## 1 Introduction  
In asphalt pavement construction, the selection of appropriate asphalt binder with the required properties to mitigate the challenges of pavement deterioration is critical in prolonging the service life of the asphalt pavement exposed to ageing conditions. Asphalt binder, within the asphalt, dominates the viscoelastic properties of the asphalt pavement subjected to various pavement distresses. Rutting and fatigue cracking are serious pavement distresses facing asphalt pavement that are exposed to intermediate and high temperatures prevailing in the tropical regions [1; 2; 3; 4; 5]. Rutting in asphalt pavement occurs as a result of accumulated strain which induces a non-recoverable deformation along the wheel path of the pavement [6]. The plastic deformation that is formed along the wheel path creates discomfort to the road user and reduces the service life of the pavement. On the other hand, fatigue cracking develops in asphalt pavement due to repeated loading on the asphalt pavement surface. Fatigue cracking is caused by weak, sub-grade, base and poor material design, and reduced strain tolerance of the asphalt mixture resulting from long-term field ageing [1, 7]. The selection of appropriate asphalt binder has been reported to enhance the resistance of hot mix asphalt (HMA) concrete when subjected to rutting and fatigue [2, 8, 9]. Further, laboratory testing of rutting and fatigue in asphalt binder requires advanced testing equipment that is not readily available in the developing countries within the tropical regions. However, governments are saddle with the responsibility of providing quality pavement structure with the limited available budget. In such cases, the application of a predictive model would reduce project cost and deliver quality pavement structure. The existing predictive models studies in pavement construction mainly focused on predicting the concrete dynamic modulus (E*), complex shear modulus (G*) and phase angle of the binder. These models do not account for the binder selection for rutting and fatigue cracking resistance in a pavement structure [10, 11, 12], which this study seeks to address using machine learning models. Machine learning within the last few years has made its presence felt in various sectors; on the internet [13], communication system [14, 15], vision and voice recognition [16, 17, 18, 19], smart device and instrumentations [20], as well as other engineering applications [21, 22, 23, 24]. Its deployment has witnessed unprecedented results and revolution in the filed of artificial intelligence. Computational methods available in machine learning field include the artificial neural networks, support vector machine, Gaussian process regression, recurrent neural networks and others. The artificial neural networks (ANN) is an artificial intelligence-based algorithm that uses the abstraction of the biological neural networks. Although ANN performance in literature has been reported to have good predictive results, it is often criticised for having high computational cost (iteration tuning) and for often trapped at a local minimum. Further, data overfitting is another disadvantage critics observed about ANN; that is, the inability of the model to correctly map new inputs to corresponding target values [25, 26]. To overcome these problems, a non-parametric approach such as Gaussian process minimises the data overfitting by defining a distribution function and setting an initial distribution to unlimited possibilities over the function directly [27]. Comparative studies of Gaussian process regression (GPR) and other machine learning tools such as ANN and support vector machine (SVM) show that the algorithm is an efficient machine learning tool with higher accuracy on the generalisation data set [27, 28, 29, 30, 31, 32, 33]. In the asphalt technology field, machine learning models have been widely reported to have higher predictive accuracy over regression models [34, 35, 36, 37, 38].