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Fangyu Liu (1), Ke Xu (2,3), Wenqi Ding (4,5), Yafei Qiao (4,5), Linbing Wang (1)
Cement and Concrete Composites, 123, July 2021. DOI: 10.1016/j.cemconcomp.2021.104196
Hybrid fiber reinforced concrete; Machine learning; Microstructure; Mechanical properties; Steel fiber; PVA fiber
Hybrid fiber reinforced concrete (HFRC) has been proposed to improve the mechanical properties of concrete, especially the tensile behavior and ductility. To better understand the mechanisms of such enhancements, a series of macro-scale and micro-scale tests have been performed and combined to analyze the relationships between microstructural characteristics and mechanical properties of HFRC. The X-ray CT test and machine learning were used to reconstruct detailed microstructures of HFRC specimens. The morphological and statistical analyses were performed to observe the fiber distribution and pore structure. For the mechanical properties, a series of systematic mechanical tests, including the compressive test, the tensile test, and the four-point bending test, were carried out to identify the mechanics and toughness of HFRC. All results reveal that (i) increasing steel fibers could significantly increase the steel fiber density around the edge regions of the specimen, make a higher fraction of steel fibers distributed along the 90° of Phi angle, and result in higher fiber connectivity; (ii) adding more PVA fibers could reduce the fraction of steel fibers distributed along the 90° of Phi angle and significantly increase the porosity and pore connectivity of HFRC; (iii) these different effects on the microstructure result in different macro mechanical responses: steel fibers have a main and positive influence on the mechanical properties of HFRC, especially the strength indices, while introducing PVA fibers could improve the toughness of HFRC but the more addition of PVA fiber could reduce strength indices.
Dragonfly was used to visualize the 3D fiber orientation and connectivity of pore networks and to calculate 3D fiber orientation and pore connectivity.
(1) Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA.
(2) Department of Civil and Environmental Engineering, University of California, Berkeley, CA, 94720, USA.
(3) Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
(4) Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China.
(5) Key Laboratory of Geotechnical and Underground Engineering, Ministry of Education, Shanghai, 200092, China.
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