Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures - Chen, Gongmei; Suhail, Salman Ali; Bahrami, Alireza; Sufian, Muhammad; Azab, Marc (Frontiers in Materials, 2023)
High-strength concrete (HSC) is vulnerable to strength loss when exposed to high temperatures or fire, risking the structural integrity of buildings and critical infrastructure. Predicting HSC compressive strength 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 HSC compressive strength is important as it can experience strength losses of up to 80% after exposure to temperatures ranging between 800-1000 °C. This study evaluates the efficacy of ML techniques such as Extreme Gradient Boosting, Random Forest (RF), and Adaptive Boosting for forecasting HSC compressive strength. The results of this study demonstrate that the RF model is the most efficient model for forecasting the compressive strength of HSC, exhibiting an R2 value of 0.98 and lower mean absolute error and root mean square error values than the other applied models. Furthermore, the SHapley Additive exPlanations (SHAP) analysis highlights temperature as the most significant factor influencing HSC compressive strength. This study provides valuable insights into the timely and effective determination of HSC compressive strength under high-temperature conditions, benefiting both the construction industry and academia. By leveraging ML techniques and considering the critical factors that influence HSC compressive strength, it is possible to optimize the design and construction process of HSC and enhance its resilience to high-temperature exposure.