Neural estimation of strong ground motion duration

Abstract

This paper presents and discusses the use of neural networks to determine strong ground motion duration. Accelerometric data recorded in the Mexican cities of Puebla and Oaxaca are used to develop a neural model that predicts this duration in terms of the magnitude, epicenter distance, focal depth, soil characterization and azimuth. According to the above the neural model considers the effect of the seismogenic zone and the contribution of soil type to the duration of strong ground motion. The final scheme permits a direct estimation of the duration since it requires easy-to-obtain variables and does not have restrictive hypothesis. The results presented in this paper indicate that the soft computing alternative, via the neural model, is a reliable recording-based approach to explore and to quantify the effect of seismic and site conditions on duration estimation. An essential and significant aspect of this new model is that, while being extremely simple, it also provides estimates of strong ground motions duration with remarkable accuracy. Additional but important side benefits arising from the model’s simplicity are the natural separation of source, path, and site effects and the accompanying computational efficiency.