الفهرس 
CONTENTSOnly 14 pages are availabe for public view
 |  | from | 181 |  |  |
| | | from | 181 | | |
Abstract
In Egypt, the current road network is expanding rapidly as part of asset infrastructure build up. Maintenance and Rehabilitation (M&R) programs for these networks require complete implementation of an effective pavement management system (PMS). This study uses the Long-Term Pavement Performance (LTPP) database for flexible pavements to develop a simplified regression model that links PCI with IRI. Measured pavement distresses from 1448 LTPP sections from the Specific Pavement Studies (SPS) and General Pavement Studies (GPS) representing 12744 data points, were utilized for the PCI estimation. A total of 1208 sections with 10868 data points were used for model development while 240 sections with 1876 data points were used for the model validation. A sigmoid function was found to best express the relationship between PCI and IRI with a coefficient of determination (R2) of 0.995. In addition, the bias in the predicted IRI values was significantly very low. The model was validated using different dataset with high accurate predictions (R2 = 0.992). Moreover, a pavement condition rating based on IRI was proposed. This system yields rating equivalent to the widely used PCI rating method which is based on the pavement condition. This research also presents a development that has been done to build a probabilistic performance prediction model as part of an integrated pavement management system. The Markov-chain models are used for predicting the performance of pavement during the life time of road networks. In this thesis, a multi-objective optimization model for pavement maintenance and rehabilitation strategies on network level was developed. Also, a user cost model that estimates the benefits gained in terms of the user cost after the repair decisions is implemented. A two-objective optimization model which minimizing maintenance and user costs and maximizing pavement condition for used road network was developed in this study. A genetic-algorithm-based procedure is used for solving the multi-objective optimization problem. from the optimal solutions represented by condition and cost, a decision maker can easily obtain maintenance and rehabilitation planning information with minimum cost and maximum condition. The developed model is applied on a network of roads and showed its ability to derive the optimal or near optimal solution.