Title:
Prepublished: AI Approach for Predicting Compressive Strength of Geopolymer Concrete
Author(s):
Muhammad Naveed, Asif Hameed , Ali Murtaza Rasool, Rashid Hameed, Danish Mukhtar
Publication:
Materials Journal
Volume:
Issue:
Appears on pages(s):
Keywords:
fly ash; green material; constituent ratios; ANN; GPC strength; mathematical modelling
DOI:
10.14359/51746714
Date:
3/24/2025
Abstract:
Geopolymer concrete (GPC) is a progressive material with the capability to significantly reduce global industrial waste. The combination of industrial by-products with alkaline solutions initiates an exothermic reaction, termed geopolymerization, resulting in a carbon-negative concrete that lessens environmental impact. The fly ash-based GPC (FA-based GPC) displays noticeable variability in its mechanical properties due to differences in mix design ratios and curing methods. To address this challenge, we optimized the constituent proportions of GPC through a meticulous selection of nine independent variables. A thorough experimental database of 1242 experimental observations was assembled from the available literature, and artificial neural networks (ANN) were employed for compressive strength modeling. The developed ANN model underwent rigorous evaluation using statistical metrics such as R-values, R2 values, and mean square error (MSE). The statistical analysis revealed an absence of a direct correlation between compressive strength and independent variables, as well as a lack of correlation among the independent variables. However, the predicted compressive strength by the developed ANN model aligns well with experimental observations from the compiled database, with R2 values for the training, validation, and testing datasets determined to be 0.84, 0.74, and 0.77, respectively. Sensitivity analysis identified curing temperature and silica-to-alumina ratio as the most crucial independent variables. Furthermore, the research introduced a novel method for deriving a mathematical expression from the trained model. The developed mathematical expressions accurately predict compressive strength, demonstrating minimal errors when using the tan-sigmoid activation function. Prediction errors were within the range of (-0.79 – 0.77) MPa, demonstrating high accuracy. These equations offer a practical alternative in engineering design, bypassing the intricacies of the internal processes within the ANN.