Discrete sizing design of steel truss bridges through teaching-learning-based and biogeography-based optimization algorithms involving dynamic constraints

In this paper, Teaching-Learning Based Optimization (TLBO) and Biogeography-Based Optimization (BBO) algorithms are presented to examine the optimum discrete sizing design of steel truss steel bridges for minimizing the structural weights. Both proposed nature-inspired metaheuristic optimization algorithms are encoded in MATLAB with integration of a structural analysis program (SAP2000) via open application programming interface (OAPI). At the end, optimal steel profiles are selected from available discrete section lists by satisfying the structural restrictions, such as stress and displacement, involved by American Institute of Steel Construction-Allowable Stress Design (AISC-ASD). Additional to these, optimum discrete sizing design process is performed for the cases with and without dynamic constraints, which are adopted from natural periods of the bridge structures with respect to the mode shapes. The algorithmic performance of the proposed algorithms outperforms on both planar and spatial steel truss bridges. To prove this obtained optimal solutions are compared with previously reported optimum designs attaining via different metaheuristics. The final optimum discrete sizing designs of the steel truss bridges reveal that the proposed TLBO and BBO algorithms can easily be applied to discrete nonlinear programming problems.