GA-BPNN Prediction Model of Broadband Ground Motion Parameters in Tianjin Area Driven by Synthetic Database Based on Hybrid Simulated Method.

This paper focuses on building a ground motion model (GMM) using artificial neural network technique driven by a synthetically generated database in a data poor region. A hybrid simulated method considering nonlinear site response is used to generate potential ground motions in Tianjin area. The synthetic database containing a total of 9267 ground motion records from 93 simulated earthquake events are utilized to develop the model. A back propagation neural network developed by genetic algorithm (GA-BPNN) is used to obtain the optimal weights and biases that most fit the target dataset without overfitting. The structure of GA-BPNN model consists of 322 unknowns including weights and biases that connect input and output parameters. The model is developed to predict PGA and 5% damped spectral acceleration (periods from 0.01 to 10 s). The input parameters consist of moment magnitude (Mw), strike (φ), dip (δ), rake angle (λ), focal depth (h), epicenter distance (R) and azimuth (θ). The model performance is observed to be within the appropriate error limits. It is found that the model has the ability to capture the parametric variation relationship. Additionally, the synthetically based model is compared with empirically based GMM relations derived from NGA-West2, finding it has similar performance and behavior to leading GMMs. The model application in Tianjin area demonstrates that this paper provides a scheme to utilize synthetic database and build GMMs in data poor regions. [ABSTRACT FROM AUTHOR]