Predicting CBR value of stabilized pond ash with lime and lime sludge using multivariate adaptive regression splines

Abstract

In this study, multivariate adaptive regression splines (MARS) model with order two and three were developed for predicting the California bearing capacity (CBR) value of pond ash stabilized with lime and lime sludge. To this aim, the model had five variables named maximum dry density, optimum moisture content, lime percentage, lime sludge percentage, and curing period as inputs, and CBR as output variable. MARS-O3 has the best results, which its R2 stood at 0.9565 and 0.9312, and PI 0.0709 and 0.1061 for the training and testing phases, respectively. In both developed models, the estimated CBR values in training and testing stages specify acceptable agreement with experimental results, representing the workability of proposed equations for predicting the CBR values with high accuracy. Comparison of two developed equations supplied that MARS-O3 has a better result than MARS-O2. Based on error curves, the MARS-O3 model results in the lowest error percentage in the CBR predicting process, providing roughly accurate prediction than those of the rest developed methods specified. Therefore, MARS-O3 could be recognized as the proposed model.