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用于评估层状水泥基复合材料失效疲劳寿命的双参数威布尔模型的数值验证——建模方法的对比评估

Numerical Validation of Two-Parameter Weibull Model for Assessing Failure Fatigue Lives of Laminated Cementitious Composites-Comparative Assessment of Modeling Approaches.

作者信息

Hanif Asad, Kim Yongjae, Park Cheolwoo

机构信息

Department of Civil Engineering, Mirpur University of Science and Technology (MUST), Allama Iqbal Road Mirpur AJ&K, Mirpur 10250, Pakistan.

Department of Civil Engineering, Kangwon National University, 346, Jungang-ro, Samcheok-si 25913, Gangwon-do, Korea.

出版信息

Materials (Basel). 2018 Dec 31;12(1):110. doi: 10.3390/ma12010110.

DOI:10.3390/ma12010110
PMID:30602671
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6337210/
Abstract

In this paper, comparative assessment of failure fatigue lives of thin laminated cementitious composites (LCCs) modeled by two modeling approaches-double-parameter Weibull distribution model and triple-parameter distribution model-was carried out. LCCs were fabricated of ordinary Portland cement (OPC), fly ash cenosphere (FAC), quartz sand, and reinforcing meshes and fibers. The failure fatigue life assessment at various probabilities by the two-parameter model was based on numerical calculations whereas the three-parameter model was applied by an open source program-ProFatigue. Respective parameters, shape and scale parameters in the two-parameter Weibull distribution model while shape, scale, and location parameters in three-parameter model were determined, and the corresponding probabilistic fatigue lives at various failure probabilities were calculated. It is concluded that the two-parameter model is more accurate in probabilistic fatigue life assessment of double-layer mesh-reinforced LCCs, whereas for single-layer reinforced LCCs, both models could be used at a fair confidence level.

摘要

本文采用双参数威布尔分布模型和三参数分布模型这两种建模方法,对薄型层状水泥基复合材料(LCC)的疲劳失效寿命进行了对比评估。LCC由普通硅酸盐水泥(OPC)、粉煤灰空心微珠(FAC)、石英砂以及增强网和纤维制成。双参数模型在不同概率下的失效疲劳寿命评估基于数值计算,而三参数模型则通过开源程序ProFatigue应用。确定了双参数威布尔分布模型中的形状和尺度参数以及三参数模型中的形状、尺度和位置参数,并计算了不同失效概率下相应的概率疲劳寿命。结果表明,双参数模型在双层网格增强LCC的概率疲劳寿命评估中更为准确,而对于单层增强LCC,两种模型在合理的置信水平下均可使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/f9710acf438c/materials-12-00110-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/7c9ef1f15f56/materials-12-00110-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/3e2ec89883bc/materials-12-00110-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/5053b9a00304/materials-12-00110-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/032d7f2135a3/materials-12-00110-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/f9710acf438c/materials-12-00110-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/7c9ef1f15f56/materials-12-00110-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/3e2ec89883bc/materials-12-00110-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/5053b9a00304/materials-12-00110-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/032d7f2135a3/materials-12-00110-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f5f/6337210/f9710acf438c/materials-12-00110-g005.jpg

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