Modeling the strength of geopolymer concrete at high temperatures: Machine learning approach

Analyzing the mechanical behavior of concrete structures under elevated temperatures is critical for ensuring fire safety, structural integrity, and damage detection. Geopolymer concrete (GPC), a sustainable alternative to Portland cement concrete, is known for its superior thermal resistance. However, accurately predicting its compressive strength after exposure to high temperatures, ranging from 25 oc to 1100 oc, remains a challenge due to the complex interactions of material properties under thermal stress. In this study, machine learning (ML) algorithms are employed to forecast the compressive strength of GPC using a comprehensive dataset of 332 experimental data points gathered from an extensive literature review. Six different ML models—Artificial Neural Networks (ANN), Support Vector Machines (SVR), Gradient Boosting (GBoost), Random Forest, XGBoost, and LightGBM—were trained and evaluated based on their performance. The results indicate that GBoost and LightGBM models outperformed others, delivering the most accurate predictions with the lowest errors. These findings highlight the effectiveness of ML models in predicting the residual compressive strength of GPC after high-temperature exposure, offering valuable insights for fire-resistant construction applications.