Comparative evaluation of machine learning and deep learning approaches for compressive strength prediction of geopolymer concrete.

Geopolymer concrete has emerged as a promising sustainable alternative to ordinary Portland cement concrete due to its lower environmental impact and favorable engineering performance. However, accurately predicting its compressive strength remains challenging because it is governed by complex interactions among binder composition, activator characteristics, curing conditions, and other mixture parameters. Therefore, reliable data-driven predictive models are needed to support efficient design and performance estimation. For this the study develops a data-driven framework for predicting the compressive strength of geopolymer concrete using both machine learning (ML) and deep learning (DL). A unified database of 594 geopolymer mix designs was compiled from published literature, covering key mix parameters (binder constituents, activator contents, aggregates, water/superplasticizer, fiber contents, and curing age). The dataset was cleaned and divided into 80% training and 20% testing for unbiased evaluation. Multiple supervised ML models (SVM, Decision Tree, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost) were trained and assessed using MAE, MSE, R