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 (M_r) of Unbound Granular Materials (UGMs). This paper focused on the development of a LSSVM model to predict the M_r 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 M_r values were determined by Repeated Load Triaxial Test (RLTT). Regression, ANN, and LSSVM models were utilized and compared in predicting the M_r of the investigated blends optimizing the best design model. Results showed that the M_r 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 M_r of the investigated RCA/RCM blends.

Keywords

Least Square Support Vector Machine / Artificial Neural Network / resilient modulus / Recycled Concrete Aggregate / Recycled Clay Masonry

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Acknowledgments

The first and corresponding author was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B2010120).

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