The high-temperature explosion and peeling of concrete have brought immeasurable negative impacts to both society and humanity. However, the current research on the high-temperature explosion spalling mechanism of Ultra-High-Performance Concrete (UHPC) is relatively shallow, and traditional prediction methods are difficult to achieve effective prediction of UHPC. Therefore, this study proposes a prediction model for high-temperature explosion spalling of UHPC based on the Artificial Neural Network algorithm (ANN) and conducts Hybrid Fiber (HF) performance intervention experiments. Verification showed that the accuracy of the prediction model was 95.95% based on the concrete mix ratio and 87.49% in compressive strength. The performance intervention test of mixed fibers showed that the explosion probability of single-doped polypropylene fibers increased by 100% compared to mixed fibers. When the compressive strength was between 100 and 120 MPa, steel fiber 60 kg/m³ and PP fiber 2 kg/m³ were added, and the high-temperature blast resistance performance of concrete specimens was the best. The results indicate that the proposed high-temperature guarantee peeling prediction model has ideal predictive performance, both in terms of concrete mix proportion and compressive strength. The hybrid polypropylene fibers and steel fibers have a positive effect on the high-temperature explosion resistance of concrete, and the size of concrete is inversely proportional to the probability of explosion spalling.

Keywords: Ultra-High-Performance Concrete, High-temperature explosion spalling, ANN, Polypropylene fiber and steel fiber, Compressive strength, Prefabricated buildings.