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Influence of earthquake-induced hydrodynamic pressure on train-bridge interactions based on back-propagation neural network
https://orcid.org/0000-0002-1449-6604
https://orcid.org/0000-0002-9683-6341
Forecasting the behavior of the train-bridge system under strong waves is crucial in designing the cross-sea bridge. However, the influence of earthquake-induced hydrodynamic pressure on the dynamic system may be significant and need to be adequately understood. This study investigates the influence of earthquake-induced hydrodynamic force on the stochastic responses of the train-bridge interaction using the Newmark-β method. A surrogate model named back-propagation neural network is implemented by correlating wave samples with the stochastic responses of the train-bridge system. Such a model improves computation efficiency and avoids further time-step integration. The Pintan’s bridge, located in China’s Eastern Region, is selected as a case study. The results show that the earthquake-induced hydrodynamic force involves significant responses of the train-bridge system. Moreover, the maximum dynamic amplification factor of the deck and the pier are 6% and 12.5%, respectively. Finally, a significant value of the peak period minimizes the effect of the earthquake-induced hydrodynamic responses on the train-bridge system.
Influence of earthquake-induced hydrodynamic pressure on train-bridge interactions based on back-propagation neural network
https://orcid.org/0000-0002-1449-6604
https://orcid.org/0000-0002-9683-6341
Forecasting the behavior of the train-bridge system under strong waves is crucial in designing the cross-sea bridge. However, the influence of earthquake-induced hydrodynamic pressure on the dynamic system may be significant and need to be adequately understood. This study investigates the influence of earthquake-induced hydrodynamic force on the stochastic responses of the train-bridge interaction using the Newmark-β method. A surrogate model named back-propagation neural network is implemented by correlating wave samples with the stochastic responses of the train-bridge system. Such a model improves computation efficiency and avoids further time-step integration. The Pintan’s bridge, located in China’s Eastern Region, is selected as a case study. The results show that the earthquake-induced hydrodynamic force involves significant responses of the train-bridge system. Moreover, the maximum dynamic amplification factor of the deck and the pier are 6% and 12.5%, respectively. Finally, a significant value of the peak period minimizes the effect of the earthquake-induced hydrodynamic responses on the train-bridge system.
Influence of earthquake-induced hydrodynamic pressure on train-bridge interactions based on back-propagation neural network
https://orcid.org/0000-0002-1449-6604
https://orcid.org/0000-0002-9683-6341
Forecasting the behavior of the train-bridge system under strong waves is crucial in designing the cross-sea bridge. However, the influence of earthquake-induced hydrodynamic pressure on the dynamic system may be significant and need to be adequately understood. This study investigates the influence of earthquake-induced hydrodynamic force on the stochastic responses of the train-bridge interaction using the Newmark-β method. A surrogate model named back-propagation neural network is implemented by correlating wave samples with the stochastic responses of the train-bridge system. Such a model improves computation efficiency and avoids further time-step integration. The Pintan’s bridge, located in China’s Eastern Region, is selected as a case study. The results show that the earthquake-induced hydrodynamic force involves significant responses of the train-bridge system. Moreover, the maximum dynamic amplification factor of the deck and the pier are 6% and 12.5%, respectively. Finally, a significant value of the peak period minimizes the effect of the earthquake-induced hydrodynamic responses on the train-bridge system.
Influence of earthquake-induced hydrodynamic pressure on train-bridge interactions based on back-propagation neural network
Wandji Zoumb, Patrick Arnaud (Autor:in) / Li, Xiaozhen (Autor:in)
Advances in Structural Engineering ; 25 ; 1209-1221
01.04.2022
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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