size and shape optimization of space trusses considering geometrical imperfection-sensitivity in buckling constraints

optimal design considering buckling of compressive members is an important subject in structural engineering. the strength of compressive members can be compensated by initial geometrical imperfection due to the manufacturing process; therefore, geometrical imperfection can affect the optimal design of structures. in this study, the metaheuristic teaching-learning-based-optimization (tlbo) algorithm is applied to study the geometrical imperfection-sensitivity of members’ buckling in the optimal design of space trusses. three benchmark trusses and a real-life bridge with continuous and discrete design variables are considered, and the results of optimization are compared for different degrees of imperfection, namely 0.001, 0.002, and 0.003. the design variables are the cross-sectional areas, and the objective is to minimize the total weight of the structures under the following constraints: tensile and compressive yielding stress, euler buckling stress considering imperfection, nodal displacement, and available cross-sectional areas. the results reveal that higher geometrical imperfection degrees significantly change the critical buckling load of compressive members, and consequently, increase the weight of the optimal design. this increase varies from 0.4 to 119% for different degrees of imperfection in the studied trusses.