Evidence map›Paper›PMID 39065373›Full record

ArticlePolymers2024

Exploring Damage Patterns in CFRP Reinforcements: Insights from Simulation and Experimentation.

Youssef Bounjoum, Oumayma Hamlaoui, Mohamed Karim Hajji, Khalil Essaadaoui, Jalal Chafiq, Mohmmed Ait El Fqih

Abstract read
In one paragraph

Article in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Youssef BounjoumLaboratory of Artificial Intelligence & Complex Systems Engineering, ENSAM, Hassan II University of Casablanca, Casablanca 20670, Morocco.
Oumayma HamlaouiCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.ORCID 0000-0001-5278-6285
Mohamed Karim HajjiCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.ORCID 0009-0007-1735-3522
Khalil EssaadaouiLaboratory of Artificial Intelligence & Complex Systems Engineering, ENSAM, Hassan II University of Casablanca, Casablanca 20670, Morocco.
Jalal ChafiqLaboratory of Artificial Intelligence & Complex Systems Engineering, ENSAM, Hassan II University of Casablanca, Casablanca 20670, Morocco.ORCID 0000-0003-2624-5594
Mohmmed Ait El FqihLaboratory of Artificial Intelligence & Complex Systems Engineering, ENSAM, Hassan II University of Casablanca, Casablanca 20670, Morocco.ORCID 0000-0002-7956-5172

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Carbon Fiber Reinforced Polymers (CFRP) have become increasingly significant in real-world applications due to their superior strength-to-weight ratio, corrosion resistance, and high stiffness. These properties make CFRP an ideal material for reinforcing concrete structures, particularly in scenarios where weight reduction is crucial, such as in bridges and high-rise buildings. The transformative potential of CFRP lies in its ability to enhance the durability and load-bearing capacity of concrete structures while minimizing maintenance costs and extending the lifespan of the infrastructure. This research explores the impact of reinforcing structural elements with advanced composite materials on the strength and durability of concrete and reinforced concrete structures. By integrating Carbon Fiber Reinforced Polymer (CFRP) reinforcements, we subjected both rectangular and T-section concrete beams to comprehensive three-point bending tests, revealing a substantial increase in flexural strength by 45% and crack resistance due to CFRP reinforcement. The study revealed that CFRP reinforcement increased the flexural strength of concrete beams by 45% and improved crack resistance significantly. Additionally, the load-bearing capacity of the beams was enhanced by 40% compared to unreinforced specimens. These improvements were validated through finite element simulations, which showed a close alignment with the experimental data. Furthermore, an innovative simulation study was conducted using a finely tuned finite element numerical model within the Abaqus calculation code. This model accurately replicated the laboratory specimens in terms of shape, dimensions, and loading conditions. The simulation results not only validated the experimental observations but also provided deeper insights into the stress distribution and failure mechanisms of the reinforced beams. Novel aspects of this study include the identification of specific failure patterns unique to CFRP-reinforced beams and the introduction of an enhanced interaction model that more accurately reflects the composite behavior under load. In CFRP-reinforced beams, specific failure patterns were identified, including flexural cracks in the tension zone and debonding of the CFRP sheets. These patterns indicate the points of maximum stress concentration and potential weaknesses in the reinforcement strategy. The study revealed that while CFRP significantly improves the overall strength and stiffness, careful attention must be given to the bonding process and the quality of the adhesive used to ensure optimal performance. These findings contribute significantly to the understanding of material interactions and structural performance, offering new pathways for the design and optimization of composite-reinforced concrete structures. This research underscores the transformative potential of composite materials in elevating the structural integrity and longevity of concrete infrastructures.

Indexed as

abaquscomposite materialsconcretedamagefinite element modelmechanical propertiesreinforced concrete

Identifiers

PMID39065373
PMCPMC11281052

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.