Recycled aggregates concrete compressive strength prediction using Artificial Neural Networks (ANNS)

Mohamad Ali Ridho Khairul Anuar, Chayut Ngamkhanong, Yubin Wu, Sakdirat Kaewunruen

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
76 Downloads (Pure)


The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside with other benefits such as minimising the usage of natural resources in exploitation to produce new conventional concrete. Eventually, this will lead to reducing the construction waste, carbon footprints and energy consumption. This paper aims to study the recycled aggregate concrete compressive strength using Artificial Neural Network (ANN) which has been proven to be a powerful tool for use in predicting the mechanical properties of concrete. Three different ANN models where 1 hidden layer with 50 number of neurons, 2 hidden layers with (50 10) number of neurons and 2 hidden layers (modified activation function) with (60 3) number of neurons are constructed with the aid of Levenberg-Marquardt (LM) algorithm, trained and tested using 1030 datasets collected from related literature. The 8 input parameters such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age are used in training the ANN models. The number of hidden layers, number of neurons and type of algorithm affect the prediction accuracy. The predicted recycled aggregates compressive strength shows the compositions of the admixtures such as binders, water–cement ratio and blast furnace–fly ash ratio greatly affect the recycled aggregates mechanical properties. The results show that the compressive strength prediction of the recycled aggregate concrete is predictable with a very high accuracy using the proposed ANN-based model. The proposed ANN-based model can be used further for optimising the proportion of waste material and other ingredients for different targets of concrete compressive strength.
Original languageEnglish
Article number17
Number of pages20
Issue number2
Early online date23 Jan 2021
Publication statusPublished - Feb 2021

Bibliographical note

Funding Information:
This research was funded by the European Commission, H2020-MSCA-RISE Project No. 691135 "RISEN: Rail Infrastructure Systems Engineering Network".

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Artificial neural network
  • Compressive concrete strength
  • Concrete engineering
  • Machine learning
  • Prediction model
  • Recycled aggregates

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Computer Science Applications
  • Geotechnical Engineering and Engineering Geology
  • Materials Science(all)


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