Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in Predicting the Compressive Strength of POFA Concrete

• Main Authors: Ahmad Nurfaidhi Rizalman, Chen, Choon Lee
• Format: Article
• Language: English; English
• Published: 2020
• Subjects: T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: https://eprints.ums.edu.my/id/eprint/26081/1/Comparison%20of%20Artificial%20Neural%20Network%20%28ANN%29%20and%20Response%20Surface%20Methodology%20%28RSM%29%20in%20Predicting%20the%20Compressive%20Strength%20of%20POFA%20Concrete.pdf; https://eprints.ums.edu.my/id/eprint/26081/2/Comparison%20of%20Artificial%20Neural%20Network%20%28ANN%29%20and%20Response%20Surface%20Methodology%20%28RSM%29%20in%20Predicting%20the%20Compressive%20Strength%20of%20POFA%20Concrete1.pdf; https://eprints.ums.edu.my/id/eprint/26081/

This study presents a comparative study between Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in predicting the compressive strength of palm oil fuel ash (POFA) concrete. The comparison was made based on the same experimental datasets. The inputs investigated in this study w...