## 1 Introduction
The traditional artificial neural networks, support vector machine and decision tree algorithm have been applied in different study areas with higher predictive accuracy over regression models. A number of literatures exist on the application of machine learning modeling in asphalt binder and hot mixed asphalt (HMA) concrete, [PERSON] et al. [34], predicted hot mixed asphalt (HMA) concrete dynamic modulus using the ANN and multivariable regression models. The study's selected input variables were drawn from features of volumetric and particles size gradation of nine mixes to extract 243 data points. Further, principal component analysis was used for orthogonal transformation and resulting principal components were used to calibrate the ANN and multivariable regression models. The results of the fitted test data show that the ANN model satisfactorily estimated the dynamic modulus (E*) of the HMA. [PERSON] and [PERSON], [39], on the other hand, compared the performance random forest algorithm with Witczak model in the prediction of E* of HMA. Using the statistical parameters (R\({}^{2}\) and average errors), the study concluded that the developed model improved the E* as compared to Witczak models. [PERSON] et al. [40], compared the traditional ANN and three existing regression models (Witczak NCHRP 1-37A, Witczak NCHRP 1-40D and Hirsch) in the prediction of E* in HMA concrete using 25 mixes each, from the Kingdom of Saudi Arabia and Idaho State. A total of 3720 cases were extracted from the 50 mixes to build the research database. Three ANN models were evaluated using input variables from the three-existing regression models. The research concluded that Witczak models are more effective in the prediction of E* when compared to the Hirsch model. Further, the input parameters in Witczak NCHRP 1-37A were reported to show a more dynamic effect on the sensitivity scale when compared with Witczak NCHRP 1-40D model inputs variables that were dominated by binder properties. The three evaluated ANN models proved to have improved E* value when compared with the corresponding regression models. A similar research to [PERSON] et al. [40], was conducted by [PERSON] et al. [36]) with the incorporation of recycled asphalt shingles (RAS) and comparison of ANN model and modified Witczak E* model developed by [PERSON] [41]. The developed ANN model also showed improved E* values when compared to [PERSON] RAS model. Further, few studies [10; 11; 12; 42] on machine learning prediction of complex modulus and phase angle did not account for ageing conditioning in asphalt binder. This limited their findings in the prediction of rutting and fatigue parameters in the asphalt binder. Existing literature accounted for a predictive model for dynamic modulus (E*) in asphalt mixture. For example, the Witczak model can predict E* using parameters that can be extracted from a basic experiment and manufacturer's specifications. The model is cost effective in mechanistic-empirical pavement design guide (MEPDG). However, predictive models for the design of mixture parameters cannot standalone without a complimentary binder model [43]. The objective of this study is to develop an efficient predictive model for the selection of binder to resist rutting and fatigue cracking in the asphalt pavement structure at intermediate and high temperatures by the inclusion of a novel approach of employing unaged parameters together with GPR algorithm for the prediction of rutting and fatigue cracking. The sections below explain, in detail, the methodology employed for this objective and the results indicating the obtained success in the prediction of rutting and fatigue cracking in asphalt pavements.