الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Any pavement management system’s capacity to accurately predict pavement deterioration is vital to its success. A effective pavement condition prediction technique is a critical tool for maintenance and rehabilitation activity planning and cost allocation.
The main aim of this research is to develop a performance model based on Benkelman Beam (BB) and traffic results by using prediction models derived from the data for the Pavement Condition Index (PCI) that was obtained from three districts belonging to the General Authority for Roads, Bridges and Land Transport (GARBLT) which consist of about Central district (26sections), Middle-Delta (22 sections), and East-Delta (14 sections) with a total length of 124 Kilometers.
The proposed model was validated by comparing the predicted values with actual (PCI) with a coefficient of determination R^2 equals 0.87. The structural examination of in-service pavements is an important activity for both project and network-level pavement management systems. The deflection was measured using the Benkelman Beam. At a distance of 1.5 metres from the pavement’s edge, test points were obtainedThe pavement temperature was recorded for subsequent changes to the deflection values because the deflections measured by the Benkelman Beam are influenced by pavement temperature and seasonal climate variations.
Since the Structure Number (SN) evaluation including in pavements is such an important component of both the Structural Condition Index and the Structural Condition Index (SCI), the resulting deflections from (BB) were converted to a structural number (SN) using a model and the validity has been checked by taking samples from the pavement layers, which revealed a strong correlation between them with a coefficient of determination (R^2) of 0.62.
The structure number in 2018 is predicted from the proposed model and then compared with actual field measurements for the same year. A conclusion is made regarding the validity of the proposed prediction model with a coefficient of determination (R^2) equals 0.91. Because the (BB) reading is crucial in determining the value of the Pavement Condition Index (PCI). The developed prediction model recognized two causal factors in defining pavement performance. They were the pavement age and pavement structure number with age being the most significant factor where the structure number was of minor importance.