Learning via Long Short-Term Memory (LSTM) network for predicting strains in Railway Bridge members under train induced vibration

TL;DR

This study demonstrates that LSTM networks can effectively predict strains in railway bridge members from noisy field data, enabling cost-effective bridge health monitoring with fewer sensors.

Contribution

It introduces the use of LSTM for predicting bridge strains from limited and noisy data, reducing the need for extensive sensor deployment.

Findings

LSTM accurately predicts strains from noisy data.

Few sensors suffice for effective strain prediction.

LSTM shows robustness in field data conditions.

Abstract

Bridge health monitoring using machine learning tools has become an efficient and cost-effective approach in recent times. In the present study, strains in railway bridge member, available from a previous study conducted by IIT Guwahati has been utilized. These strain data were collected from an existing bridge while trains were passing over the bridge. LSTM is used to train the network and to predict strains in different members of the railway bridge. Actual field data has been used for the purpose of predicting strain in different members using strain data from a single member, yet it has been observed that they are quite agreeable to those of ground truth values. This is in spite of the fact that a lot of noise existed in the data, thus showing the efficacy of LSTM in training and predicting even from noisy field data. This may easily open up the possibility of collecting data from the bridge with a much lesser number of sensors and predicting the strain data in other members through LSTM network.