Prediction of geopolymer concrete compressive strength using artificial neural network and genetic algorithm
Year: 2025
Type: article
Abstract: The environmental concerns associated with Portland cement production have led to the development of geopolymer concrete as a sustainable alternative with reduced carbon emissions. In this study, an advanced hybrid predictive model based on Artificial Neural Networks (ANNs) combined with three optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Levenberg-Marquardt (LM) algorithm—was developed to estimate the compressive strength of geopolymer concrete (GPC). A dataset of 162 mix designs, obtained from experimental studies, was used for model training and validation. The performance of the proposed models was evaluated based on statistical accuracy metrics, demonstrating that the ANN-LM model outperformed the other two in prediction precision. The findings highlight the effectiveness of AI-based models in optimizing mix design parameters and predicting mechanical properties of sustainable concrete materials. The proposed approach can contribute to reducing experimental costs and accelerating the adoption of eco-friendly construction materials.
Source: Results in Engineering
Authors: Hossein Khosravi, Mohammad Bahram
Institution: Hakim Sabzevari University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
Funder: Hakim Sabzevari University
OpenAlex ID: W4410870895