Predicting resilient modulus of recycled concrete and clay masonry blends for pavement applications using soft computing techniques

Abstract

To date, very few researchers employed the Least Square Support Vector Machine (LSSVM) in predicting the resilient modulus (Mr) of Unbound Granular Materials (UGMs). This paper focused on the development of a LSSVM model to predict the Mr of recycled materials for pavement applications and comparison with other different models such as Regression, and Artificial Neural Network (ANN). Blends of Recycled Concrete Aggregate (RCA) with Recycled Clay Masonry (RCM) with proportions of 100/0, 90/10, 80/20, 70/30, 55/45, 40/60, 20/80, and 0/100 by the total aggregate mass were evaluated for use as UGMs. RCA/RCM materials were collected from dumps on the sides of roads around Mansoura city, Egypt. The investigated blends were evaluated experimentally by routine and advanced tests and the Mr values were determined by Repeated Load Triaxial Test (RLTT). Regression, ANN, and LSSVM models were utilized and compared in predicting the Mr of the investigated blends optimizing the best design model. Results showed that the Mr values of the investigated RCA/RCM blends were generally increased with the decrease in RCM proportion. Statistical analyses were utilized for evaluating the performance of the developed models and the inputs sensitivity parameters. Eventually, the results approved that the LSSVM model can be used as a novel tool to estimate the Mr of the investigated RCA/RCM blends.