摘要
为研究纤维网格增强混凝土(textile reinforced concrete, TRC)板的双向弯曲性能,对12块四边简支TRC板进行了单点静载试验,分别研究纤维网格层数及种类、聚乙烯醇(polyvinyl alcohol, PVA)纤维体积掺率和纤维混掺对TRC板双向弯曲性能的影响.结果表明:纤维网格层数的增加能够有效提高TRC板的承载能力和耗能能力,改善其弯曲变形性能,且相比于玄武岩纤维网格(basalt fiber net, BFN),耐碱玻璃纤维网格(alkali-resistant glass fiber net, GFN)的效果更佳;与PVA纤维体积掺率为0.5%的玄武岩纤维增强混凝土(basalt textile reinforced concrete, BTRC)板相比,PVA纤维体积掺率在达到1.5%时,能够有效改善TRC板内力重分布过程,提高其极限承载力,同时裂后刚度分别提高20.8%和25.5%,且能量吸收值是体积掺率为0.5%时的1.37倍;1.0%钢纤维与0.5%PVA纤维混掺在提高BTRC板承载力方面表现出正混杂效应,极限荷载相比于仅掺2种短纤维的BTRC板分别提高9%和12%;无论是BFN还是GFN,随着配网率的增加,其纤维有效利用率都呈递减趋势.
To study the biaxial flexural behaviors of textile reinforced concrete(TRC) slab,a monotonic static load test was carried out on 12 TRC square slabs with four-sided simple support.The effect of textile layers and types,PVA fiber content,and hybrid fiber on the biaxial flexural behaviors of TRC slab were studied.Results show that the increase of textile layers can effectively increase the load-bearing capacity and energy dissipation capacity of TRC slab,and improve its bending deformation ability.Compared with BFN,the effect of GFN is better.Compared with the BTRC slab with 0.5% PVA,when the PVA fiber content reaches 1.5%,its full distribution process of internal force can effectively improve,thus increasing its ultimate bearing capacity,and the post cracking stiffness is increased by 20.8% and 25.5%,respectively.Moreover,the energy absorption value is 1.37 times that when the fiber content is 0.5%;a hybrid of 1.0% steel fiber and 0.5% PVA fiber shows a positive hybrid effect in improving the bearing capacity of BTRC slab.Compared with BTRC slabs adding single short fibers,the ultimate load is increased by 9% and 12%,respectively.Whether it is BFN or GFN,as the distribution network rate increases,its fiber effective utilization rate shows a decreasing trend.
作者
邓宗才
龚明高
DENG Zongcai;GONG Minggao(Key Laboratory of Urban Security and Disaster Engineering,Ministry of Education,Beijing University of Technology,Beijing 100124,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2022年第4期367-377,共11页
Journal of Beijing University of Technology
基金
国家重点研发计划资助项目(2016YFB0303200)。