Prediction of Mechanical and Tensile Properties of Self-Compacting Concrete Incorporating Fly Ash and Waste Copper Slag by Artificial Neural Network-ANN.

KanthCh, Lakshmi; Kumar, P. Ravindra; Chaitanya, Bypaneni Krishna; Kumar, N. Venkata Sairam; Thati, Naga Sai Rama Krishna; Kumar, N. Satya Vijay; Ravi, B.; Rao, Annamdasu Nagesawara

Self-compacting concrete (SCC) is a specialized form of concrete known for its exceptional workability, high paste content, and incorporation of cement substitutes like silica fume, natural pozzolana, and slag. These cement alternatives offer various advantages including cost reduction, decreased carbon dioxide emissions, reduced depletion of natural resources, and enhanced properties in both fresh and hardened states. SCC finds application in diverse scenarios such as structures with densely packed reinforcement and tall shear walls, necessitating accurate performance prediction. This study aims to develop artificial neural network (ANN) models for forecasting the compressive strength, split tensile strength, and flexural strength of self-compacting concrete incorporating fly ash and waste copper slag, assessed at curing periods of 7, 28, 56, and 90 days. The ANN model comprises several input and output parameters, with model accuracy evaluated using Mean Squared Error (MSE) and R-squared (R2) metrics. Furthermore, the network's performance is evaluated through error histograms and regression network predictions using ANN. The Levenberg-Marquardt optimization method, implemented in MATLAB 2020a, is employed to effectively estimate the compression strength, split tensile strength, and flexural strength of self-compacting concrete, ensuring reliable results.

ARTIFICIAL neural networks; COPPER slag; CONCRETE waste; CARBON emissions; FLY ash; SELF-consolidating concrete

Annales de Chimie Science des Matériaux, 2024, Vol 48, Issue 5, p655

0151-9107

Academic Journal

10.18280/acsm.480506