Automated location of steel truss bridge damage using machine learning and raw strain sensor data

Strategic major infrastructure ageing requires structural health monitoring usage to avoid critical safety issues and disasters. Machine Learning can be a valuable tool to automate the process of analysing raw monitoring data. Usually, frequency domain damage-sensitive features are extracted with da...

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Bibliographic Details
Published in:	Automation in construction 2022-06, Vol.138, p.104249, Article 104249
Main Authors:	Parisi, F., Mangini, A.M., Fanti, M.P., Adam, Jose M.
Format:	Article
Language:	eng
Subjects:	Algorithms Artificial neural networks Automation Classification Damage detection Damage location Data analysis Diagnostic systems Dimensional analysis Feature extraction Feature selection Finite element method Machine learning Neural network Railway bridges Railway engineering Railway steel bridge Sensors Signal processing Steel bridges Structural health monitoring Timeseries Truss bridges
Online Access:	Get full text
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Summary:	Strategic major infrastructure ageing requires structural health monitoring usage to avoid critical safety issues and disasters. Machine Learning can be a valuable tool to automate the process of analysing raw monitoring data. Usually, frequency domain damage-sensitive features are extracted with data pre-processing procedures; thus these features are used as input for classification or regression problems. This paper describes a method of locating damage in steel truss railway bridges through machine learning classification tools, enabling automatic analysis of raw strain sensors signals without any pre-processing or preliminary feature extraction. Data were generated by simulating different damage scenarios with a finite element software, and then were processed by two machine learning classification tools: (a) the K-nearest Neighbours was adopted with the Dynamic Time Warping algorithm metric to select the most informative features; (b) a model suitable for high-dimensional data analysis, known as the Convolutional Neural Network, was then trained to classify strain sensors time series. The results indicate that the method applied can detect damages with an accuracy of 93% and is suitable for structural health monitoring.
ISSN:	0926-5805 1872-7891