Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A rapid analysis framework for seismic response prediction and running safety assessment of train-bridge coupled systems
https://orcid.org/0000-0002-1636-4111
Abstract With high-speed railway lines increasing gradually, the running safety assessment (RSA) of train-bridge coupled (TBC) systems has become an indispensable part of railway seismic design. However, the modeling of TBC systems is difficult and computationally complex. Therefore, it is difficult to conduct real-time assessment and response analysis rapidly. With this in mind, this paper integrates a surrogate framework based on deep learning methods and can help rapid-diagnose the seismic response and RSA. Meanwhile, real-time information feedback is performed to determine the safe speed of the train. To this end, a convolutional neural network-long short-term memory (CNN-LSTM) model with excellent time-series data processing capability is newly used for the seismic response analysis of the TBC system. Furthermore, response data obtained are processed and input to an interpretable adaptive network-based fuzzy inference system (ANFIS). The ANFIS derives a series of fuzzy rules while conducting the RSA, and these rules can help non-specialists understand the assessment process. Finally, parameter inversion is performed by the ANFIS to determine a safe threshold of train speed. According to the tests, the proposed rapid analysis framework can be applied to seismic response analysis and RSA with high efficiency and accuracy in multiple engineering scenarios. The framework may be helpful for railway design and make it possible to realize real-time data analysis for TBC systems.
Highlights A new rapid analysis framework that can perform comprehensive computations on TBC systems is proposed. A CNN-LSTM model is constructed for seismic response prediction of TBC systems. An interpretable ANFIS is constructed for running safety assessment of TBC systems. A parameter inversion operation is set up for the ANFIS to rapidly determine the safe speed thresholds of trains.
A rapid analysis framework for seismic response prediction and running safety assessment of train-bridge coupled systems
https://orcid.org/0000-0002-1636-4111
Abstract With high-speed railway lines increasing gradually, the running safety assessment (RSA) of train-bridge coupled (TBC) systems has become an indispensable part of railway seismic design. However, the modeling of TBC systems is difficult and computationally complex. Therefore, it is difficult to conduct real-time assessment and response analysis rapidly. With this in mind, this paper integrates a surrogate framework based on deep learning methods and can help rapid-diagnose the seismic response and RSA. Meanwhile, real-time information feedback is performed to determine the safe speed of the train. To this end, a convolutional neural network-long short-term memory (CNN-LSTM) model with excellent time-series data processing capability is newly used for the seismic response analysis of the TBC system. Furthermore, response data obtained are processed and input to an interpretable adaptive network-based fuzzy inference system (ANFIS). The ANFIS derives a series of fuzzy rules while conducting the RSA, and these rules can help non-specialists understand the assessment process. Finally, parameter inversion is performed by the ANFIS to determine a safe threshold of train speed. According to the tests, the proposed rapid analysis framework can be applied to seismic response analysis and RSA with high efficiency and accuracy in multiple engineering scenarios. The framework may be helpful for railway design and make it possible to realize real-time data analysis for TBC systems.
Highlights A new rapid analysis framework that can perform comprehensive computations on TBC systems is proposed. A CNN-LSTM model is constructed for seismic response prediction of TBC systems. An interpretable ANFIS is constructed for running safety assessment of TBC systems. A parameter inversion operation is set up for the ANFIS to rapidly determine the safe speed thresholds of trains.
A rapid analysis framework for seismic response prediction and running safety assessment of train-bridge coupled systems
https://orcid.org/0000-0002-1636-4111
Abstract With high-speed railway lines increasing gradually, the running safety assessment (RSA) of train-bridge coupled (TBC) systems has become an indispensable part of railway seismic design. However, the modeling of TBC systems is difficult and computationally complex. Therefore, it is difficult to conduct real-time assessment and response analysis rapidly. With this in mind, this paper integrates a surrogate framework based on deep learning methods and can help rapid-diagnose the seismic response and RSA. Meanwhile, real-time information feedback is performed to determine the safe speed of the train. To this end, a convolutional neural network-long short-term memory (CNN-LSTM) model with excellent time-series data processing capability is newly used for the seismic response analysis of the TBC system. Furthermore, response data obtained are processed and input to an interpretable adaptive network-based fuzzy inference system (ANFIS). The ANFIS derives a series of fuzzy rules while conducting the RSA, and these rules can help non-specialists understand the assessment process. Finally, parameter inversion is performed by the ANFIS to determine a safe threshold of train speed. According to the tests, the proposed rapid analysis framework can be applied to seismic response analysis and RSA with high efficiency and accuracy in multiple engineering scenarios. The framework may be helpful for railway design and make it possible to realize real-time data analysis for TBC systems.
Highlights A new rapid analysis framework that can perform comprehensive computations on TBC systems is proposed. A CNN-LSTM model is constructed for seismic response prediction of TBC systems. An interpretable ANFIS is constructed for running safety assessment of TBC systems. A parameter inversion operation is set up for the ANFIS to rapidly determine the safe speed thresholds of trains.
A rapid analysis framework for seismic response prediction and running safety assessment of train-bridge coupled systems
Zhang, Peng (Autor:in) / Zhao, Han (Autor:in) / Shao, Zhanjun (Autor:in) / Jiang, Lizhong (Autor:in) / Hu, Huifang (Autor:in) / Zeng, Yingying (Autor:in) / Xiang, Ping (Autor:in)
25.11.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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