| Past research efforts have used a wide variety of methodological approaches to analyze pavement performance indicators, pavement rehabilitation treatments, and pavement service life. Using big data informatics methods, the intent of this Thesis is to conduct a detailed, statistical assessment of pavement rehabilitation treatments by PPP type, by studying their performance in terms of pavement indicators (International Roughness Index, rutting depth, and Pavement Condition Rating) and in terms of extending pavement lives. In order to model and forecast pavement performance, a three-stage least squares (3SLS) approach is used. For the pavement service life, the elapsed time until the pavement crosses a threshold is investigated, using random parameters hazard-based duration models. The model estimation results show that several influential factors such as traffic characteristic, weather characteristics, pavement characteristics, and drainage condition, affect pavement performance and pavement service life; and these factors differ among pavement rehabilitation treatments and PPP types. |
