Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

Md Safiuddin; Sudharshan N Raman; Md Abdus Salam; Mohd Zamin Jumaat

doi:10.3390/ma9050396

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

Materials (Basel). 2016 May 20;9(5):396. doi: 10.3390/ma9050396.

Authors

Md Safiuddin¹, Sudharshan N Raman², Md Abdus Salam³, Mohd Zamin Jumaat⁴

Affiliations

¹ Angelo Del Zotto School of Construction Management, George Brown College, 146 Kendal Avenue, Toronto, ON M5T 2T9, Canada. msafiuddin@georgebrown.ca.
² Department of Architecture, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia. snraman@ukm.edu.my.
³ Department of Civil Engineering, Dhaka University of Engineering & Technology, Gazipur-1700, Dhaka 1213, Bangladesh. masalam@duet.ac.bd.
⁴ Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia. zamin@um.edu.my.

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.

Keywords: artificial neural network (ANN); compressive strength; modeling; palm oil fuel ash (POFA); self-consolidating high-strength concrete (SCHSC).