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Improvement in Predicting the Post-Cracking Tensile Behavior of Ultra-High Performance Cementitious Composites Based on Fiber Orientation Distribution
In this paper, the post-cracking tensile behavior of Ultra-High Performance Cementitious Composites (UHPCC) was studied and an improved analytical model to predict the behavior depending on the fiber orientation distribution was proposed. Two different casting methods were adopted to estimate the in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5456628/ https://www.ncbi.nlm.nih.gov/pubmed/28773949 http://dx.doi.org/10.3390/ma9100829 |
Sumario: | In this paper, the post-cracking tensile behavior of Ultra-High Performance Cementitious Composites (UHPCC) was studied and an improved analytical model to predict the behavior depending on the fiber orientation distribution was proposed. Two different casting methods were adopted to estimate the influence of the casting method on the tensile behavior. The direct tensile test results showed that the post-cracking tensile behavior was considerably dependent on the casting method. The influence of the casting method was quantified by image analysis of the fiber distribution. The fiber orientation distribution obtained by image analysis may sometimes include considerable error according to the image resolution, which may cause inaccuracy when predicting the post-cracking tensile behavior based on the fiber orientation distribution. To overcome this dependency, the tensile bridging behavior by the fibers in UHPCC was simulated considering the obtained fiber orientation distribution as well as the number of fibers detected. The post-cracking behavior was then simulated by combining the bridging behavior and tension softening behavior of the matrix. The approach adopted in this study to simulate the post-cracking behavior of UHPCC showed good agreement with the experimental results. |
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