Modeling of compressive strength for self-consolidating high-strength concrete incorporating palm oil fuel ash

Md Safiuddin, Sudharshan Naidu Raman, Md Abdus Salam, Mohd Zamin Jumaat

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R2) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN.

Original languageEnglish
Article number396
JournalMaterials
Volume9
Issue number5
DOIs
Publication statusPublished - 2016

Fingerprint

Ashes
Palm oil
Fuel oils
Compressive strength
Concretes
Neural networks
Testing
palm oil
Concrete mixtures
Mean square error

Keywords

  • Artificial neural network (ANN)
  • Compressive strength
  • Modeling
  • Palm oil fuel ash (POFA)
  • Self-consolidating high-strength concrete (SCHSC)

ASJC Scopus subject areas

  • Materials Science(all)

Cite this

Modeling of compressive strength for self-consolidating high-strength concrete incorporating palm oil fuel ash. / Safiuddin, Md; Raman, Sudharshan Naidu; Salam, Md Abdus; Jumaat, Mohd Zamin.

In: Materials, Vol. 9, No. 5, 396, 2016.

Research output: Contribution to journalArticle

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