Modeling asphalt pavement condition using artificial neural networks

A Bayesian decision support system for optimizing pavement management programs

“…Regarding the prediction models, the major two groups of prediction models are deterministic and probabilistic models [ 1 ]. Several approaches have been adopted to develop prediction models such as Regression analysis [ 8 ], Decision-trees [ 9 ], Artificial neural networks [ 10 ], Markov chain models [ 11 ], Bayesian approaches [ 12 ], and many others. Compared with deterministic models, probabilistic models have a better ability to handle the complexity associated with the pavement deterioration process [ 13 ].…”

Time-series forecasting of road distress parameters using dynamic Bayesian belief networks

“…Various performance prediction models have been reported in the literature; the majority of them fall into two categories: deterministic models and probabilistic models (George et al , 1989). Different approaches were used over the years for evaluating pavement performance, including regression analysis (Pan et al , 2011; Kim and Kim, 2006; Mills et al , 2012), artificial neural networks (ANN) (Yang et al , 2003; Roberts and Attoh-Okine, 1998; Vyas et al , 2022), Bayesian belief networks (BBN) (Pantuso et al , 2021; Xiao et al , 2022), Markov chain models (Kobayashi et al , 2010; Kobayashi et al , 2012; Salman and Gursoy, 2022), decision trees (Piryonesi and El-Diraby, 2020) and others (Hu et al , 2022). According to Osorio-Lird et al (2018), the advantages of probabilistic models, specifically Bayesian approaches and Markov chain models over regression and ANN methods in predicting the pavement performance are their capability to capture uncertainty and employment of a transition probability matrix (TPM) for predicting future behavior based on the current state.…”