Prediction of High-temperature Explosion Spalling in Ultra-High-Performance Concrete Based on Intervention Analysis of Hybrid Fiber Performance
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The high-temperature explosion and peeling of concrete have brought immeasurable negative impacts to both society andhumanity. However, the current research on the high-temperature explosion spalling mechanism of Ultra-High-PerformanceConcrete (UHPC) is relatively shallow, and traditional prediction methods are difficult to achieve effective prediction ofUHPC. Therefore, this study proposes a prediction model for high-temperature explosion spalling of UHPC based onthe 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% incompressive strength. The performance intervention test of mixed fibers showed that the explosion probability of singledopedpolypropylene fibers increased by 100% compared to mixed fibers. When the compressive strength was between 100and 120 MPa, steel fiber 60 kg/m3 and PP fiber 2 kg/m3 were added, and the high-temperature blast resistance performanceof concrete specimens was the best. The results indicate that the proposed high-temperature guarantee peeling predictionmodel has ideal predictive performance, both in terms of concrete mix proportion and compressive strength. The hybridpolypropylene fibers and steel fibers have a positive effect on the high-temperature explosion resistance of concrete, and thesize of concrete is inversely proportional to the probability of explosion spalling.
Abstract
The high-temperature explosion and peeling of concrete have brought immeasurable negative impacts to both society andhumanity. However, the current research on the high-temperature explosion spalling mechanism of Ultra-High-PerformanceConcrete (UHPC) is relatively shallow, and traditional prediction methods are difficult to achieve effective prediction ofUHPC. Therefore, this study proposes a prediction model for high-temperature explosion spalling of UHPC based onthe 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% incompressive strength. The performance intervention test of mixed fibers showed that the explosion probability of singledopedpolypropylene fibers increased by 100% compared to mixed fibers. When the compressive strength was between 100and 120 MPa, steel fiber 60 kg/m3 and PP fiber 2 kg/m3 were added, and the high-temperature blast resistance performanceof concrete specimens was the best. The results indicate that the proposed high-temperature guarantee peeling predictionmodel has ideal predictive performance, both in terms of concrete mix proportion and compressive strength. The hybridpolypropylene fibers and steel fibers have a positive effect on the high-temperature explosion resistance of concrete, and thesize of concrete is inversely proportional to the probability of explosion spalling.