Long-term deflection of high-strength fiber reinforced concrete beams

Ashour, Samir A.; Mahmood, Khalid; Wafa, Faisal F.

doi:10.12989/sem.1999.8.6.531

Cited by 11 publications

(15 citation statements)

References 6 publications

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“…The readers are referred to the articles by Swamy et al [19] and Khuntia et al [20] for discussions on the effect of fiber orientation and the quantification of this effect. The models of the first category which are used in this study are those of Mansur et al [21], Narayanan and Darwish [22], Ashour et al [23], Swamy et al [19], and Imam et al [24]. In the second category, fibers mechanical properties are indirectly accounted for through material strength characteristics like splitting tensile strength, modulus of rupture, equivalent flexural strength, and residual tensile strength factors.…”

Section: Prediction Models For Shear Strength Of Frc Beamsmentioning

confidence: 99%

Analytical study on shear strength of macro synthetic fiber reinforced concrete beams

Yazdanbakhsh

Altoubat

Rieder

2015

Engineering Structures

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Section: Prediction Models For Shear Strength Of Frc Beamsmentioning

confidence: 99%

Analytical study on shear strength of macro synthetic fiber reinforced concrete beams

Yazdanbakhsh

Altoubat

Rieder

2015

Engineering Structures

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“…Compared with normal-strength concrete, the increased compressive strength of HSC makes it more brittle and less resistive to crack opening and propagation [35], which means that the design equations for the structure with normal-strength concrete to be applied on high-strength concrete will lead to unconservative design. (B) The structural performance: Ashour, Hasanain and Wafa [15] demonstrated that the shear strength rigidity increased with increased compressive strength of the concrete. For the aspect of microstructure analysis in reinforced concrete beams, the application of high-strength concrete ensures similar strength between aggregate and concrete matrix.…”

Section: High-strength Concretementioning

confidence: 99%

“…Ashour, Hasanain and Wafa [15] developed a shear capacity formula based on regression analysis, with a focus on the shear capacity contribution of concrete compressive strength and shear span ratio:…”

Section: Equations Proposed In Published Literaturementioning

confidence: 99%

“…However, most studies tend to focus on the normal-strength fibre-reinforced concrete beam, while ignoring the synergetic effect between fibres and concrete, which changes with increasing compressive strength. Hence, Khuntia, et al [13], Al-Ta'an, et al [14], and Ashour, et al [15] each proposed the calculation formula for high-strength fibre-reinforced concrete beams. Since the current empirical or semi-empirical formulations are developed based on narrow datasets, it is reasonable to develop a data-driven model for predicting shear strength of high-strength fibre-reinforced concrete beams.…”

Section: Introductionmentioning

confidence: 99%

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Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models

Qin

Kaewunruen

2023

Sustainability

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In recent years, adding fibres into brittle concrete to improve ductility has gained momentum in the construction industry. Despite the significant momentum, limitations do exist in design and industrial applications, contributing to the complexity of shear behaviours in fibre-reinforced concrete and the existing empirical models that can hardly provide a reasonable prediction, especially for high-strength concrete applications. A critical review reveals that current research mostly focuses on single eigenvalue analysis and pay less attention to the different synergetic effect of fibres on high-strength concrete and normal-strength concrete. This study aims to fill the research gap by the unprecedented use of reliable models for the prediction and evaluation of structural and sustainable properties of high-strength fibre-reinforced concrete beams. To this end, this study establishes three novel deep learning (ANN, BNN, and Xgboost) models for designing and optimising the shear capacity of ‘high-strength’ fibre-reinforced concrete beams towards the circular economy. In addition to introducing a new type of novel machine learning (BNN) model, which is capable of structural design and takes into account complex design features, our study also enhances sustainability by reducing greenhouse gas (GHG) emissions. The novel prediction models unprecedentedly elicit flexural capacity, structural stiffness, carbon emission, and price, together with the shear strength for high-strength fibre-reinforced structures. Firstly, this study focuses on multiple parameters for forecasting high-strength fibre-reinforced concrete beams. In addition, the models provide more comprehensive insights into the design and manufacture of high-strength steel fibre-reinforced concrete structures in a more environmentally friendly manner. With the help of the proposed models, it will be more cost-benefit and time-efficient for the researchers to obtain the optimum design with the consideration of both structural and sustainable performance. The established models exhibit excellent prediction accuracy, and the Bayesian neural network (BNN) is found to have the best performance: R2 is 0.937, MSE is 0.06 and MAE is 0.175 in shear strength prediction; R2 = 0.968, MSE is 0.040, and MAE is 0.110 in flexural capacity prediction; R2 is 0.907, MSE is 0.070, and MAE is 0.204 in shear stiffness prediction; R2 is 0.974, MSE is 0.022, and MAE is 0.063 in carbon emission prediction; and R2 is 0.977, MSE is 0.020, and MAE is 0.082 in price prediction.

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“…When compared to other models, the models from Ashour et al [60] and Khuntia et al [54] demonstrate the lowest prediction performance, indicating a higher discrepancy between their results and the actual outcomes, suggesting room for improvement in their predictive capabilities. The results from Ashour came from a proposed equation in the study [61], which is an adaptation of Zsutty's equation found in reference [62]. This modified version integrates the fiber factor, accounting for its influence on the overall equation.…”

Section: Comparision With Previouly Developed Modelsmentioning

confidence: 99%

Shear Strength Prediction of Steel-Fiber-Reinforced Concrete Beams Using the M5P Model

Al-Abdaly

Hussein

Imran³

et al. 2023

Fibers

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This article presents a mathematical model developed using the M5P tree to predict the shear strength of steel-fiber-reinforced concrete (SFRC) for slender beams using soft computing techniques. This method is becoming increasingly popular for addressing complex technical problems. Other approaches, such as semi-empirical equations, can show known inaccuracies, and some soft computing methods may not produce predictive equations. The model was trained and tested using 332 samples from an experimental database found in the previous literature, and it takes into account independent variables such as the effective depth d, beam width bw, longitudinal reinforcement ratio ρ, concrete compressive strength fc, shear span to effective depth ratio a/d, and steel fiber factor Fsf. The predictive performance of the proposed M5P-based model was also compared with the one of existing models proposed in the previous literature. The evaluation revealed that the M5P-based model provided a more consistent and accurate prediction of the actual strength compared to the existing models, achieving an R2 value of 0.969 and an RMSE value of 37.307 for the testing dataset. It was found to be a reliable and also straightforward model. The proposed model is likely to be highly helpful in assessing the shear capacity of SFRC beams during the pre-planning and pre-design stages and could also be useful to help for future revisions of design standards.

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Long-term deflection of high-strength fiber reinforced concrete beams

Cited by 11 publications

References 6 publications

Analytical study on shear strength of macro synthetic fiber reinforced concrete beams

Analytical study on shear strength of macro synthetic fiber reinforced concrete beams

Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models

Shear Strength Prediction of Steel-Fiber-Reinforced Concrete Beams Using the M5P Model

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