Prediction of ultimate bearing capacity of RC beams after fire based on data-driven method

The ultimate bearing capacity of reinforced concrete beams after exposure to fire is investigated in this study through the utilization of numerical simulation, regression fitting, and machine learning techniques to examine the thermal and mechanical properties of such beams under high temperatures. In this study, a series of fire and static load tests are conducted on seven reinforced concrete T-beams. Based on the experimental observations and considering the effects of high-temperature concrete spalling and steel/concrete bond degradation, a numerical model is developed to simulate the temperature distribution and structural behavior of reinforced concrete T-beams. The accuracy of the numerical model is validated by comparing the cross-sectional temperature profiles and ultimate bearing capacities after fire exposure with experimental results. A dataset comprising 500 samples is established, with variables including fire exposure time, depth of concrete spalling, spalling area ratio, and loading conditions. Regression fitting and machine learning techniques were employed to establish predictive formulas and models for estimating the ultimate bearing capacity of reinforced concrete T-beams after fire exposure. The accuracy of both methods is found to be within 10%.