Fast Seismic Assessment of Built Urban Areas with the Accuracy of Mechanical Methods Using a Feedforward Neural Network

Capacity curves obtained from nonlinear static analyses are widely used to perform seismic assessments of structures as an alternative to dynamic analysis. This paper presents a novel 'en masse' method to assess the seismic vulnerability of urban areas swiftly and with the accuracy of mechanical methods. At the core of this methodology is the calculation of the capacity curves of low-rise reinforced concrete buildings using neural networks, where no modeling of the building is required. The curves are predicted with minimal error, needing only basic geometric and material parameters of the structures to be specified. As a first implementation, a typology of prismatic buildings is defined and a training set of more than 7000 structures generated. The capacity curves are calculated through push-over analysis using SAP2000. The results feature the prediction of 100-point curves in a single run of the network while maintaining a very low mean absolute error. This paper proposes a method that improves current seismic assessment tools by providing a fast and accurate calculation of the vulnerability of large sets of buildings in urban environments.

Automated summary

This paper presents a novel method using neural networks to calculate the capacity curves of low-rise reinforced concrete buildings, providing a fast and accurate assessment of the seismic vulnerability of urban areas.