Failure Mechanism Analysis of Reactive Powder Concrete Under Diverse Loading Conditions Based on Acoustic Emission and IVY-Optimized Machine Learning

Reactive Powder Concrete (RPC) exhibits mechanical failure behaviors distinct from those of ordinary concrete. To investigate the mechanical properties and damage evolution characteristics of RPC during failure, uniaxial compression, axial compression, splitting tensile, and four-point bending tests were performed on RPC specimens integrated with Acoustic Emission (AE) technology. Subsequently, damage stage identification models were established using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms coupled with AE parameters—including ringing count (RC), energy, peak frequency, RA, and AF—and were optimized via the Ivy algorithm (IVY). Results indicate that RPC demonstrated the highest ductility and resistance to failure under four-point bending, compared to its weakest performance under axial compression. By integrating the evolution of AE ringing counts and energy, the damage process was divided into three stages: compaction-elastic, crack propagation, and failure. Under axial compression, AE activity peaked before reaching the peak stress, whereas splitting tension exhibited concentrated signal bursts during crack propagation, and bending failure was characterized by a sustained signal escalation. The proportion of high-frequency signals was highest in cubic compression specimens, while splitting tension was dominated by low-frequency signals. The RA-AF distribution revealed that steel fibers inhibited through-thickness tensile cracks, and a decrease in the b-value served as a precursor to unstable failure. Notably, the IVY-optimized XGBoost model achieved the best performance, with an accuracy improvement of 26%. Under compressive stress, AF was identified as the primary parameter, whereas peak frequency became critical under tensile-bending conditions, reflecting the distinct damage mechanisms associated with different loading modes. These findings provide a scientific basis for damage assessment and early warning strategies in RPC structures.