Evaluation of geopolymer concrete at high temperatures: An experimental study using machine learning

Studying the mechanical performance of concrete after being exposed to high temperatures is an important step in the damage assessment of buildings and fire safety applications. However, predicting the compressive strength of GPC accurately after exposure to high temperatures is a challenging task. In this paper, artificial neural network (ANN) and support vector regression (SVR) models were developed to predict the compressive strength of geopolymer concrete (GPC) at high temperatures ranging from 100 degrees C to 1000 degrees C. A series of experiments consisting of different mix designs were conducted at elevated temperatures to prepare a dataset. Besides ex-periments' results, the data of previously published studies were also collected and used. Monte Carlo simula-tions were performed to study the effect of inputs' uncertainties on models' outputs and generate probability curves. Feature importance analysis was performed to investigate the significance of inputs on the residual compressive strength of GPC. Results indicate that SVR outperformed ANN. Sodium silicate and curing time were observed as the most influential controlling factors affecting the residual compressive strength of GPC at high temperatures. In contrast, fly ash exhibited the slightest effect on the residual compressive strength of GPC, followed by sodium hydroxide. The overall results demonstrate that the machine learning (ML) approaches can effectively improve the monitoring of GPC after exposure to high temperatures.

Automated summary

Studying the mechanical performance of concrete after exposure to high temperatures is crucial for the damage assessment and fire safety applications in buildings. However, accurately predicting the compressive strength of geopolymer concrete (GPC) at high temperatures is a challenging task. In this study, artificial neural network (ANN) and support vector regression (SVR) models were developed and compared to predict the compressive strength of GPC at temperatures ranging from 100°C to 1000°C. Results show that SVR outperformed ANN, and sodium silicate and curing time were identified as the most influential factors on the residual compressive strength of GPC at high temperatures. The findings demonstrate that machine learning approaches can effectively enhance the monitoring of GPC after exposure to high temperatures.