| 混杂纤维增强应变硬化水泥基复合材料的弯曲性能预测 |
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| 引用本文： | 张聪,夏超凡,袁振,李志华.混杂纤维增强应变硬化水泥基复合材料的弯曲性能预测[J].建筑材料学报,2021,24(1):14-21. |
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| 作者姓名： | 张聪 夏超凡 袁振 李志华 |
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| 作者单位： | 江南大学环境与土木工程学院,江苏无锡214000;武汉理工大学硅酸盐建筑材料国家重点实验室,湖北武汉430000;中国建筑材料科学研究学院绿色建筑材料国家重点实验室,北京100024;江南大学环境与土木工程学院,江苏无锡214000 |
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| 基金项目： | 绿色建筑材料国家重点实验室开放基金项目;硅酸盐建筑材料国家重点实验室开放基金项目;国家自然科学基金资助项目;江苏省自然科学基金资助项目 |
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| 摘 要： | 基于混凝土断裂力学与细观力学理论,同时考虑钢纤维(SF)/聚乙烯醇(PVA)混杂纤维对应变硬化水泥基复合材料(SHCC)弯曲性能的影响,提出了一种适用于SF/PVA纤维混杂SHCC(SF-PVA/SHCC)弯曲性能的预测方法.开展了SF-PVA/SHCC弯曲性能试验,分析了纤维种类和掺量对SHCC抗弯强度、极限弯曲挠度及弯曲荷载挠度曲线的影响.结果表明所提出的弯曲性能计算方法可以较好地预测SF-PVA/SHCC的抗弯强度和极限弯曲挠度.
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| 关 键 词： | 应变硬化水泥基复合材料 混杂 钢纤维 聚乙烯醇 弯曲性能 预测模型 |
| 收稿时间： | 2019/8/7 0:00:00 |
| 修稿时间： | 2019/11/1 0:00:00 |
Flexural Property Prediction for Hybrid Fiber Reinforced Strain Hardening Cementitious Composites |
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| ZHANG Cong,XIA Chaofan,YUAN Zhen,LI Zhihua.Flexural Property Prediction for Hybrid Fiber Reinforced Strain Hardening Cementitious Composites[J].Journal of Building Materials,2021,24(1):14-21. |
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| Authors: | ZHANG Cong XIA Chaofan YUAN Zhen LI Zhihua |
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| Affiliation: | School of Environment and Civil Engineering, Jiangnan University, Wuxi 214000, China |
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| Abstract: | Based on the theory of fracture mechanics and micromechanics of concrete, a flexural property prediction method for steel fiber PVA fiber reinforced strain hardening cementitious composite(SF PVA/SHCC) was developed by considering the effect of hybrid fibers on the flexural properties of strain hardening cementitious composite(SHCC). In order to verify the effectiveness of the proposed method, a series of flexural tests for SF PVA/SHCC were performed. The influence of fiber type and fiber content on the flexural strength, ultimate flexural deflection and load deflection curves of SHCC was studied. The results show that the proposed method can well predict the flexural strength and ultimate flexural deflection of SHCC containing steel fiber and PVA fiber. |
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| Keywords: | strain hardening cementitious composite(SHCC) hybrid steel fiber(SF) polyvinyl alcohol(PVA) flexural property prediction model |
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