| Summary: | Palm Oil Fuel Ash (POFA) is used as a supplementary cementitious material in
concrete. Using different percentages of POFA leads to a non-linear variation among the
characteristics of concrete. This study aims at developing an empirical model to predict the
compressive strength of concrete using POFA as a cement replacement material and other
properties of the concrete such as the slump and modulus of elasticity using an artificial
neural network. Mixtures of concrete were selected with water-to-binder ratios of 0.50, 0.55
and 0.60, and 10%, 20%, 30% and 40% of the cement content was POFA. The 28-day
compressive strength was tested, and the experimental results show that 0%–20% of POFA
inclusion in the concrete mixtures has the most positive effects on the compressive strength.
Then, a three-layer feed forward-back propagation ANN model with three inputs and three
outputs was developed. Finally, the best architecture for the model was trained, tested and
validated.
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