contributor author | Minwoo Chang; Marc Maguire; Yan Sun | |
date accessioned | 2019-03-10T12:14:47Z | |
date available | 2019-03-10T12:14:47Z | |
date issued | 2019 | |
identifier other | %28ASCE%29IS.1943-555X.0000466.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4255200 | |
description 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. | |
publisher | American Society of Civil Engineers | |
title | Stochastic Modeling of Bridge Deterioration Using Classification Tree and Logistic Regression | |
type | Journal Paper | |
journal volume | 25 | |
journal issue | 1 | |
journal title | Journal of Infrastructure Systems | |
identifier doi | 10.1061/(ASCE)IS.1943-555X.0000466 | |
page | 04018041 | |
tree | Journal of Infrastructure Systems:;2019:;Volume ( 025 ):;issue: 001 | |
contenttype | Fulltext | |