Estimation of spectral acceleration based on neural networks

This study presents an effective method based on artificial intelligence to predict spectral acceleration from the data of the ‘Next generation attenuation' project. The proposed method uses the learning abilities of artificial neural networks to expand the knowledge of the mapping from earthquake parameters to spectral accelerations, which results in spectral accelerations for special frequencies. In this paper, the Levenberg–Marquardt algorithm is applied for training neural networks. For each type of faults, considered earthquake parameters included in the input of the neural network are moment magnitude, distance from the recording site to epicentre, hypocentre depth and average shear-wave velocity in the top 30 m. To test the adequacy of the trained neural networks, they were examined through their training set and new data. The obtained results demonstrate that neural networks can be effectively and reliably employed in the estimation of spectral acceleration.