Seismic upgrading of RC structures through an optimization procedure based on Genetic Algorithm

Nowadays governments are encouraging the upgrading and the reuse (rather than the demolition) of older structures in order to reduce land use and environmental impact due to the construction of new buildings. The choice of the most suitable intervention for the seismic upgrading of existing structures could also be addressed combining member-level (e.g., FRP-confinement of single columns) and structural-level (e.g., insertion of steel bracing systems) techniques, although it may result in a complex technical challenge for engineers, since a huge number of combinations of technically feasible upgrading interventions are theoretically possible to accomplish the desired structural performance. In order to support the intervention choice, an “objective” approach could be implemented making use of recently-invented Artificial Intelligence (AI) procedures. Specifically, the application of Genetic Algorithms (GAs) is usually thought as a suitable optimization procedure in several civil engineering problems. By means of a Genetic Algorithm (GA), the design of upgrading interventions results to be based on one objective criterion related to the cost-effectiveness of the intervention, rather than on the highly subjective “engineering-judgement”. The present paper aims to highlight the capability of a Soft-Computing (SC) procedure in selecting the most cost-effective combination of the aforementioned member-level and structural-level interventions, among the technically consistent ones. To this aim a parametric study on a RC structure based on a similar GA procedure is reported, varying some “engineering parameters” related to the target seismic risk class of the upgraded structure. Relevant differences in the results are observed due to the variation of target of the analyses.