Multi-objective design optimisation of steel framed structures using three different methods

The optimality objectives are the structure weight and embodied energy as well as calculating the cost and embodied carbon of the resulting optimum options. Three optimality algorithms developed in MATLAB, namely, genetic algorithms (GA), particle swarm optimisation (PSO) and harmony search algorithm (HSA), were used for structural optimisation to compare the effectiveness of the algorithms. Two life-cycle stages were considered, production and construction stages, which include three boundaries: materials, transportation and erection. In the formulation of the optimum design problem, 107 universal steel beams (UKB) and 64 columns (UKC) sections were considered for the discrete design variables. The imposed behavioural constraints in the optimum design process were set according to the provision of Eurocode 3 (EC3). The study aims to find the optimum solution of 2D steel frames whilst considering weight and embodied energy, investigate the performance of the analysis integrated with MATLAB and provide three examples to which all these are applied to.

Undoubtedly, in structural engineering, the best design of any structure aims at the most economical and environmental option, without impairing the functional and its structural integrity. In the paper, multi-objective stochastic search methods are proposed for optimum design of three two-dimensional multi-story frames.

Results showed that the optimised designs obtained by HSA are better than those found by the GA and PSO with an average difference of 16% from GA and PSO, where this difference increases at larger frame structures. It was, therefore, concluded that the integration of the analysis, design and optimisation methods employed in MATLAB can be effective in obtaining prompt optimum results during the decision-making stage.

There may be some possible limitations in the study. Due to the time constraints, only three meta-heuristic approaches were investigated, where more methods should be investigated to fully understand their effectiveness in multi-objective problems.

Investigating the performance of three optimisation methods in multi-objective problems developed in MATLAB. More importantly, developing optimisation models for evaluation of embodied energy, embodied carbon and cost for steel structures to assist designers, during the initial stages, to evaluate design decisions against their energy consumption and carbon impacts.