Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete

Abstract

The construction industry’s evolution towards sustainability necessitates the adoption of environmentally friendly materials and practices. Geopolymer concrete (GeC) stands out as a promising alternative to conventional concrete due to its reduced carbon footprint and potential for cost savings. This study explores the predictive capabilities of soft computing models in estimating the compressive strength of GeC, utilizing multi-layer perceptron (MLP) neural networks and hybrid systems incorporating the Gannet Optimization Algorithm (GOA) and Grey Wolf Optimizer (GWO). A dataset comprising 63 observations from a quarry mine in Malaysia is employed, with influential parameters normalized and utilized for model development. Consequently, we integrate optimization algorithms (GOA and GWO) with MLP to fine-tune the model’s parameters and improve prediction accuracy. The models are evaluated using R2, RMSE, and VAF. Various MLP architectures are explored, evaluating transfer functions and training techniques to optimize performance. In addition, hybrid models GOA–MLP and GWO–MLP are developed, with parameters fine-tuned to enhance predictive accuracy. During the training phase, the GWO–MLP model achieved an R2 of 0.981, RMSE of 0.962, and VAF of 97.44%, compared to MLP’s R2 of 0.95, RMSE of 0.918, and VAF of 94.59%. During the testing phase, GWO–MLP also showed the best performance with an R2 of 0.976, RMSE of 1.432, and VAF of 97.51%, outperforming both MLP and GOA–MLP. The GOA–MLP model demonstrated improved performance over MLP with an R2 of 0.963, RMSE of 0.811, and VAF of 95.78% in the training phase and R2 of 0.944, RMSE of 2.249, and VAF of 92.86% in the testing phase. Hence, the results show that the GWO–MLP model consistently outperforms both MLP and GOA–MLP models. Sensitivity analysis further elucidates the impact of key parameters on compressive strength, aiding in the optimization of GeC formulations for enhanced mechanical properties. Overall, the study underscores the efficacy of machine learning models in predicting GeC compressive strength, offering insights for sustainable construction practices.