الفهرس 
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Abstract
Pavement evaluation is conducted to assess the functional and/or structural condition of existing pavement systems which can be done on a project or network levels. Through pavement condition assessment, pavement deterioration models can be established and thus maintenance and rehabilitation alternatives can be proposed. This helps in preserving the expensive transportation infrastructure network. A comprehensive pavement management system questionnaire (PMSQ) was designed to investigate the common flexible pavement distresses in Egypt, current maintenance techniques and existing pavement management systems (PMS), if any. from the questionnaire results, it was found that roads and transport directorates (RTDs) in Egypt evaluate the existing pavement conditions by visual survey only. Furthermore, no pavement condition index is being used to express the pavement condition. Moreover, RTDs take the maintenance decision only based on experience, or received complaints from road users. Finally, RTDs suffer from a big gap between the available funds and the maintenance and repair costs especially post the COVID -19 pandemic. Thus, this research aims at developing an effective PMS for (RTDs) in Egypt. In order to establish the PMS, first a simple but accurate pavement condition index is needed. Thus, pavement condition data was collected from literature, the General Authority for Roads, Bridges and Land Transport (GARBLT) in Egypt and the Long-Term Pavement Performance (LTPP) database. Due to its accuracy, only the LTPP data was used to modify the current pavement condition rating (PCR) method to yield comparable values to the very well-known Pavement Condition Index (PCI). The least square method was used to optimize the current distress weight in the PCR method to produce comparable values to the PCI method. To find the modified weights, a total of 851 LTPP data points and 68 hypothetical points (70% of the collected data from the LTPP general pavement studies (GPS) and specific pavement studies (SPS) in addition to hypothetical sections) were randomly selected while 362 LTPP data points and 30 hypothetical points (30% of the data) were used for the verification of the suggested weights. Collapse of the roads side slopes, polished aggregate and shoving were also, added to the proposed modified pavement condition rating (MPCR) method based on the recommendations of the RTDs engineers. The proposed MPCR method was found to produce very similar values and pavement conditions to the well-known PCI method with reasonable accuracy (R2 = 0.846) and (R2 = 0.874) based on the data used for the development and verification respectively. The MPCR was also validated using pavement distress data collected from two rural roads in Egypt contently 53 points and the data showed reasonable accuracy with R2 of 0.912 as compared to the PCI method. The MPCR method was found to yield pavement condition states that matches the others produced by the PCI method with an accuracy of 81.6%. Secondly, a pavement deterioration prediction model was developed using both Markov chain and Artificial Neural Networks (ANN). The Markov chain model was developed based on 245 LTPP sections containing variety of pavement condition states from very poor to very good with no maintenance in these sections. The ANN model used the same data that was used to build the Markov chain model. The accuracy of the ANN model was poor with R2 = 0.53. The last step was to suggest an optimization methodology for fund allocation to maximize the average pavement condition index and minimize the maintenance cost. Thus, the future pavement conditions based on Markov chain model was used to develop a particle swarm optimization (PSO) model. The PSO was used to solve the multi objective optimization problem based on minimum maintenance cost and maximum average MPCR by using the ideal solutions indicated by cost and pavement condition. The generated model was tested on a network of roads and was shown to be capable of generating optimal or near-optimal solutions. The proposed PMS framework for RTDs when implemented is expected to help in making maintenance and rehabilitation (M&R) decisions to keep the pavements in good workable condition.