Static Mechanical Properties and Microscopic Analysis of Hybrid Fiber Reinforced Ultra-High Performance Concrete with Coarse Aggregate
Author(s): |
Shengbing Liu
Yonglei Zhang |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Advances in Civil Engineering, January 2022, v. 2022 |
Page(s): | 1-13 |
DOI: | 10.1155/2022/4529993 |
Abstract: |
An orthogonal test was conducted to investigate the effect of hybrid fiber on the mechanical properties of ultra-high performance concrete (UHPC-CA) with coarse aggregate . It was used to design and manufacture 144 test specimens for compressive strength, flexural tensile strength, static compressive elastic modulus test, and SEM microscopic test. Considering the mass replacement rate of coarse aggregate (10%, 15%, 20%, and 30%), the steel fiber volume rate (0%, 0.5%, 1.0%, and 1.5%), and the polypropylene fiber volume rate (0%, 0.05%, 0.10%, and 0.15%). The results show that the volume fraction of steel fiber has the greatest impact on compressive strength and flexural tensile strength, followed by the mass substitution rate of coarse aggregate, and the volume fraction of polypropylene fiber has the smallest impact. For the elastic modulus under static compression, the mass substitution rate of coarse aggregate has the greatest impact, followed by the volume fraction of steel fiber and polypropylene fiber. Based on the analysis of compressive properties, flexural tensile properties, and elastic modulus, the optimal mix ratio is recommended as follows: coarse aggregate 15%, steel fiber 1.5%, and polypropylene fiber 0.10%. Finally, three kinds of strength parameters are predicted based on the back propagation (BP) neural network system. The absolute value of the relative error between the predicted strength and the experimental value is less than 5%, which indicates that the prediction model proposed in this paper can provide a reference for the multiobjective optimization of the mix proportion of hybrid fiber ultra-high performance concrete. |
Copyright: | © Shengbing Liu and Yonglei Zhang et al. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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10698200 - Published on:
11/12/2022 - Last updated on:
15/02/2023