基于多模态无损评价的钢纤维增强高性能混凝土增韧机理定量分析

D. Loshkov, Y. Peng, R. Kravchuk, E. Landis
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摘要

为了更好地了解中观尺度模型和更合理的“设计材料”概念,我们试图隔离和测量导致钢纤维增强超高性能混凝土(UHPC)高强度和高延性的不同机制。本文描述的工作联合应用定量x射线计算机断层扫描(CT)和声发射(AE)技术来监测和测量UHPC劈裂圆柱体试验中的损伤进展。对两种不同纤维类型的直径为50mm的试样在载荷试验前后进行CT扫描。从产生的图像,纤维排列评估,以量化其对试样性能的影响。结果表明纤维排列的重要性,最好的情况下比最坏的情况高20%到30%。累积声发射能量也有相应的影响。测试后的CT扫描用于测量由于基体开裂和纤维拔出引起的内部能量耗散,并对每一种进行校准测量。利用人工神经网络处理声发射数据,对能量耗散进行分类。CT分析表明,纤维拔出是主要的耗能机制,但测得的内耗能之和仅占荷载净功测得的试件耗散总能量的60%。声发射分析表明,能量耗散分布更为均匀。声发射数据还显示了耗散机制如何随着损伤的累积而变化。
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A quantitative analysis of toughening mechanisms in steel fibre reinforced ultra-high-performance concrete through multimodal nondestructive evaluation
For the joint purposes of better informed meso-scale models and for more rational for “materials by design” concepts, we seek to isolate and measure the different mechanisms that lead to high strength and high ductility of steel fiber reinforced ultra-high-performance concrete (UHPC). The work described here jointly applies quantitative x-ray computed tomography(CT) and acoustic emission (AE) techniques to monitor and measure damage progression in split cylinder tests of UHPC. 50-mm diameter specimens of two different fiber types were CT scanned both before and after load testing. From the resulting images, fiber alignment was evaluated to quantify its effect on specimen performance. Results demonstrate the significance of fiber alignment, with best case being between 20 and 30% higher than the worst case. Cumulative AE energy was also affected commensurately. Post-test CT scans of the specimen were used to measure internal energy dissipation due to both matrix cracking and fiber pullout using calibration measurements for each. AE data, processed using an artificial neural network, was also used to classify energy dissipation. CT analysis showed that fiber pullout was the dominant energy dissipation mechanism, however, the sum of internal energy dissipation measured amounted to only 60% of the total energy dissipated by the specimens as measured by the net work of load. AE analysis showed a more balanced distribution of energy dissipation. AE data additionally showed how the dissipation mechanisms shift as damage accumulates.
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