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Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model
Joint Authors
Wang, Hao
Lu, Pan
Tolliver, Denver
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Prediction of bridge component condition is fundamental for well-informed decisions regarding the maintenance, repair, and rehabilitation (MRR) of highway bridges.
The National Bridge Inventory (NBI) condition rating is a major source of bridge condition data in the United States.
In this study, a type of generalized linear model (GLM), the ordinal logistic statistical model, is presented and compared with the traditional regression model.
The proposed model is evaluated in terms of reliability (the ability of a model to accurately predict bridge component ratings or the agreement between predictions and actual observations) and model fitness.
Five criteria were used for evaluation and comparison: prediction error, bias, accuracy, out-of-range forecasts, Akaike’s Information Criteria (AIC), and log likelihood (LL).
In this study, an external validation procedure was developed to quantitatively compare the forecasting power of the models for highway bridge component deterioration.
The GLM method described in this study allows modeling ordinal and categorical dependent variable and shows slightly but significantly better model fitness and prediction performance than traditional regression model.
American Psychological Association (APA)
Lu, Pan& Wang, Hao& Tolliver, Denver. 2019. Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1200854
Modern Language Association (MLA)
Lu, Pan…[et al.]. Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model. Mathematical Problems in Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1200854
American Medical Association (AMA)
Lu, Pan& Wang, Hao& Tolliver, Denver. Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1200854
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200854