### 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 (R^{2}) 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 language | English |
---|---|

Article number | 396 |

Journal | Materials |

Volume | 9 |

Issue number | 5 |

DOIs | |

Publication status | Published - 2016 |

### Fingerprint

### 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

*Materials*,

*9*(5), [396]. https://doi.org/10.3390/ma9050396

**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.

Research output: Contribution to journal › Article

*Materials*, vol. 9, no. 5, 396. https://doi.org/10.3390/ma9050396

}

TY - JOUR

T1 - Modeling of compressive strength for self-consolidating high-strength concrete incorporating palm oil fuel ash

AU - Safiuddin, Md

AU - Raman, Sudharshan Naidu

AU - Salam, Md Abdus

AU - Jumaat, Mohd Zamin

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Artificial neural network (ANN)

KW - Compressive strength

KW - Modeling

KW - Palm oil fuel ash (POFA)

KW - Self-consolidating high-strength concrete (SCHSC)

UR - http://www.scopus.com/inward/record.url?scp=84970015761&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84970015761&partnerID=8YFLogxK

U2 - 10.3390/ma9050396

DO - 10.3390/ma9050396

M3 - Article

VL - 9

JO - Materials

JF - Materials

SN - 1996-1944

IS - 5

M1 - 396

ER -