Influence of high temperatures on the bond between carbon Fibre-Reinforced polymer bars and concrete

López, Fernando José Cos-Gayón; Marco, Javier Benlloch; Rodríguez, Víctor Calvet

doi:10.1016/j.conbuildmat.2021.124967

Cited by 5 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, RCS performance progressively worsens and reaches unacceptable minimums [15] (Figure 1). To fight this, and to minimise corrosion damage in RCS, especially those located in marine environments, some authors have proposed using corrosion-resistant reinforcements like stainless steel [17][18][19][20] or fibre-reinforced polymer bars [21], such as those made of carbon fibre [22,23] or glass fibre [24,25]. Nonetheless, carbon steel is irreplaceable due to the practical advantages of low cost, easy field processing, versatility and mechanical performance [13,26].…”

Section: Or Peer Reviewmentioning

confidence: 99%

“…Many works have focused on analysing the mechanical properties of HPC, such as those that focus on compressive strength and fracture energy [30], strength and modulus of elasticity [31], abrasion resistance [32], split tensile strength [33] or pulse velocity and Poisson's ratio [34]. In contrast, works that have analysed aspects related to the durability and corrosion perfor- To fight this, and to minimise corrosion damage in RCS, especially those located in marine environments, some authors have proposed using corrosion-resistant reinforcements like stainless steel [17][18][19][20] or fibre-reinforced polymer bars [21], such as those made of carbon fibre [22,23] or glass fibre [24,25]. Nonetheless, carbon steel is irreplaceable due to the practical advantages of low cost, easy field processing, versatility and mechanical performance [13,26].…”

Section: Or Peer Reviewmentioning

confidence: 99%

See 1 more Smart Citation

OC, HPC, UHPC and UHPFRC Corrosion Performance in the Marine Environment

Lliso-Ferrando,

Gandía-Romero,

Soto

et al. 2023

Buildings

View full text Add to dashboard Cite

This work aims to study the corrosion performance of six concretes in the marine environment: three ordinary concretes (C30, C40 and C50); one high-performance concrete (C90); two ultra high-performance concretes, one without fibres (C150-NF) and another one with steel fibres (C150-F). To this end, porosity and chloride ingress resistance were analysed at different ages. Resistivity was also evaluated and the corrosion rate in the embedded rebars was monitored. The results showed that C30, C40 and C50 had porosity accessible to water percentages and capillary absorption values between six- and eight-fold higher than C90 and C150-NF and C150-F, respectively. Similar differences were obtained when oxygen permeability was analysed. Chloride ingress resistance in the ordinary concretes was estimated to be one-fold lower than in C90 and two-fold lower than in C150-NF and C150-F. Presence of fibres in C150-F increased the diffusion coefficient between 5% and 50% compared to C150-NF. Fibres also affected resistivity: C150-NF had values above 5500 Ωm, but the C150-F and C90 values were between 700 and 1000 Ωm and were one-fold higher than the ordinary concretes. After 3 years, the corrosion damage in the embedded rebars exposed to a marine environment was negligible in C90, C150-NF and C150-F (9.5, 6.2 and 3.5 mg mass loss), but with higher values (between 170.4 and 328.9 mg) for C3, C40 and C50. The results allow a framework to be established to make comparisons in future studies.

show abstract

Section: Or Peer Reviewmentioning

confidence: 99%

Section: Or Peer Reviewmentioning

confidence: 99%

OC, HPC, UHPC and UHPFRC Corrosion Performance in the Marine Environment

Lliso-Ferrando,

Gandía-Romero,

Soto

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…The mixture must also be optimised for the specific technology used in the construction process, considering elements such as workability and setting speed. Environmental conditions, such as temperature [7,8], precipitation, distance away from the construction area, as well as the volume of traffic, must also be considered when designing the concrete mix. The final composition of the mix is determined by construction specifications, such as the desired compressive strength or resistance to environmental elements such as chloride ingression and, increasingly, ecological considerations, such as low emissivity.…”

Section: Introductionmentioning

confidence: 99%

Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design

Ziolkowski

2023

Materials

View full text Add to dashboard Cite

The design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering and difficulty predicting concrete properties. Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive strength of concrete. This study evaluates five deep neural network models of varying computational complexity in three series. Each model is trained and tested in three series with a vast database of concrete mix recipes and associated destructive tests. The findings suggest a positive correlation between increased computational complexity and the model’s predictive ability. This correlation is evidenced by an increment in the coefficient of determination (R2) and a decrease in error metrics (mean squared error, Minkowski error, normalized squared error, root mean squared error, and sum squared error) as the complexity of the model increases. The research findings provide valuable insights for increasing the performance of concrete technical feature prediction models while acknowledging this study’s limitations and suggesting potential future research directions. This research paves the way for further refinement of AI-driven methods in concrete mix design, enhancing the efficiency and precision of the concrete mix design process.

show abstract