Backpropagation Artificial Neural Network Model for Predicting the Mechanical Properties of Bagasse Ash Blended Concrete

Technology in construction has been attempting to discover eco-friendly materials, to identify waste products that can be processed as an alternative supplement to cement. The study utilizes sugar cane bagasse ash (SCBA) to produce a bagasse ash blended concrete. There were 15 previous experimental studies that were gathered and analyzed which have the same variables in terms of determining the mechanical proeprties of the blended concrete with SCBA. The variables that were considered are: compressive strength, cement content (CC), fine aggregate (FA) and coarse aggregate (CA) content, water-cement ratio (W/C), water content (WC), and sugarcane bagasse ash content. 74 different data sets in all were obtained. The study's goal is to develop a prediction model for estimating the mechanical properties of concrete made with SCBA. The work employed MATLAB R2021a neural network (NN) toolbox for model development and simulation of the dataset with the use of the backpropagation ANN. The best model was observed to have a structure of 7-7-1 (input-hidden-output) having the highest R all value and lowest AIC value, with a mean absolute percentage error (MAPE) is 3.718% considered to be a highly accurate model. The relative importance (RI) showed that the FA, CA, and CC were the most significant factors to the CS while water and SCBA were the least influential parameters. The overall findings reveal that the MAPE of the compression strength prediction model decreased from 3.718% to 3.519% exhibiting a 5.35% improvement in the model’s performance.