Journal Article

Ann Prediction of Mechanical Properties of GGBFS and Alccofine Based High Strenth Self-Compacting Concrete

Tabassum, Nazia, Vijay, Manu, Akihla, CG, Bowaj, Abhishek J, Kabeer Ahmed, Shaik, Naveen Kumar, S M

2024

Overview

In this study, we use Artificial Neural Networks (ANN) and Multiple Regression Analysis to evaluate the prediction of two crucial self-compacting concrete properties: compressive strength and split tensile strength. It was possible to create four different datasets, each of which had different concrete mix proportions along with their respective ages in days, compressive strengths (MPa), and split tensile strengths (MPa). Separate ANN models and Regression models were trained and tested using these datasets. As a gauge of prediction accuracy, Mean Squared Error (MSE) was used to assess the performance of the models. This study offers insightful information on the application of multiple regression analysis and artificial neural networks to forecast the characteristics of self-compacting concrete using GGBS and Alccofine. Here Alccofine functions as an additive and GGBS acts as a partial substitute for cement at 0 to 60% with a fluctuation of 10%. The outcomes highlight the potential of neural networks as a tool for concrete mix design optimization and quality control since they can capture complex correlations between input variables and concrete strength.

• Publisher: IOP Publishing
• Subject: Artificial Neural Network (ANN) / Artificial neural networks / Complex variables / Compressive strength / Compressive strength (C.S) / Concrete / Concrete mixes / Concrete mixing / Concrete properties / Datasets / Design optimization / GGBS / Mean squared error (M.S.E) / Mechanical properties / Multiple regression analysis / Multiple Regression Analysis (MRA) / Neural networks / Performance assessment / Predictions / Quality control / Regression analysis / Regression models / Self Compacting Concrete (SCC) / Self-compacting concrete / Split tensile strength (S.T.S) / Tensile strength