Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures

Abstract

High-strength concrete (HSC) is vulnerable to strength loss when exposed to high temperatures or fire, risking the structural integrity of buildings and critical infrastructures. Predicting the compressive strength of HSC under high-temperature conditions is crucial for safety. Machine learning (ML) techniques have emerged as a powerful tool for predicting concrete properties. Accurate prediction of the compressive strength of HSC is important as HSC can experience strength losses of up to 80% after exposure to temperatures of 800°C–1000°C. This study evaluates the efficacy of ML techniques such as Extreme Gradient Boosting, Random Forest (RF), and Adaptive Boosting for predicting the compressive strength of HSC. The results of this study demonstrate that the RF model is the most efficient for predicting the compressive strength of HSC, exhibiting the R2 value of 0.98 and lower mean absolute error and root mean square error values than the other applied models. Furthermore, Shapley Additive Explanations analysis highlights temperature as the most significant factor influencing the compressive strength of HSC. This article provides valuable insights into the timely and effective determination of the compressive strength of HSC under high-temperature conditions, benefiting both the construction industry and academia. By leveraging ML techniques and considering the critical factors that influence the compressive strength of HSC, it is possible to optimize the design and construction process of HSC and enhance its resilience to high-temperature exposure.