ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

Abambres, Miguel; Lantsoght, Eva O. L.

doi:10.3390/fib7100088

Cited by 25 publications

(13 citation statements)

References 120 publications

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“…As compared to previously developed ANN-based expressions for similar problems in structural concrete, e.g., problems where the amount of experimental data is large but the theoretical understanding is limited, we found larger errors for this problem. Other structural concrete problems that we studied with a similar approach were the shear capacity of one-way slabs without shear reinforcement [59] and the shear capacity of steel fiber reinforced concrete beams without stirrups [84]. These observations are in line with the scatter observed in experiments.…”

Section: Discussionsupporting

confidence: 63%

“…An overview of these features, which were selected from the literature, is given in Table 3 and Table 4. A description of these features can be found in [59], (note that F14 has been removed), and the way in which the input data is divided into the training, the validation, and the testing subsets (F4) is given in [84]. The work was coded in Matlab [85] using the neural network toolbox for popular learning algorithms [options 1–3 of feature 13 (F13) in Table 4].…”

Section: Methodsmentioning

confidence: 99%

“…The validation of the developed software can be found in [86]. Moreover, several papers involving the successful application of this software have already been published [84,87].…”

Section: Methodsmentioning

confidence: 99%

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ANN-Based Fatigue Strength of Concrete under Compression

Abambres

Lantsoght

2019

Materials

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When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we study the reduced capacity as a function of a given number of cycles by means of artificial neural networks. We used an input database with 203 datapoints gathered from the literature. To find the optimal neural network, 14 features of neural networks were studied and varied, resulting in the optimal neural net. This proposed model resulted in a maximum relative error of 5.1% and a mean relative error of 1.2% for the 203 datapoints. The proposed model resulted in a better prediction (mean tested to predicted value = 1.00 with a coefficient of variation 1.7%) as compared to the existing code expressions. The model we developed can thus be used for the design and the assessment of concrete structures and provides a more accurate assessment and design than the existing methods.

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Section: Discussionsupporting

confidence: 63%

Section: Methodsmentioning

confidence: 99%

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ANN-Based Fatigue Strength of Concrete under Compression

Abambres

Lantsoght

2019

Materials

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“…Previous studies on SFRC have primarily focused on its tensile [ 9 , 20 , 21 , 22 ], shear [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], and flexural behavior [ 5 , 32 , 33 , 34 ]. While, the number of studies on SFRC’s torsional behavior remains limited, although in some load cases the torsional response of beams may govern the overall structural behavior.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Regression Strength Model for Steel Fiber-Reinforced Concrete Beams under Torsion

2021

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Torsional behavior and an analysis of steel fiber reinforced concrete (SFRC) beams are investigated in this paper. The purpose of this study is twofold: to examine the torsion strength models for SFRC beams available in the literature and to address properly verified design formulations for SFRC beams under torsion. A total of 210 SFRC beams tested under torsion from 16 different experimental investigations around the world are compiled. The few strength models available from the literature are adapted herein and are used to calculate the torsional strength of the beams. The predicted strength is compared with the experimental values measured by the performed torsional tests and these comparisons showed room for improvement. First, a proposed model is based on optimizing the constants of the existing formulations using multi-linear regression. Furthermore, a second model is proposed, which is based on modifying the American Concrete Institute (ACI) design code for reinforced concrete (RC) members to include the effect of steel fibers on the torsional capacity of SFRC beams. Applications of the proposed models showed better compliance and consistency with the experimental results compared to the available design models, providing safe and verified predictions. Furthermore, the second model implements the ACI code for RC using a simple and easy-to-apply formulation.

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“…The incorporation of randomly distributed short steel fibers has been developed as an alternative reinforcement technique to improve the brittle tensional failure and poor cracking performance of concrete by the debonding and pullout process of the fibers [7][8][9]. For this reason, deformed steel fibers with hooked ends exhibit anchoring action, providing increased bond characteristics that leads to a crucial enhancement of the post-cracking response, improving the energy-absorbing capability [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Effect of Steel Fibers on the Hysteretic Performance of Concrete Beams with Steel Reinforcement—Tests and Analysis

et al. 2020

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The use of fibers as mass reinforcement to delay cracking and to improve the strength and the post-cracking performance of reinforced concrete (RC) beams has been well documented. However, issues of common engineering practice about the beneficial effect of steel fibers to the seismic resistance of RC structural members in active earthquake zones have not yet been fully clarified. This study presents an experimental and a numerical approach to the aforementioned question. The hysteretic response of slender and deep steel fiber-reinforced concrete (SFRC) beams reinforced with steel reinforcement is investigated through tests of eleven beams subjected to reversal cyclic loading and numerical analysis using 3D finite element (FE) modeling. The experimental program includes flexural and shear-critical SFRC beams with different ratios of steel reinforcing bars (0.55% and 1.0%), closed stirrups (from 0 to 0.5%), and fibers with content from 0.5 to 3% per volume. The developed nonlinear FE numerical simulation considers well-established relationships for the compression and tensional behavior of SFRC that are based on test results. Specifically, a smeared crack model is proposed for the post-cracking behavior of SFRC under tension, which employs the fracture characteristics of the composite material using stress versus crack width curves with tension softening. Axial tension tests of prismatic SFRC specimens are also included in this study to support the experimental project and to verify the proposed model. Comparing the numerical results with the experimental ones it is revealed that the proposed model is efficient and accurately captures the crucial aspects of the response, such as the SFRC tension softening effect, the load versus deformation cyclic envelope and the influence of the fibers on the overall hysteretic performance. The findings of this study also reveal that SFRC beams showed enhanced cyclic behavior in terms of residual stiffness, load-bearing capacity, deformation, energy dissipation ability and cracking performance, maintaining their integrity through the imposed reversal cyclic tests.

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ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

Cited by 25 publications

References 120 publications

ANN-Based Fatigue Strength of Concrete under Compression

ANN-Based Fatigue Strength of Concrete under Compression

Multivariable Regression Strength Model for Steel Fiber-Reinforced Concrete Beams under Torsion

Effect of Steel Fibers on the Hysteretic Performance of Concrete Beams with Steel Reinforcement—Tests and Analysis

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