Post-Exposure Behavior of Air-Entrained Concrete Under High-Temperature and Cooling Conditions: Microstructure and ANN Prediction

Fire remains one of the most damaging exposures a concrete structure can face, yet how entrained air interacts with that damage is still not fully mapped out. This work looks at that gap directly, testing concretes with nominal total fresh-concrete air contents of approximately 2% (control), 4% (AE-4), and 6% (AE-6) after exposure to temperatures between 23 °C and 700 °C, followed by either air or water cooling. Six properties were measured: dry unit weight, thermal conductivity, compressive strength, flexural strength, ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DEM), and each was then modeled with a dedicated feed-forward artificial neural network (ANN) using only AE content and temperature as inputs. The compressive strength of the control mix fell from 65.30 MPa at ambient temperature to 8.57 MPa at 700 °C, an 87% loss, with comparably steep reductions recorded for the other properties. At every temperature tested, water-cooled control specimens retained less strength and stiffness than their air-cooled counterparts. The ANN models, trained with the Levenberg–Marquardt algorithm, reproduced the experimental trends closely, returning coefficients of determination between 0.9364 and 0.9735. Sensitivity analysis placed temperature well ahead of AE content as the dominant driver of property change in every model (sensitivity ratio of 2.36–7.25 versus 1.14–1.87), although AE content was never negligible. The models provide accurate predictions within the investigated experimental ranges and may support preliminary assessment of comparable air-entrained concrete systems. Scanning electron microscopy tied these numbers to what was actually happening inside the material: the C–S–H structure held together reasonably well up to about 500 °C, then broke down visibly by 700 °C, with the AE’s air voids appearing to interrupt crack growth along the way. Together, the results offer both a practical dataset and a set of ready-to-use ANN tools for assessing the residual condition of air-entrained concrete after fire.