Comparative Study of Dynamic Optimization Techniques and Genetic Algorithms Approaches for Weight Reduction in Steel Truss Structures

Abstract

In order to promote the appropriate consumption of steel, it is always important to examine and update the techniques of optimizing the steel structures. This thesis investigates two weight-reduction methods, Dynamic Optimization and Genetic Algorithm Optimization, on a steel truss structure under dynamic loads. The wind and seismic loads, with particular emphasis on the stability of the structure against these loads, have been studied in the previous work. This thesis compares the dynamic optimization method and genetic algorithm to find out which method is more effective in reducing the weight of fabricated steel trusses without compromising their functionality. To evaluate these objectives, this study applies the Dynamic Optimization technique by the Newmark-beta method with Newton Raphson iterations and the Genetic Algorithm Optimization technique to obtain the optimal structural configuration of a steel truss. Ultimately, numerical simulations by Finite Element Analysis (FEA) and various optimizing algorithms, which mainly incorporate MATLAB and ANSYS applications, are employed. The efficacy of Dynamic Optimization and Genetic Algorithm Optimization techniques in enhancing the structural performance and reducing the weight of steel truss structure is examined. The findings indicate that both optimization techniques are appropriate and, to some extent, helpful for weight reduction, and each strategy offers unique advantages. Larger structures are better suited for the genetic algorithm, on the other hand time-dependent loads are successfully handled by the dynamic optimization strategy.