Date of Award
Open Access Thesis
Civil and Environmental Engineering
States, counties, and municipalities rely on pavement performance curves to forecast future pavement conditions in their jurisdictions. Accurate prediction is essential for budget planning and the identification of candidates for rehabilitation. This study investigates the use of a grey model (GM) to estimate and predict pavement conditions. An advantage of the GMs is that they do not require a large sample size for model estimation. This aspect is important since smaller towns and municipalities often cannot afford to collect pavement condition data frequently due to cost. There are other situations where sample size may be limited, such as using project-level pavement condition data to determine the optimal maintenance plan to prolong the life of the pavement. To this end, a novel trigonometric GM is applied to estimate and predict pavement conditions. The model’s performance is compared with the performance of the first-order GM (i.e., GM(1,1)) and two S-shaped nonlinear models using pavement data from South Carolina. The estimation results indicate that the proposed GM (1,1|cos(ωt)) model outperforms the S-shaped nonlinear models and GM(1,1) model for the two considered pavement types (bituminous and bituminous over concrete) and two rehabilitation methods (mill-andreplace 2 to 4 inches + 2-inch overlay and mill-and-replace 1 to 2 inches + 4-inch overlay) in terms of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).
Kouyate, A.(2021). Evaluation of a Trigonometric Grey Model for Estimating And Predicting Pavement Condition. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/6739