Neural-Network Based Prediction of Inelastic Response Spectra - Sofiane Hammal; Nouredine Bourahla; Nasser Laouami (Salehan Institute of Higher Education, 2020)
The prediction of the nonlinear seismic demand for a given hazard level is still a challenging task for seismic risk assessment. This paper presents a Ground Motion Prediction Model (GMPE) for efficient estimation of the inelastic response spectra of 5% damped Single Degree of Freedom (SDOF) systems, with Elastic-Perfectly-Plastic hysteretic behavior in terms of seismological parameters and structural properties. The model was developed using an Artificial Neural Network (ANN) with Back-Propagation (BP) learning algorithm, by means of 200 records collected from KiK-Net database. The proposed model outputs an inelastic response spectra expressed by a 21 values of displacement amplitudes for an input set composed of three earthquake parameters; moment magnitude, depth and source-to-site distance; one site parameter, the shear wave velocity; and one structural parameter, the strength-reduction factor. The performance of the neural network model shows a good agreement between the predicted and computed values of the inelastic response spectra. As revealed by a sensitivity analysis, the seismological parameters have almost the same influence on the inelastic response spectra, only the depth which shows a reduced impact. The advantage of the proposed model is that it does not require an auxiliary elastic GMPE, which makes it easy to be implemented in Probabilistic Seismic Hazard Analysis (PSHA) methodology to generate probabilistic hazard for the inelastic response.