Assessment of interfacial fracture energy between concrete and CFRP under water immersion conditions

He, Jun; Shi, Jiajun; Lu, Zhongyu

doi:10.1016/j.conbuildmat.2021.122942

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…The good bonding performance can be attributed to the large number of hydroxyl bonds formed on the fiber surfaces. During such a hydration process, a large amount of the hydration product continuously accumulated on the surfaces of the basalt fibers, which led to a strong adhesive force between the basalt fibers and the pea gravel concrete by forming shear keys at the interfaces [41,42]. As shown in Figure 13b, due to this good bonding performance, the short-cut basalt fibers across the macroscopic cracks suffered tensile fractures with the crushing of the BFRPGC block.…”

Section: Results Of Scanning Electron Microscopymentioning

confidence: 99%

Parametric Study on Mechanical Properties of Basalt Fiber-Reinforced Pea Gravel Concrete

Li,

Wang,

Zhang

et al. 2024

Buildings

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Basalt fiber-reinforced pea gravel concrete (BFRPGC) has remarkable potential for use as the retrofitting covers for masonry walls. However, a quantitative understanding of the mechanical properties of the BFRPGC material is still a perceived gap in the current literature. In this study, the role of basalt fibers in pea gravel concrete was evaluated by a comprehensive experimental investigation involving compressive strength tests and splitting tensile tests. Fiber length and volume fraction were selected as the key parameters. Two fiber lengths of 6 mm and 12 mm were considered, while the volume fraction corresponding to each of the fiber lengths was increased from 0.3% to 0.8%, with a step of 0.1%. The measured strengths were not simply proportional to the fiber volume fraction. The reason behind this phenomenon, i.e., the coupling effect of the bridging role of basalt fibers on concrete microcracks and the fiber agglomeration in concrete, was analyzed. The best performance of the BFRPGC material was achieved by incorporating 12-millimeter-length fibers with a volume fraction of 0.4%. Compared to that of the reference pea gravel concrete, a significant increase of up to 44.5% in compressive strength was recorded in this case. Furthermore, the failure mechanism of basalt fibers in pea gravel concrete was disclosed via the scanning electron microscope observations. In addition, the uniaxial compressive stress–strain model of the BFRPGC material was established.

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Section: Results Of Scanning Electron Microscopymentioning

confidence: 99%

Parametric Study on Mechanical Properties of Basalt Fiber-Reinforced Pea Gravel Concrete

Li,

Wang,

Zhang

et al. 2024

Buildings

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“…Building upon this foundation, other researchers have introduced an innovative and interpretable machine learning technique to predict the interfacial bond strength of FRP concrete by integrating machine learning models with traditional physical models [20]. However, the majority of research, whether using experimental methods or machine learning methods, predominantly concentrates on assessing the durability of FRP-to-concrete interfaces with respect to a singular environmental factor [21,22]. There is a scarcity of research considering the coupling effect of environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Bond Strength Evaluation of FRP–Concrete Interfaces Affected by Hygrothermal and Salt Attack Using Improved Meta-Learning Neural Network

Wang,

Ye,

Liu

et al. 2024

Applied Sciences

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Fiber-reinforced polymer (FRP) laminates are popular in the strengthening of concrete structures, but the durability of the strengthened structures is of great concern. Due to the susceptibility of the epoxy resin used for bonding and the deterioration of materials, the bond performance of the FRP–concrete interface could be degraded due to environmental exposure. This paper aimed to establish a data-driven method for bond strength prediction using existing test results. Therefore, a method composed of a Back Prorogation Net (BPNN) and Meta-learning Net was proposed, which can be used to solve the implicit regression problems in few-shot learning and can obtain the deteriorated bond strength and the impact weight of each parameter. First, the pretraining database Meta1, a database of material strength degradation, was established from the existing results and used in the meta-learning network. Then, the database Meta2 was built and used in the meta-learning network for model fine-tuning. Finally, combining all prior knowledge, not only the degradation of the FRP–concrete bond’s strength was predicted, but the respective weights of the environment parameters were also obtained. This method can accurately predict the degradation of the bond performance of FRP–concrete interfaces in complex environments, thus facilitating the further assessment of the remaining service life of FRP-reinforced structures.

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Experimental and numerical analysis of CFRP-concrete joint bond behavior after exposure to wet-dry cycles

Al‐Lami

Colombi

D’Antino

2023

Composite Structures

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Assessment of interfacial fracture energy between concrete and CFRP under water immersion conditions

Cited by 9 publications

References 30 publications

Parametric Study on Mechanical Properties of Basalt Fiber-Reinforced Pea Gravel Concrete

Parametric Study on Mechanical Properties of Basalt Fiber-Reinforced Pea Gravel Concrete

Bond Strength Evaluation of FRP–Concrete Interfaces Affected by Hygrothermal and Salt Attack Using Improved Meta-Learning Neural Network

Experimental and numerical analysis of CFRP-concrete joint bond behavior after exposure to wet-dry cycles

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