In addressing the challenges of analyzing seismic response data for high-speed railroads,
this research introduces a hybrid prediction model combining convolutional neural networks …

In this paper, a novel method is proposed to predict the nonlinear seismic response of train-
bridge coupled (TBC) systems by utilizing a long short-term memory (LSTM) recurrent neural …

In traditional train–bridge coupled system (TBCS), simply supported box girder bridges are
often modeled using Euler beam elements, neglecting their spatial structure. This …

High complexity and randomness in high-speed train-bridge interactive dynamic analysis
under earthquake lead to massive calculations in high-speed railway seismic design. To …

Random analysis has emerged as the dominant approach for analyzing train-bridge
coupled (TBC) systems, replacing deterministic analysis. This shift is driven by …

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 …

Bridges represent a significant proportion of high-speed railway (HSR) lines in China, and
high-speed trains operate on these structures for prolonged periods. Consequently, the …

In previous simulations of train–bridge interaction systems (TBIS), the supporting system for
the train are commonly treated as beam structures, leading to less accurate results …

Accurate and efficient safety assessment for train-bridge coupled (TBC) systems is
paramount in railway engineering. Traditional neural networks, though efficient and apt for …

The global expansion of high-speed railway networks has elevated their significance as a
primary mode of transportation, particularly concerning train running safety (TRS) on bridges …