Predicting Marshall Asphalt Stability Using Supervised Machine Learning Algorithms, support vector machine and random forest

Road construction and maintenance organizations usually use certain criteria to qualify asphalt mixtures before use in construction. One of the most important features that is measured in the asphalt mixing and quality control plan is the Marshall asphalt stability. This study examines the use of machine learning techniques to predict Marshall asphalt stability. Due to the time-consuming and costly process of asphalt production and quality control, it is necessary to use new methods in this process. In this research, two supervised support vector machine and random forest algorithms, which are machine learning algorithms, were used to predict the marshal asphalt stability. For this purpose, the test results of 2000 asphalt samples including Granulation of aggregate, Fracture percentage, bitumen adsorption, bitumen specific gravity, actual specific gravity of materials, bitumen consumption percentage, dust to effective binder ratio and Marshall asphalt stability for training and evaluation Models were used. After modeling and evaluation, the value of R2 is 87.5 for the support vector machine method and 82.69 for the random forest. Also, MAPE, RMES and SDE values for SVM were 3.1073, 40.042 and 0.0208, respectively, and for RF were 3.1641, 41.870 and 0.0211, respectively. The results show the efficiency of the models used against laboratory methods for predicting marshal asphalt stability, which SVM method has a better performance than RF. Machine learning methods can be used to predict other parameters of the asphalt mixing plan and reduce the time, cost and human error of the tests.