An Artificial Neural Network for Predicting the Near-fault Directivity-pulse Period

The velocity pulses produced by forward-directivity effects in the near-fault regions can have destructive effects on structures. Proper estimation of the duration of such velocity pulses is an essential step in the near-fault seismic hazard analysis and mitigating potential damage. In this study, the effects of different source, path, source-to-site geometry, and local site parameters on the duration of directivity pulse (T _p) are investigated based on the mutual information (MI) concept. A dataset of near-fault pulse-like ground motions from the NGA-West2 database including 135 observations from 17 strike-slip events and 14 non-strike-slip events is utilized for the purpose of this study. The selected ground motion variables are the magnitude, hypocentral distance, depth, D and V_S30 that are further applied in an artificial neural network (ANN) to predict T _p. The ANN estimates are verified by support vector regression (SVR) as one of the most efficient machine learning algorithms. High correlation between observations and predictions and low error functions reveals the good predictive ability of both ANN and SVR for estimating directivity pulse period. The predictions made by ANN and SVR are further compared with those provided by the empirical and physical models.