FRAGILITY ASSESSMENT OF RC BUILDINGS IN SOUTHERN SPAIN BASED ON NEURAL NETWORK PREDICTIONS

Resumen

The computational burden needed to perform a fragility analysis of structures can be excessive and beyond the capability of regular computing systems. In this work, a Neural Network (NN)implementation is presented to make fragility analyses attainable. Neural Networks allowfinding solutions to complex problems at a fraction of the computational time required byconventional analyses. The fragility assessment has been developed for low- and mid-rise 3Dbuildings located in southern Spain, a moderate earthquake prone area. Nonlinear staticanalyses are carried out to determine the capacity curves of reinforced concrete buildings,avoiding their specific modelling. The curves are predicted with minimal error, requiring onlybasic geometric and material parameters of the structures to be specified. Four levels ofperformance-based seismic design have been considered to assess the seismic performance.Fragility curves have been developed for the structural models with different types of structuralconfigurations and heights. Finally, it should be noted that fragility curves have not beenobtained to date for the reinforced concrete buildings of the area.