Treatment and Assessment of Nonlinear Seismic Data by a Genetic Algorithm Based Neural Network Model

Actual seismic records usually involve a very high nonlinear data. set, which may require a tedious work to access by conventional statistical and vibration analysis. Alternatively, the present study develops an improved artificial neural network (ANN) model for evaluating the current seismic zone divisions in Taiwan's standard using the global search capability of genetic algorithm (GA). This model (GA+ANN) predicts the key factor of peak ground acceleration (PGA) using as inputs the recorded values of actual earthquake parameters including magnitude, epicenter distance, and focal depth. Results are presented to show that this model exhibits an improved generalization capability, with acceptably high coefficient of correlation and low root mean square error between estimations I and records. In addition, four locations out of twenty-four subdivision zones in total are identified by the model as having a potential for experiencing a higher horizontal PGA than that of the design value in the standard. Furthermore, the equation PGA = 0.44 1 exp(-0.020D(f)), developed by curve fitting, is presented for approximately describing the relationship between horizontal PGA and focal distance (D-f). The method employed provides a new approach to treat this type of nonlinear problem, and the information obtained provides a good basis for further research in this region.