An Experimental Investigation and Computer Modeling of Direct Tension Pullout Test of Reinforced Concrete Cylinder

Abbas, Nadeem; Yousaf, Muhammad; Akbar, M.; Saeed, Muhammad; Huali, Pan; Hussain, Zahoor

doi:10.3390/inventions7030077

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…They focused on the calibration of FE models to match the experimental results. Abbas et al [14] performed finite element analysis in ANSYS software and compared the FEM bond-slip curve with the experiment. Their bond-slip curve showed that the stiffness did not reflect the experimental results correctly.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Study on Predicting Bond-Slip Relationship of Reinforced Concrete using Surface Based Cohesive Behavior

Minhajul Bari Prince,

Debasish Sen

2024

Frattura Ed Integrità Strutturale

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Overall structural integrity and load transfer between concrete and reinforcement enabling composite action relies on the bond between concrete and reinforcement. Bond strength determination of reinforced concrete is an essential task that a pullout test can determine. Experimental pullout behaviour can be affected by various parameters, such as concrete and steel strength, boundary conditions, etc. Therefore, a parametric study on pullout tests is time-consuming. In addition, measurements of internal stress-strain components and damages of constitutive materials are also difficult in experimental endeavours. In this context, finite element (FE) models should be developed to perform a parametric study with cost and time-saving. This study proposes a finite element modeling strategy of reinforced concrete under pullout force by using the expected failure mechanism to predict bond-slip behaviour in ABAQUS. In FE models, surface-to-surface cohesive interaction behaviour was used to assign interaction between reinforcement and concrete. The proposed modelling strategy was validated with available experimental data from four reference specimens, having all possible failures (i.e., pullout, splitting, and splitting-pullout) under pullout loading. The finite element analysis showed that the proposed FE modeling strategy performed well in predicting bond-slip behaviour in elastic regions. Additionally, maximum bond stresses were predicted satisfactorily, except for the splitting failure pattern, using the proposed FE modelling strategy.

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Section: Introductionmentioning

confidence: 99%

A Numerical Study on Predicting Bond-Slip Relationship of Reinforced Concrete using Surface Based Cohesive Behavior

Minhajul Bari Prince,

Debasish Sen

2024

Frattura Ed Integrità Strutturale

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“…Non-destructive testing (NDT) is primarily concerned with determining the quality and long-term performance of a concrete construction. A number of countries have embraced non-destructive testing (NDT) [ 26 ] since it does not alter the structure’s appearance, quality or performance. For the evaluation of structural conditions, ultrasonic pulse velocity (UPV) is a non-destructive test that is commonly employed.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on Non-Destructive Evaluation of the Mechanical Characteristics of a Sustainable Concrete Incorporating Industrial Waste

et al. 2022

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Structural materials sustainability is gaining popularity across the globe at present. Reusing natural resources, building, demolition debris, and solid waste are the most apparent tools to make construction more environmentally friendly. Traditional concrete is believed to be less durable, stronger, environmentally friendly, and socially and commercially feasible than industrial waste concrete. The evolution of non-destructive testing (NDT) across time has not been investigated in depth by researchers. An experimental study was carried out to propose the use of non-destructive mechanisms that would enable us to assess concrete’s compressive strength without causing destruction. Varying quantities of industrial waste (coal bottom ash (CBA) and waste glass sludge (WGS)) were incorporated to cast concrete prisms (150 mm × 150 mm × 150 mm). The results obtained helped us to establish relationships between the compressive strength of concrete and the Schmidt hammer rebound value, as well as the ultrasonic pulse velocities. Microstructural analysis showed that incorporating 10% of CBA and WGS improved the porosity of concrete specimens, which shows the applicability of these industrial wastes as partial cement replacements. Scanning electron microscopy (SEM) showed traces of calcium alumino-silicate hydrate (C-A-S-H), portlandite and C-S-H, which indicates the binder characteristics of CBA and WGS. The concept of the response surface approach (RSM) for optimizing cement and industrial waste substitution was validated by the polynomial work expectation. The model was statistically significant when the fluctuation of ANOVA was analyzed using a p value with a significance level of 0.05. The study results show that the usage of 15% CBA and 10% WGS as a cementitious additive and cement replacement has the potential to increase the strength of concrete significantly.

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Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

Zar,

Hussain,

Akbar

et al. 2024

Int J Mech Mater Des

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In this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

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An Experimental Investigation and Computer Modeling of Direct Tension Pullout Test of Reinforced Concrete Cylinder

Cited by 7 publications

References 23 publications

A Numerical Study on Predicting Bond-Slip Relationship of Reinforced Concrete using Surface Based Cohesive Behavior

A Numerical Study on Predicting Bond-Slip Relationship of Reinforced Concrete using Surface Based Cohesive Behavior

An Experimental Study on Non-Destructive Evaluation of the Mechanical Characteristics of a Sustainable Concrete Incorporating Industrial Waste

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

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