Estimation of Minimum Torsional Reinforcement of Reinforced Concrete and Steel Fiber-Reinforced Concrete Members

Ju, Hyunjin; Han, Sun Jin; Zhang, Dichuan; Kim, Jong; Wu, Wei; Kim, Kang Su

doi:10.1155/2019/4595363

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…For this study, the dataset compiled by Bernardo et al [3] and also used by Henedy et al [47] was considered. In the former study, a literature review was performed to compile the main features and torsional capacity of RC rectangular beams tested under pure torsion until failure [5,6,17,[56][57][58][59][60][61][62][63][64][65][66][67][68]. In total, 202 RC beams were compiled to build the dataset.…”

Section: Datasetmentioning

confidence: 99%

Torsional Capacity Prediction of Reinforced Concrete Beams Using Machine Learning Techniques Based on Ensembles of Trees

Bernardo

Imran³

et al. 2023

Applied Sciences

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For the design or assessment of framed concrete structures under high eccentric loadings, the accurate prediction of the torsional capacity of reinforced concrete (RC) beams can be critical. Unfortunately, traditional semi-empirical equations still fail to accurately estimate the torsional capacity of RC beams, namely for over-reinforced and high-strength RC beams. This drawback can be solved by developing accurate Machine Learning (ML) based models as an alternative to other more complex and computationally demanding models. This goal has been herein addressed by employing several ML techniques and by validating their predictions. The novelty of the present article lies in the successful implementation of ML methods based on Ensembles of Trees (ET) for the prediction of the torsional capacity of RC beams. A dataset incorporating 202 reference RC beams with varying design attributes was divided into testing and training sets. Only three input features were considered, namely the concrete area (area enclosed within the outer perimeter of the cross-section), the concrete compressive strength and the reinforcement factor (which accounts for the ratio between the yielding forces of both the longitudinal and transverse reinforcements). The predictions from the used models were statistically compared to the experimental data to evaluate their performances. The results showed that ET reach higher accuracies than a simple Decision Tree (DT). In particular, The Bagging Meta-Estimator (BME), the Forests of Randomized Trees (FRT), the AdaBoost (AB) and the Gradient Tree Boosting (GTB) reached good performances. For instance, they reached values of R2 (coefficient of determination) in the range between 0.982 and 0.990, and values of cvRMSE (coefficient of variation of the root mean squared error) in the range between 10.04% and 13.92%. From the obtained results, it is shown that these ML techniques provide a high capability for the prediction of the torsional capacity of RC beams, at the same level of other more complicated ML techniques and with much fewer input features.

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Section: Datasetmentioning

confidence: 99%

Torsional Capacity Prediction of Reinforced Concrete Beams Using Machine Learning Techniques Based on Ensembles of Trees

Bernardo

Imran³

et al. 2023

Applied Sciences

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“…For this study, an extensive literature review was performed to compile the main properties and experimental results of RC rectangular beams tested under pure torsion until failure. A total of 202 beams were compiled from several studies [ 10 , 12 , 19 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] to build the database (with 158 plain beams and 44 hollow beams). The number of beams found in the literature was higher than 202; however, some of them were disregarded based on the following criteria: The main properties of the tested RC beams needed to compute the normative torsional strength should be given; The experimental torsional strength should be given and the RC beams should have failed in pure torsion in their ultimate stage as expected; The beams should comply with reinforcement requirements from ACI 318R-19 [ 3 ].…”

Section: Database With Reference Beamsmentioning

confidence: 99%

Improved Equations for the Torsional Strength of Reinforced Concrete Beams for Codes of Practice Based on the Space Truss Analogy

et al. 2022

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Design codes provide the necessary tools to check the torsional strength of reinforced concrete (RC) members. However, some researchers have pointed out that code equations still need improvement. This study presents a review and a comparative analysis of the calculation procedures to predict the torsional strength of RC beams from some reference design codes, namely the Russian, American, European, and Canadian codes for RC structures. The reliability and accuracy of the normative torsional strengths are checked against experimental results from a broad database incorporating 202 RC rectangular beams tested under pure torsion and collected from the literature. The results show that both the readability and accuracy of the codes’ equations should be improved. Based on a correlation study between the experimental torsional strengths, and geometrical and mechanical properties of the beams, refined yet simple equations are proposed to predict torsional strength. It is demonstrated that the proposed formulation is characterized by a significant improvement over the reference design codes. The efficiency of the proposed formulae is also assessed against another equation earlier proposed in the literature, and an improvement is noted as well. From the results, it can be concluded that the proposed equations in this study can contribute to a more accurate and economical design for practice.

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“…To evaluate the accuracy of the design methods specified in the international codes including SNiP, 15 a total of 256 specimens failed in pure torsion were collected from the existing literature, 28,29,[32][33][34][35][36][37][38][39][40][41][42][43][44] of which the details are summarized in Table 1. All the specimens are reinforced in the longitudinal and transverse directions, among which three specimens reinforced less than the minimum reinforcement ratio specified in ACI 318 were excluded in following verification process.…”

Section: Torsional Database and Details Used In Comparative Studymentioning

confidence: 99%

Torsional design method used in Eurasia region: A comparative study

Lee

Kim

et al. 2021

Structural Concrete

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This study conducted an extensive review and evaluation of torsional design methods specified in SNiP and other international building codes. Unlike thin‐walled tube approach utilized as a key basis of design expressions in ACI 318, CSA, and Eurocode 2, skew‐bending theory is still adopted as torsional design methods specified in SNiP code used through Eurasia region. This comparative study is timely required since there are radical code changes in leading Eurasia countries, such as Russia, Kazakhstan, and Uzbekistan, toward Eurocode from SNiP code. To this end, a total of 253 reinforced concrete (RC) specimens that failed in pure torsion were collected from the literature to compare the analytical accuracy of the torsional design methods specified in various design codes. It was found that the torsional design methods specified in SNiP and CSA codes can estimate the torsional strengths of RC members with good level of accuracy, while Eurocode 2 provided quite conservative results. In addition, the maximum limitations stipulated in each design code on the compressive strength of concrete and yield strength of reinforcements are of importance for the safe and efficient torsional design due to interrelation between reinforcement ratios considering materials strengths and inclination angle of compressive concrete strut.

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Estimation of Minimum Torsional Reinforcement of Reinforced Concrete and Steel Fiber-Reinforced Concrete Members

Cited by 8 publications

References 17 publications

Torsional Capacity Prediction of Reinforced Concrete Beams Using Machine Learning Techniques Based on Ensembles of Trees

Torsional Capacity Prediction of Reinforced Concrete Beams Using Machine Learning Techniques Based on Ensembles of Trees

Improved Equations for the Torsional Strength of Reinforced Concrete Beams for Codes of Practice Based on the Space Truss Analogy

Torsional design method used in Eurasia region: A comparative study

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