الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The condition of a nation’s road network is crucial for public transportation and freight, as well as the economy. Highways are commonly constructed using flexible pavements with asphalt concrete layers, rigid pavements topped with Portland Cement Concrete slabs, or semi-rigid pavements. Recently, rigid pavements are gaining vast interest due to their durability and large traffic volume endurance. This raises the need for a robust method to predict their long-term performance and hence decide the future maintenance and rehabilitation requirements. One of the most descriptive parameters of the pavement ride quality is the International Roughness Index (IRI). It can be used as a performance indicator for the level of serviceability of rigid pavement. The goal of this study is to develop a reliable model that can forecast IRI for jointed plain concrete pavement (JPCP), using climate, traffic, and pavement structural data, without needing to measure pavement distress data. This approach allows government agencies to plan pavement maintenance financially, without requiring field measurements of distresses. This study presents the evaluation of 21 variables covering climate, traffic, and structural parameters in predicting IRI for Jointed Plain Concrete Pavement (JPCP) sections using 1414 data points acquired from the Long-Term Pavement Performance (LTPP) database. It was found that only 10 variables are significant in the IRI prediction for JPCP. Moreover, four modeling techniques were applied, namely, linear regression, Multivariate Adaptive Regression Splines (MARS), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN). It was found that a deep ANN model with a structure of one input layer, three hidden layers and one output layer [10-36-18-9-1] gives the highest IRI prediction accuracy with Root Mean Square Error (RMSE) and coefficient of determination (R2) of 0.117 and 0.92, respectively. Finally, a sensitivity analysis was performed to evaluate the impact of each input variable on the accuracy of IRI prediction for JPCP sections, and it was found that the most influencing variables are initial IRI, pavement age, mean and standard deviation of humidity, standard deviation of evaporation, mean and standard deviation of freezing index, pavement compressive strength, ratio between pavement and base layers thickness, and finally an estimated parameter called Site Factor which is a function of (age, freezing index and soil percentage passing sieve no. 200). The suggested models offer a reliable and cost-effective method for forecasting IRI for JPCP sections using their structural characteristics, age, and environmental conditions. The models’ primary descriptive data requirement makes the models dependable and low maintenance. These findings can assist government agencies in planning pavement maintenance financially and predicting future traffic statistics based on the region’s traffic growth rate and available climatic information from weather stations and previous traffic data.