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

  • Mosbeh R. Kaloop
  • Alaa R. Gabr
  • Sherif M. El-Badawy
  • Ali Arisha
  • Sayed Shwally
  • Jong Wan HuEmail author
Research Article


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.


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


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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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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mosbeh R. Kaloop
    • 1
    • 2
    • 3
  • Alaa R. Gabr
    • 3
  • Sherif M. El-Badawy
    • 3
  • Ali Arisha
    • 3
  • Sayed Shwally
    • 3
  • Jong Wan Hu
    • 1
    • 2
    Email author
  1. 1.Department of Civil and Environmental EngineeringIncheon National UniversityIncheonSouth Korea
  2. 2.Incheon Disaster Prevention Research CenterIncheon National UniversityIncheonSouth Korea
  3. 3.Department of Public Works and Civil EngineeringMansoura UniversityMansouraEgypt

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