MATLAB GraphicalUser Interface (GUI) forPrediction of Optimum Asphalt Content That Satisfies Marshall Parameters of HRS-Base HotMixtureAsphalt by Using Artificial Neural Networks
Marshall test is the most common standard laboratory test method for hot mixture asphalt used in Indonesia. The Marshall test method aims to measure the stability of aggregate and asphalt mixtures against plastic deformation (flow), as well as to analyze the density and voids of the compacted mixture. The optimum asphalt content of the mixture is usually obtained from the mean value of the optimum asphalt content range at a desired density that satisfies all Marshall parameters (VMA, VIM, VFB, Marshall Stability, Flow, and Marshall Quotient). This study aims to predict the optimum asphalt content of HRS-Base hot mixture asphalt by using Artificial Neural Networks (ANN) and to design a graphical user interface that enables users to perform interactive work. The optimum asphalt content of mixture was determined by using ANN optimization method with MATLAB R2016a 9.0 software. Graphical User Interface (GUI) command to find the optimum asphalt content of HRS-Base hotmix asphalt was given based on the empirical formula that already obtained from previous ANN modeling. The selected learning algorithm was backpropagation with training function Levenberg-Marquardt (trainlm). The selected network architecture was found to give optimum results where the predicted value is same with the target value.
