Reliability analysis of shear strength of reinforced concrete deep beams using NLFEA

Díaz, Rafael Andrés Sanabria; Nova, Silvia Juliana Sarmiento; Silva, Maria C.A. Teixeira da; Trautwein, Leandro Mouta; Almeida, Luiz Carlos de

doi:10.1016/j.engstruct.2019.109760

Cited by 16 publications

(5 citation statements)

References 31 publications

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“…(iv) For idx � 1: N P , do (v) Calculate V and P for each CR using the following formula by the calculated values of mutual information for all elements of chromosomes CR i dx . 8 Complexity from forming numerical attributes with more extensive ranges from dominating smaller ones. In this study, several scenarios are developed to estimate the shear strength of deep beams.…”

Section: Model Development and Performancementioning

confidence: 99%

“…e RC behaviour of deep beams and specifically the shear strength has been quantitatively studied over several decades using various models and empirical approaches such as the strut and sill model, which is considered the most commonly used approach and has been adopted by various codes such as ACI 318-14 [5], CSA A23.3-14 [6], and EC2 [7], or mechanism analysis based on the upper bound theory of plasticity and the finite element approach. However, the design methods of these approaches are linear and therefore unable to capture the complex relationship between the vectors affecting the shear strength and the shear strength itself, in which, in turn, the obtained predicted value of the shear strength differs significantly from the actual value [8].…”

Section: Introductionmentioning

confidence: 99%

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Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams

et al. 2022

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The deep beam in load transfer is very important as well as difficult to design due to its shear stress problems. Accurate estimation of shear stress would help engineers to get a safer design. One of the major obstacles in building an accurate prediction model is optimising the input variables. Therefore, developing an efficient algorithm to select the optimal input parameters that have the highest information content to represent the target and minimise redundant data is very important. The feature-section algorithm based on the combination of genetic algorithm and information theory (GAITH) was used to select the most important input combinations and introduce them into the prediction models. Four models were used in this study: locally weighted linear regression (LWLR) based on the radial basis kernel function, multiple linear regression (MLR), extreme learning machine (ELM), and random forest (RF). The study found that all applied models were significantly improved by the presence of the GAITH algorithm, except for the MLR model. The LWLR-GAITH model showed 29.15% to 47.88% higher performance accuracy in terms of root mean square error (RMSE) than the other hybrid models during the test phase. Moreover, the results of the standard models (without using the GAITH algorithm) proved the superiority of the LWLR model in reducing the RMSE by 34.51%, 55.17%, and 35.35% compared to RF, MLR, and ELM, respectively. Thus, the inclusion of the LWLR model with GAITH has demonstrated a reliable and applicable computer aid for modelling shear strength in deep beams.

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Section: Model Development and Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams

et al. 2022

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“…The influence of high strength concrete on the shear capacity of deep beams has been discussed in references [24,25]. Moreover, in reference [26,27], through the analysis of nonlinear finite element numerical, the stress characteristics and failure mechanism of deep beam structure are further obtained, which provides theoretical support for the design of deep beam. Considering the fracture and damage characteristics of concrete as a typical brittle material [28][29][30], some scholars studied deep beams by changing the composite structure of deep beams or adding new materials.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Shear Capacity of High Strength Reinforcement Concrete Deep Beams with Small Shear Span–Depth Ratio

Zhang

Xie

et al. 2020

Materials

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This study aimed to investigate the shear capacity performance for eight deep beams with HTRB600 reinforced high strength concrete under concentrated load to enable a better understanding of the effects of shear span–depth ratio, longitudinal reinforcement ratio, vertical stirrup ratio and in order to improve design procedures. The dimension of eight test specimens is 1600 mm × 200 mm × 600 mm. The effective span to height ratio l0/h is 2.0, the shear span–depth ratio λ is 0.3, 0.6 and 0.9, respectively. In addition, the longitudinal reinforcement ratio ρs is set to 0.67%, 1.05%, 1.27%, and the vertical stirrup ratio is taken to be 0%, 0.25%, 0.33%, 0.5%. Through measuring the strain of steel bar, the strain of concrete and the deflection of mid-span, the characteristics of the full process of shear capacity, the failure mode and the load deflection deformation curve were examined. The test results showed that the failure mode of deep beams with small shear span–depth ratio is diagonal compression failure, which is influenced by the layout and quantity of web reinforcement. The diagonal compression failure could be classified into two forms: crushing-strut and diagonal splitting. With decreasing of shear span–depth ratio and increasing longitudinal reinforcement ratio, the shear capacity of deep beams increases obviously, while the influence of vertical web reinforcement ratio on shear capacity is negligible. Finally, the shear capacity of eight deep beams based on GB 50010-2010 is calculated and compared with the calculation results of ACI 318-14, EN 1992-1-1:2004 and CSA A23.3-04, which are based on strut-and-tie model. The obtained results in this paper show a very good agreement with GB50010-2010 and ACI 318-14, while the results of EN 1992-1-1:2004 and CSA A23.3-04 are approved to be conservative.

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“…NLFEA revolutionized the field, enabling practitioners and researchers to explore and replicate the response of reinforced concrete (RC) members and systems, accounting for both material and geometric non-linearities. The application of NLFEA in structural engineering, especially within the context of RC structures, has been the focus of numerous investigations and guidelines [26][27][28]. Moreover, NLFEA has emerged as a powerful tool for assessing existing RC structures and infrastructures, considering the impact of deterioration processes [29,30].…”

Section: Introductionmentioning

confidence: 99%

Global Resistance Methods for the Design of Fiber-Reinforced Concrete (FRC) Beams with Material Nonlinear Finite Element Analysis

Shahrbijari,

Barros,

Valente

2023

Buildings

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This article explores the application of the global resistance methods (GRMs) on the design of hybrid glass fiber-reinforced polymer (GFRP) and steel fiber-reinforced concrete (SFRC) beams. Addressing challenges posed by GFRP-reinforced beams, this study aims to assess the impact of material uncertainties on the behavior of such hybrid beams. The investigation involves the experimental testing of I-shaped SFRC beams, which are used to develop and validate nonlinear finite element analysis (NLFEA) models. These models incorporate material non-linearities while minimizing uncertainties related to modeling assumptions. Through the application of GRM, the study evaluates the global resistance safety factor, offering insights into the structural performance of hybrid reinforcement SFRC beams. Ultimately, this research seeks to facilitate a transition from traditional localized approaches to more accurate and comprehensive analyses for the design of hybrid reinforcement SFRC beams, contributing to the advancement of structural engineering by promoting safer, more resilient, and sustainable construction systems.

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Reliability analysis of shear strength of reinforced concrete deep beams using NLFEA

Cited by 16 publications

References 31 publications

Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams

Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams

Experimental Study on Shear Capacity of High Strength Reinforcement Concrete Deep Beams with Small Shear Span–Depth Ratio

Global Resistance Methods for the Design of Fiber-Reinforced Concrete (FRC) Beams with Material Nonlinear Finite Element Analysis

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