Comparison of Random Survival Forest with Accelerated Failure Time-Weibull Model for Bridge Deck Deterioration

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.

Multi-Objective Optimization of Maintenance, Rehabilitation, and Reconstruction Decision Making Considering Safety

“…The AFT-Weibull model assumes that the deterioration time follows a Weibull distribution and incorporates covariates into the model through the AFT approach. This AFT-Weibull model has been proven to provide a better model fit for pavement deterioration data than other statistical models ( 18 ). Therefore, a semi-Markov chain process with the transition matrix calculated based on the AFT-Weibull model is developed, which helps overcome the memoryless property of the exponential distribution-based Markov chain process.…”

“…The objectives are integrated into the MRR planning using a pavement deterioration model, where the costs of each objective are estimated based on a predicted condition. Transportation asset deterioration modeling can be categorized into four types: (i) deterministic models such as linear regression ( 14 ) or polynomial regression ( 15 ), (ii) state-based stochastic models such as Markov models or semi-Markov models ( 16 ), (iii) time-based stochastic models such as the Cox model or the accelerated failure time (AFT)-Weibull model ( 17 ), and (iv) machine learning based survival models ( 18 ). Of the different models, the AFT-Weibull model has a flexible form that can take any bathtub shape distribution to fit the deterioration process with a simple formulation, and has been shown in the literature to achieve high accuracy ( 16 , 19 ).…”

An Integrated Model for the Geohazard Accident Duration on a Regional Mountain Road Network Using Text Data

“…In the case of non-linearity, the links between different categories can be maximized. However, estimating a SSVM can be very complex and time-consuming, especially when the kernel function is complex and the dataset size is large [19].…”

“…Recently, more advanced machine learning methods that can model survival times have been proposed. In contrast to the machine learning model, the survival machine learning model was adapted to include censored data and to provide a full probability of deterioration curve [19]. The RSF model is a typical survival machine learning model, which overcomes the weakness of needing to establish the basis for certain assumptions and addresses the high variability and bias of traditional survival analysis [20].…”

“…In the field of transportation applications, Wang et al [24] considered influencing factors, such as the cause of the event, time of the event, and line-related variables, and used the RSF model to describe the duration of subway service interruption. Lu and Ilgin [19] used the RSF model for bridge deck deterioration analysis to provide bridge deterioration survival analysis influence factor identification and survival time prediction. Multiple studies compared the RSF with traditional survival analysis in duration modeling; RSF showed better performance than other methods [25].…”