A comparative study of two meta-heuristic algorithms in optimizing cost of reinforced concrete segmental lining

In this work, we tried to automatically optimize the cost of the concrete segmental lining used as a support system in the case study of Mashhad Urban Railway Line 2 located in NE Iran. Two meta-heuristic optimization methods including particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were presented. The penalty function was used for unfeasible solutions, and the segmental lining structure was defined by nine design variables: the geometrical parameters of the lining cross-section, the reinforced feature parameters, and the dowel feature parameters used among the joints to connect the segment pieces. Furthermore, the design constrains were implemented in accordance with the American Concrete Institute code (ACI318M-08) and guidelines of lining design proposed by the International Tunnel Association (ITA). The objective function consisted of the total cost of structure preparation and implementation. Consequently, the optimum design of the system was analyzed using the PSO and ICA algorithms. The results obtained showed that the objective function of the support system by the PSO and ICA algorithms reduced 12.6% and 14% per meter, respectively.

Keywords:

Meta-Heuristic Optimization , Segmental Lining , Particle Swarm Optimization , Imperialist Competitive Algorithm , Tunnel Boring machine