Showing 42 results for Genetic Algorithm
B. Ahmadi-Nedushan, A. M. Almaleeh,
Volume 14, Issue 4 (10-2024)
Abstract
This study uses an elitist Genetic Algorithm (GA) to optimize material costs in one-way reinforced concrete slabs, adhering to ACI 318-19. A sensitivity analysis demonstrated the critical role of elitism in GA performance. Without elitism, the GA consistently failed to reach the target objective, with success rates often nearing zero across various crossover fractions. Incorporating elitism dramatically increased success rates, highlighting the importance of preserving high-performing individuals. With an optimal configuration of 0.3 crossover fraction and 0.45 elite percentage, a 92% success rate was achieved, finding a cost of 24.91 in 46 of 50 runs for a simply supported slab. This optimized design, compared to designs based on ACI 318-99 and ACI 318-08, yielded material cost savings of between 5.8% to 8.6% for simply supported, one-end continuous, both-ends continuous, and cantilevered slabs. The influence of slab dimensions on cost was evaluated across 64 scenarios, varying slab lengths from 5 to 20 feet for each support condition. Resulting cost versus slab length diagrams illustrate the economic benefits of GA optimization.
M. Fahimi Farzam, M. Salehi,
Volume 15, Issue 4 (11-2025)
Abstract
Reducing the degrees of freedom of building models significantly reduces computational costs in time-consuming structural engineering problems, such as dynamic analysis, nonlinear analysis, or the optimal design of structural systems. In this study, the Finite Element (FE) model of a 20-story benchmark steel building with numerous degrees of freedom (DoF) is simplified to a 20-degree-of-freedom linear shear-type building. First, a preliminary linear shear-type model was derived by estimating the story stiffness so that the fundamental frequency matches that of the FE model. Then, an optimization problem is formulated and solved using a Genetic Algorithm (GA) combined with a weighted-sum method to achieve greater accuracy at higher frequencies in the preliminary model. Two objective functions were established and assessed for the optimization problem: one is the difference in frequencies between the FE model and the preliminary model with equal weighting, and the other is the first objective function improved with the modal participation percent weighting. The stiffness of each story in the preliminary model is selected as the design variable in both optimization problems. Finally, these optimized models are evaluated against the FE model using frequencies and dynamic time-history responses. The model derived from the weighted objective function demonstrates acceptable accuracy compared to its FE model in frequency and time-history analysis. It can be used for dynamic analysis and other structural and earthquake engineering purposes.
