A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response

TL;DR

This paper introduces a deep learning approach using bidirectional LSTM networks to accurately estimate railway track stiffness from drive-by vibration data, enhancing infrastructure health monitoring capabilities.

Contribution

It develops a novel LSTM-BiLSTM model with framing techniques for improved resolution in infrastructure condition estimation from vibration signals.

Findings

Achieves a maximum mean absolute percentage error of 1.7% in railpad stiffness estimation.

Successfully identifies local stiffness reductions despite noise.

Demonstrates the effectiveness of temporal analysis in vibration-based infrastructure monitoring.

Abstract

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.