Advances in Engineering Software
Analysis of a deformed three-dimensional culvert structure using neural networks
Advances in Engineering Software
Advances in Engineering Software
Simulating the seismic response of embankments via artificial neural networks
Advances in Engineering Software
Engineering Applications of Artificial Intelligence
A hybrid computational approach to derive new ground-motion prediction equations
Engineering Applications of Artificial Intelligence
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Earthquake engineering problems in parallel neuro environment
HiPC'04 Proceedings of the 11th international conference on High Performance Computing
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Peak ground acceleration is a very important factor that must be considered in construction site for examining the potential damage resulting from earthquake. The actual records by seismometer at stations related to the site may be taken as a basis, but a reliable estimating method may be useful for providing more detailed information of the strong motion characteristics. Therefore, the purpose of this study was by using back-propagation neural networks to develop a model for estimating peak ground acceleration at two main line sections of Kaohsiung Mass Rapid Transit in Taiwan. Additionally, the microtremor measurements with Nakamura transformation technique were taken to further validate the estimations. Three neural networks models with different inputs including epicentral distance, focal depth and magnitude of the earthquake records were trained and the output results were compared with available nonlinear regression analysis. The comparisons exhibited that the present neural networks model did have a better performance than that of the other methods, as the calculation results were more reasonable and closer to the actual seismic records. Besides, the distributions of estimating peak ground acceleration from both of computations and measurements might provide valuable information from theoretical and practical standpoints.