symmetry S S Article Application of Artiﬁcial Intelligence (AI) for Sustainable Highway and Road System Md Arifuzzaman 1, * , Muhammad Aniq Gul 1 , Kaffayatullah Khan 1 and S. M. Zakir Hossain 2   Citation: Arifuzzaman, M.; Aniq Gul, M.; Khan, K.; Hossain, S.M.Z. Application of Artiﬁcial Intelligence (AI) for Sustainable Highway and Road System. Symmetry 2021, 13, 60. https://doi.org/10.3390/sym1301 0060 Received: 29 November 2020 Accepted: 29 December 2020 Published: 31 December 2020 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional clai- ms in published maps and institutio- nal afﬁliations. Copyright: © 2020 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P.O. Box 380, Al-Hofuf, Al-Ahsa 31982, Saudi Arabia; mgul@kfu.edu.sa (M.A.G.); kkhan@kfu.edu.sa (K.K.) 2 Department of Chemical Engineering, College of Engineering, University of Bahrain, P.O. Box 32038, Isa Town, Kingdom of Bahrain, Bahrain; zhossain@uob.edu.bh * Correspondence: marifuzzaman@kfu.edu.sa; Tel.: +966-13-589-9047; Fax: +966-13-581-7068 Abstract: There are several environmental factors such as temperature differential, moisture, oxi- dation, etc. that affect the extended life of the modiﬁed asphalt inﬂuencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R 2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model. Keywords: artiﬁcial intelligence; asphalt; adhesion; highway 1. Introduction A resilient asphalt pavement ensures smooth vehicle movement and resists the harm- ful effects of the environment [1,2]. The two most important factors, among the several types of environmental factors that endanger the surface resistance of asphalt, are moisture and oxidation [3–5]. Numerous actions have been adopted to avoid the adverse effects of these factors on the surface of asphalt pavements. Indeed, there is no clear strategy to provide a clear cut solution and does not differentiate the process of riding quality loss. The differed processes of deterioration or prolonged durability are the result of better properties due to the actions taken, which are gradually eliminated by the effects of moisture and oxidation over the life of the asphalt pavement surface. Based on this temporary solution, it can be concluded that providing properties developed over a longer time can extend the durability of asphalt surfaces. In this context, it is important to become familiar with the important properties of bituminous binders that are affected mainly by the addition of moisture and oxidation [6–8]. During the life of the asphalt pavement surface, moisture and oxidation cause loss of adhesion between binder and aggregate. In the presence of moisture between the asphalt binder and the binder, the adhesive and cohesive properties of the asphalt are weakened. Asphalt adhesion isimpaired by the action of water vapor, and the action of such steam is deﬁned as stripping. This release effect destroys the adhesive bonds in the asphalt binder and at the interface between the asphalt and the surface of the aggregates. Apart from moisture, asphalt oxidation also changes the composition of the asphalt and then inﬂuences Symmetry 2021, 13, 60. https://doi.org/10.3390/sym13010060 https://www.mdpi.com/journal/symmetry