.

Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions

“…There are some new prediction approaches [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 ], such as adaptive neuro-fuzzy inference system (ANFIS), deep learning (DL), marine predators algorithm (MPA), pure random orthogonal search (PROS), artificial neural networks (ANNs), genetic algorithm (GA), and particle swarm optimization (PSO) method, etc. These approaches can better reveal the strength of structures in a more unbiased way, which should be studied in the future.…”

Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps

“…Solorzano and Plevris (Solorzano and Plevris, 2020) used Genetic Algorithms to find the optimal design of concrete isolated footings (both pure axially loaded and with eccentricities) according to the ACI318-19 code regulations. They use the minimization of the material cost including both the concrete and the steel reinforcement as the objective function.…”

Mathematical Modeling for the Optimal Cost Design of Circular Isolated Footings with Eccentric Column

“…Fathipour et al [24] proposed a numerical study to obtain the lateral soil pressure on geosynthetic-reinforced retaining structures by finite element and second-order cone programming. Solorzano and Plevris [25] presented the optimal design of rectangular footings using genetic algorithms according to the ACI 318-19 standard of the American Concrete Institute. Waheed et al [26] developed and applied a parametric investigation for the optimal design of footings based on the Metaheuristic Practical Tool.…”