A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response
This provides an automated tool for monitoring bridge and railway conditions using vibration measurements, but it is incremental as it builds on existing LSTM methods with a bidirectional approach.
The paper tackled estimating infrastructure health parameters like railway track stiffness from drive-by vibration data using a deep learning model combining LSTM and BiLSTM networks, achieving a maximum mean absolute percentage error of 1.7% for railpad stiffness and 0.7% for ballast stiffness in simulations.
The growing volume of available infrastructural monitoring data enables the development of powerful datadriven approaches to estimate infrastructure health conditions using direct measurements. This paper proposes a deep learning methodology to estimate infrastructure physical parameters, such as railway track stiffness, using drive-by vibration response signals. The proposed method employs a Long Short-term Memory (LSTM) feature extractor accounting for temporal dependencies in the feature extraction phase, and a bidirectional Long Short-term Memory (BiLSTM) networks to leverage bidirectional temporal dependencies in both the forward and backward paths of the drive-by vibration response in condition estimation phase. Additionally, a framing approach is employed to enhance the resolution of the monitoring task to the beam level by segmenting the vibration signal into frames equal to the distance between individual beams, centering the frames over the beam nodes. The proposed LSTM-BiLSTM model offers a versatile tool for various bridge and railway infrastructure conditions monitoring using direct drive-by vibration response measurements. The results demonstrate the potential of incorporating temporal analysis in the feature extraction phase and emphasize the pivotal role of bidirectional temporal information in infrastructure health condition estimation. The proposed methodology can accurately and automatically estimate railway track stiffness and identify local stiffness reductions in the presence of noise using drive-by measurements. An illustrative case study of vehicle-track interaction simulation is used to demonstrate the performance of the proposed model, achieving a maximum mean absolute percentage error of 1.7% and 0.7% in estimating railpad and ballast stiffness, respectively.