Effects of initial damage on self-healing of fly ash-based engineered geopolymer composites (FA-EGC)

Guo, Xiaolu; Liu, Xinhao; Yuan, Shuting

doi:10.1016/j.jobe.2023.106901

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…However, the production of ECC typically requires 2 to 3 times more cement than traditional concrete [21]. Given that cement manufacturing is a process with high energy consumption and high carbon dioxide emissions [22], using non-clinker cements (such as geopolymers) to produce EGC represents a promising alternative [23][24][25][26][27][28]. Studies show that compared to the production of ECC, the production of fly ash-based EGC reduces energy consumption by about 60% and carbon dioxide emissions by about 80% [29,30].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of the Bond Performance at the Interface between Engineered Geopolymer Composites and Existing Concrete

Li,

Tan,

Ouyang

et al. 2024

Buildings

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Engineered geopolymer composite (EGC) exhibits ultra-high toughness, excellent crack control capability, and superior durability, making it highly promising for applications in bridge connecting slabs, wet joints of prefabricated components, and concrete structure reinforcement. However, the bond performance and failure mechanisms at the interface between EGC and existing concrete remain unclear. To elucidate the bond performance of EGC to existing concrete, direct shear tests were conducted on 15 sets of EGC–existing concrete bond specimens. This study explored the effects of existing concrete strength, interface roughness, and EGC strength on the bond performance and mechanisms. Additionally, a direct shear bond mechanical model was established to predict the interface bond strength. The results indicate that, with comparable compressive strength, the preparation of EGC can reduce the total carbon emissions by up to 127% compared to ECC. The failure mode of EGC-existing concrete bond specimens was mainly adhesive failure (except for specimen C30-III-G95), which can be categorized into serrated interfacial failure and alternating crack paths. The change in interface roughness was the primary factor leading to the transition between failure paths. The changes in interface roughness and EGC strength significantly influenced the bond performance. Under their combined effect, the interface bond strength of specimen C50-III-G95 increased by 345% compared to C50-I-G45. In contrast, the improvement in existing concrete strength had a relatively smaller effect on the increase in interface bond strength. Based on the experimental results and the bonding mechanism under direct shear stress, a direct shear bond mechanical model correlating existing concrete strength, interface roughness, and EGC strength was established. The model predictions showed good consistency with the experimental results. This study provides theoretical support and experimental data for the engineering application of EGC.

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Section: Introductionmentioning

confidence: 99%

Experimental Investigation of the Bond Performance at the Interface between Engineered Geopolymer Composites and Existing Concrete

Li,

Tan,

Ouyang

et al. 2024

Buildings

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A scientometric evaluation of self-healing cementitious composites for sustainable built environment applications

Salmi

Elhadi

Raza

et al. 2023

Journal of Building Engineering

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Durability prediction of geopolymer mortar reinforced with nanoparticles and PVA fiber using particle swarm optimized BP neural network

Zhang,

Yuan

et al. 2024

Nanotechnology Reviews

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In this study, polyvinyl alcohol (PVA) fibers and nanoparticles were incorporated to enhance the durability of geopolymer mortar (GM) with metakaolin (MK) and fly ash (FA). The dosage of nano-SiO2 (NS) was 0–2.5% and that of PVA fiber was 0–1.2%. The durability of GM includes resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion. Compared with the single BP neural network (BPNN) model, a particle swarm optimized BPNN (PSO-BPNN) model was utilized to predict the resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion of GMs with different dosages of nanoparticles and PVA fibers. In the model, the dosage of NS, PVA fiber, FA, and MK were used as input layers, and the durability parameters of electric flux, mass loss, and compressive strength loss of GMs were used as output layers. The result exhibits that the root mean square errors (RMSEs) of BPNN for resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion of GM mixed with nanoparticles and PVA fibers are 145.39, 6.43, and 2.19, whereas RMSEs obtained from PSO-BPNN are 76.33, 2.87, and 1.03, respectively. The NN optimized by particle swarm algorithm has better prediction accuracy. The PSO-BPNN can be utilized for estimating durability of GM reinforced by NS and PVA fiber, which can provide a guide for the proportion design of GM with PVA fiber and NS as well as for the engineering practice in the future.

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Effects of initial damage on self-healing of fly ash-based engineered geopolymer composites (FA-EGC)

Cited by 4 publications

References 42 publications

Experimental Investigation of the Bond Performance at the Interface between Engineered Geopolymer Composites and Existing Concrete

Experimental Investigation of the Bond Performance at the Interface between Engineered Geopolymer Composites and Existing Concrete

A scientometric evaluation of self-healing cementitious composites for sustainable built environment applications

Durability prediction of geopolymer mortar reinforced with nanoparticles and PVA fiber using particle swarm optimized BP neural network

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