Comparison of Random Survival Forest with Accelerated Failure Time-Weibull Model for Bridge Deck Deterioration. Issue 7 (July 2022)

• Record Type: Journal Article
• Authors: Lu, Muyang; Ilgin Guler, S.
• Is Part Of: Transportation research record. Volume 2676:Issue 7(2022)
• Page Start: 296
• Page End: 311
• Publication Date: 2022-07
• Subjects: data and data science -- machine learning (artificial intelligence) -- infrastructure -- infrastructure management and system preservation -- bridge and structures management -- bridge performance measurement/analysis deterioration modeling -- deterioration; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221078281
• Languages: English
• ISSNs: 0361-1981

Abstract

Bridge deck deterioration modeling is critical to infrastructure management. Deterioration modeling is traditionally done using deterministic models, stochastic models, and recently basic machine learning methods. The advanced machine learning-based survival models, such as random survival forest, have not been adapted for use in infrastructure management. This paper introduces random survival forest models for bridge deck deterioration modeling and compare their performance with a commonly used traditional stochastic model, that is, the Weibull distribution-based accelerated failure time (AFT-Weibull) model. To better adapt the random survival model for bridge deck deterioration modeling, the selection of the dependent variables is discussed between two variables: time-in-rating, and cumulative truck traffic. Inspection data from about 22, 000 state-owned bridge decks in Pennsylvania are used to validate and test the performance of the models. The results suggest that cumulative truck traffic is more suitable to be selected as the dependent variable when analyzing the reliability of the bridge deck. Further, the random survival forest model outperformed the AFT-Weibull model in predictive accuracy.