Neural Computing and Applications

, Volume 32, Issue 1, pp 295–308 | Cite as

Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models

  • Aliakbar Gholampour
  • Iman Mansouri
  • Ozgur Kisi
  • Togay OzbakkalogluEmail author
Original Article


This paper investigates the application of three artificial intelligence methods, including multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) for the prediction of the mechanical behavior of recycled aggregate concrete (RAC). A large and reliable experimental test database containing the results of 650 compressive strength, 421 elastic modulus, 152 flexural strength, and 346 splitting tensile strength tests of RACs with no pozzolanic admixtures assembled from the published literature was used to train, test, and validate the three data-driven-based models. The results of the model assessment show that the LSSVR model provides improved accuracy over the existing models in the prediction of the compressive strength of RACs. The results also indicate that, although all three models provide higher accuracy than the existing models in the prediction of the splitting tensile strength of RACs, only the performance of the LSSVR model exceeds those of the best-performing existing models for the flexural strength of RACs. The results of this study indicate that MARS, M5Tree, and LSSVR models can provide close predictions of the mechanical properties of RACs by accurately capturing the influences of the key parameters. This points to the possibility of the application of these three models in the pre-design and modeling of structures manufactured with RACs.


Recycled aggregate concrete (RAC) Mechanical properties Least squares support vector regression (LSSVR) M5 model tree (M5Tree) Multivariate adaptive regression splines (MARS) 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of Civil, Environmental and Mining EngineeringUniversity of AdelaideAdelaideAustralia
  2. 2.Department of Civil EngineeringBirjand University of TechnologyBirjandIran
  3. 3.Faculty of Natural Sciences and EngineeringIlia State UniversityTbilisiGeorgia
  4. 4.School of Engineering and TechnologyUniversity of HertfordshireHatfieldUnited Kingdom

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