Reference
R. R. Samani, A. Núñez, and
B. De Schutter, "A bidirectional long short term memory approach for
infrastructure health monitoring using on-board vibration response,"
Proceedings of the 104th Annual Meeting of the Transportation
Research Board, Washington, DC, 16 pp., Jan. 2025. Paper TRBAM-25-04560.
Abstract
The growing volume of available infrastructural monitoring data enables the
development of powerful data-driven 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.
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BibTeX
@inproceedings{SamNun:25-003,
author = {Samani, Reza Riahi and N{\'{u}}{\~{n}}ez, Alfredo and De
Schutter, Bart},
title = {A Bidirectional Long Short Term Memory Approach for
Infrastructure Health Monitoring Using On-board Vibration
Response},
booktitle = {Proceedings of the 104th Annual Meeting of the Transportation
Research Board},
address = {Washington, DC},
month = jan,
year = {2025},
note = {Paper TRBAM-25-04560}
}