This paper introduces a new way of system identification of dynamic based on artificial neural networks (ANN) and explains concretely how to predict seismic response of structures by ANN in a practical example. This ...This paper introduces a new way of system identification of dynamic based on artificial neural networks (ANN) and explains concretely how to predict seismic response of structures by ANN in a practical example. This paper identifies the structural model of a shear system by the feed forward network of the BP (back propagation) algorithm. First of all, the BP network described in this paper has been trained by practical seismic waves and the corresponding imitated seismic response. Then the seismic response of structures under other seismic excitation will be predicted by BP network of ANN that had been trained. The new ANN can identify the dynamical character and predict dynamical response of structures exactly. This paper also discusses the effects of network topology and input layer elements on the network learning and prediction, etc.展开更多
High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitori...High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of buildings.To improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based realtime structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy.The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision predictions.Experiments were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method.In addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study.Furthermore,by conducting a comparative experiment,the impact of the range of seismic wave amplitudes on the prediction accuracy was studied.The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.展开更多
文摘This paper introduces a new way of system identification of dynamic based on artificial neural networks (ANN) and explains concretely how to predict seismic response of structures by ANN in a practical example. This paper identifies the structural model of a shear system by the feed forward network of the BP (back propagation) algorithm. First of all, the BP network described in this paper has been trained by practical seismic waves and the corresponding imitated seismic response. Then the seismic response of structures under other seismic excitation will be predicted by BP network of ANN that had been trained. The new ANN can identify the dynamical character and predict dynamical response of structures exactly. This paper also discusses the effects of network topology and input layer elements on the network learning and prediction, etc.
基金support from the National Natural Science Foundation of China(52025083 and U2139209)XPLORER PRIZE of New Cornerstone Science Foundation,the Shanghai Social Development Science and Technology Research Project(22dz1201400)the Shanghai Urban Digital Transformation Special Fund(202201033).
文摘High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of buildings.To improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based realtime structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy.The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision predictions.Experiments were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method.In addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study.Furthermore,by conducting a comparative experiment,the impact of the range of seismic wave amplitudes on the prediction accuracy was studied.The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.