Developing Multi Linear Regression Models for Estimation of Marshall Stability

Nowadays, asphalt roads are exposed to increasing traffic loads in recent times. It is important to obtain a quality and healthy asphalt road covering when considering the conditions of our country where freight and passenger transportation are carried out by roads. One of the most important issues in asphalt road design is the determination of the optimum percentage of bitumen. The Marshall stability test is utilized for optimum percent bitumen determination. In our work, instead of the long and laborious Marshall experiment process, Multi Linear Regression (MLR) Models are developed as an alternative. Models were developed for Marshall experiment result for Marshall stability prediction. In order to construct stability estimation models, pre-made test parameters are used. These parameters are; the bitumen penetration (P),weight of the sample in the weather (H), the temperature (C), the bitumen weight (G), the sample heights (Y), the bitumen percentage (W), weight of the sample in water (S), the stability (ST). In the performance evaluation of the models, the correlation coefficient (R), the mean percentage errors (MPE) and the meansquare errors (MSE) are used. It is seen that the model with the highest performance value is composed of six variable model in this study formed by the MLR. The R value of the best model is 0.571.The MSE value of the best model is 14841,81. The MPE value of the best model is 9.58