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Article Citation - WoS: 11Citation - Scopus: 10Multi-Stage Guided Stochastic Search for Optimization and Standardization of Free-Form Steel Double-Layer Grids(Elsevier Science inc, 2021) Azad, Saeid Kazemzadeh; Aminbakhsh, Saman; Shaban, Samer S. S.There has been a growing interest in the use of free-form structures with irregularly curved yet aesthetically pleasing configurations in the recent decades. Although design optimization of regular steel grids has been well addressed in the literature of structural optimization, still limited work has been devoted to optimum design of real-size free-form grid structures. On the one hand, a main obstacle when dealing with real-size free-form steel grids is the excessive computational effort associated with contemporary evolutionary optimization algorithms. On the other hand, it is generally perceived that the obtained final designs using conventional optimization algorithms may not necessarily be favored in practice if certain provisions are not stipulated by the algorithm to preclude an abundance of distinct steel section sizes in the final design. Hence, instead of offering a single optimum or near optimum design, it would be more desirable to provide the designer or decision maker with a Pareto front set of non-dominated design alternatives taking into account both the minimum weight as well as the assortment of available steel section sizes in the final design. Accordingly, in this paper, a computationally efficient multi-stage guided stochastic search algorithm is proposed for optimization and standardization of realsize free-form steel double-layer grids. A gradual design-oriented section elimination approach is followed where in the first optimization stage, a complete set of commercially available steel sections is introduced to the algorithm and in the succeeding stages, the size of section list is reduced by eliminating the redundant sizes. Two variants of the algorithm are employed to demonstrate the usefulness of the proposed technique in challenging test examples of free-form steel double-layer grids, and the obtained Pareto fronts are plotted to illustrate the trade-off between minimum weight and assortment of steel section sizes in the final design.Article Citation - WoS: 25Citation - Scopus: 32High-Dimensional Optimization of Large-Scale Steel Truss Structures Using Guided Stochastic Search(Elsevier Science inc, 2021) Azad, Saeid Kazemzadeh; Aminbakhsh, SamanDespite a plethora of truss optimization algorithms devised in the recent literature of structural optimization, still high-dimensional large-scale truss optimization problems have not been properly tackled basically due to the excessive computational effort required to handle the foregoing instances. In this study, application of a recently developed design-driven heuristic, namely guided stochastic search (GSS), is extended to a more challenging class of truss optimization problems having thousands of design variables. Two variants of the algorithm, namely GSSA and GSSB, have been employed for sizing optimization of four high-dimensional examples of steel trusses, i.e., a 2075-member single-layer onion dome, a 2688-member double-layer open dome, a 6000-member doublelayer scallop dome, and a 15048-member double-layer grid as per AISC-LRFD specification. The numerical results obtained indicate the efficiency of GSSA and GSSB in handling high-dimensional instances of large-scale steel trusses with up to 15048 discrete design variables.
