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    Vision and Support Vector Machine–Based Train Classification Using Weigh-in-Motion Data

    Source: Journal of Bridge Engineering:;2022:;Volume ( 027 ):;issue: 006::page 06022001
    Author:
    Zhen Sun
    ,
    João Santos
    ,
    Elsa Caetano
    DOI: 10.1061/(ASCE)BE.1943-5592.0001878
    Publisher: ASCE
    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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      Vision and Support Vector Machine–Based Train Classification Using Weigh-in-Motion Data

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4282627
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    • Journal of Bridge Engineering

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    contributor authorZhen Sun
    contributor authorJoão Santos
    contributor authorElsa Caetano
    date accessioned2022-05-07T20:34:35Z
    date available2022-05-07T20:34:35Z
    date issued2022-6-1
    identifier other(ASCE)BE.1943-5592.0001878.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4282627
    description abstractTrains 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.
    publisherASCE
    titleVision and Support Vector Machine–Based Train Classification Using Weigh-in-Motion Data
    typeJournal Paper
    journal volume27
    journal issue6
    journal titleJournal of Bridge Engineering
    identifier doi10.1061/(ASCE)BE.1943-5592.0001878
    journal fristpage06022001
    journal lastpage06022001-8
    page8
    treeJournal of Bridge Engineering:;2022:;Volume ( 027 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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