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    Stochastic Modeling of Bridge Deterioration Using Classification Tree and Logistic Regression

    Source: Journal of Infrastructure Systems:;2019:;Volume ( 025 ):;issue: 001
    Author:
    Minwoo Chang; Marc Maguire; Yan Sun
    DOI: 10.1061/(ASCE)IS.1943-555X.0000466
    Publisher: American Society of Civil Engineers
    Abstract: This paper presents a new method to develop stochastic deterioration models using a combination of methods including Markov chains, logistic regression, and classification trees. It is computationally more efficient to use logistic regression with the Markov chain process than it is to use optimization-based approaches, and the former is shown to marginally improve the prediction of condition ratings for small data sets. Annually inspected bridge data are split into groups using a classification tree, and logistic regression is used to determine transition probabilities for a Markov chain process. A case study was conducted to determine the effectiveness of using the proposed logistic regression and Markov chain approach for the small data sets created by the classification tree. Wyoming bridge inspection data were split into 15 subsets based on 5 explanatory variables, and deterioration models were developed for each subset. Error analysis showed that logistic regression performed marginally better than traditional methods when estimating the transition probability matrix when limited data are accessible.
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      Stochastic Modeling of Bridge Deterioration Using Classification Tree and Logistic Regression

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    contributor authorMinwoo Chang; Marc Maguire; Yan Sun
    date accessioned2019-03-10T12:14:47Z
    date available2019-03-10T12:14:47Z
    date issued2019
    identifier other%28ASCE%29IS.1943-555X.0000466.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4255200
    description abstractThis paper presents a new method to develop stochastic deterioration models using a combination of methods including Markov chains, logistic regression, and classification trees. It is computationally more efficient to use logistic regression with the Markov chain process than it is to use optimization-based approaches, and the former is shown to marginally improve the prediction of condition ratings for small data sets. Annually inspected bridge data are split into groups using a classification tree, and logistic regression is used to determine transition probabilities for a Markov chain process. A case study was conducted to determine the effectiveness of using the proposed logistic regression and Markov chain approach for the small data sets created by the classification tree. Wyoming bridge inspection data were split into 15 subsets based on 5 explanatory variables, and deterioration models were developed for each subset. Error analysis showed that logistic regression performed marginally better than traditional methods when estimating the transition probability matrix when limited data are accessible.
    publisherAmerican Society of Civil Engineers
    titleStochastic Modeling of Bridge Deterioration Using Classification Tree and Logistic Regression
    typeJournal Paper
    journal volume25
    journal issue1
    journal titleJournal of Infrastructure Systems
    identifier doi10.1061/(ASCE)IS.1943-555X.0000466
    page04018041
    treeJournal of Infrastructure Systems:;2019:;Volume ( 025 ):;issue: 001
    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
    yabeshDSpacePersian