Vision and Support Vector Machine–Based Train Classification Using Weigh-in-Motion Data
Publication: Journal of Bridge Engineering
Volume 27, Issue 6
Abstract
Trains with a different number of carriages can induce stress responses with varying amplitudes in the long-span steel bridges, which consequently cause different levels of fatigue damage. To better evaluate the fatigue life of bridges, it is important to obtain the volume and types of different trains running on bridges. To overcome errors in identifying the different trains caused by electronic noise and, to more efficiently utilize machine learning techniques, the original train weigh-in-motion (WIM) time series are encoded into images. Subsequently, a support vector machine (SVM) based approach is proposed to classify trains with a different number of carriages. The method is divided into three steps: data conversion for image preprocessing, feature extraction for machine learning, and train category classification with SVM. In the image preprocessing step, the time history of the WIM train passing data is saved into image format. In the feature extraction step, the Histogram of Oriented Gradients (HOG) is obtained in row vectors for each image as input for machine learning. In the train carriage classification step, SVM is adopted as the machine learning model to predict different train types. To verify the proposed approach, train WIM data from the structural health monitoring (SHM) system of a suspension bridge are employed, and an accuracy of 97.5% is achieved in the classification of trains when considering noisy datasets. Compared with other state-of-the-art machine learning algorithms, i.e., AdaBoost, K-Nearest Neighbor (KNN), and Linear Classification (LC) Model, the SVM leads to the highest prediction accuracy and shortest computation time.
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Acknowledgments
This work was financially supported by Project POCI-01-0145-FEDER-007457, CONSTRUCT, Institute of R&D In Structures and Construction funded by FEDER funds through COMPETE2020, Programa Operacional Competitividade e Internacionalização (POCI) and by national funds through FCT, Fundação para a Ciência e a Tecnologia, and by the Project SAFESUSPENSE, Safety Control and Management of Long-Span Suspension Bridges (reference POCI-01-0145-FEDER-031054), funded by COMPETE 2020, POR Lisboa and FCT. The authors thank LNEC for providing the monitoring data.
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Received: Jul 7, 2021
Accepted: Feb 14, 2022
Published online: Mar 29, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 29, 2022
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