LAUSR

Abstract In this study regression analysis using machine learning models was investigated to predict and validate the composition of alternative mineral filler in micro surfacing mix design. To generate the data, 168 experiments were conducted with mixing time (sec), cohesion (30 min) kg.cm, cohesion (60 min) kg.cm, set time (sec), wet track abrasion loss (g/m2) as an additives for the design of alternative fillers such as Copper Slag, Fly Ash and High Calcium Fly Ash. Training and testing of feature vector which were formed after conducting experiment was fed into machine learning regression models for prediction of composition of fillers. Support vector machine with polynomial, radial basis function and PUK kernel, Artificial neural network with RBF kernel and Isotonic regression models were considered in the present study. Machine learning regression models were evaluated using three parameters Correlation coefficient, Spearman rho’s and Mean absolute error. Excellent agreement between regression models and experimental results observed. The methodology used will be useful for prediction of micro surfacing mix design for alternative fillers used in the construction industry.