Prediction of Direct Tensile Strength of Bamboo Fiber Reinforced Concrete Using Artificial Neural Network

Abstract

Bamboo is among the most economically-significant non-timber forest products in the Philippines, and its fiber can be used for concrete reinforcement, known as bamboo fiber reinforced concrete (BFRC). However, BFRC needs further exploration, and its direct tensile strength is an essential factor that needs to be studied. The present study assessed the direct tensile strength of BFRC utilizing finite element modeling (FEM) and Artificial Neural Network (ANN). A positive correlation was found between compressive and direct tensile strength of the BFRC. The optimum ANN model was obtained from the trial-error approach. This model has 5-5-1 (input-hidden neurons-output) network topology and uses the tan sigmoid transfer function for both input-hidden and hidden-output layer which brought the most accurate prediction. The Levenberg–Marquardt (LM) provided the best optimization performance among other algorithms. The best model has a mean squared error (MSE) of 0.0000287 and r values of 0.98619, 0.99936, 0.99516, and 0.98565 for training, testing, validating, and overall. Sensitivity Analysis shows that compressive can significantly change the system’s performance even with a small change in its parameter value.