Abstract
The selection of a given method for the seismic vulnerability assessment of buildings is mostly dependent on the scale of the analysis. Results obtained in large-scale studies are usually less accurate than the ones obtained in small-scale studies. In this paper a study about the feasibility of using Artificial Neural Networks (ANNs) to carry out fast and accurate large-scale seismic vulnerability studies has been presented. In the proposed approach, an ANN was used to obtain a simplified capacity curve of a building typology, in order to use the N2 method to assess the structural seismic behaviour, as presented in the Annex B of the Eurocode 8. Aiming to study the accuracy of the proposed approach, two ANNs with equal architectures were trained with a different number of vectors, trying to evaluate the ANN capacity to achieve good results in domains of the problem which are not well represented by the training vectors. The case study presented in this work allowed the conclusion that the ANN precision is very dependent on the amount of data used to train the ANN and demonstrated that it is possible to use ANN to obtain simplified capacity curves for seismic assessment purposes with high precision.
Highlights
There are many methods used for the seismic vulnerability assessment of buildings
The results obtained in this work indicate that the combination of the N2 method with a simplified capacity curve obtained from an Artificial Neural Networks (ANNs) allows fast structural seismic performance evaluation on large-scale studies, with an accuracy level of the results much closer to the obtained in a study carried out at a building scale than the ones obtained with a mean typological capacity curve
The blue dots are the results obtained from the 125 nonlinear structural analysis (TS1) and the red and green lines are the corresponding results obtained with ANN1 and ANN2, respectively
Summary
There are many methods used for the seismic vulnerability assessment of buildings. These methods can be classified as empirical, analytical/mechanical or hybrid methods [1] and present different levels of accuracy, being the analytical/mechanical methods the most accurate and the empirical the less ones. A study about the feasibility of using Multi-layer Feed-Forward Neural Network (MFFNN) to obtain a simplified capacity curve of a given building typology is presented, trying to reduce the results dispersion normally associated to the use of a mean typological capacity curve. The results obtained in this work indicate that the combination of the N2 method with a simplified capacity curve obtained from an ANN allows fast structural seismic performance evaluation on large-scale studies, with an accuracy level of the results much closer to the obtained in a study carried out at a building scale (by minimizing the dispersion values) than the ones obtained with a mean typological capacity curve. The obtained results open the path for the development of more complex ANN architectures and considering much more input variables
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