Development of Prediction Models for the Torsion Capacity of Reinforced Concrete Beams Using M5P and Nonlinear Regression Models

Henedy, Sadiq N.; Naser, Ali H.; Imran, Hamza; Bernardo, Luís; Teixeira, Mafalda; Al-Khafaji, Zainab S.

doi:10.3390/jcs6120366

Cited by 8 publications

(13 citation statements)

References 33 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].…”

Section: Datasetmentioning

confidence: 99%

“…Based on results from previous studies [3,47], the following three input variables were used to characterize each RC beam and predict the torsional capacity T R_Exp : the cross-section area A c (area enclosed within the outer perimeter of the cross-section), the average concrete compressive strength f c , and the reinforcement factor A l f ly A t f ty s (factor accounting for the ratio between the yielding forces of both the longitudinal and transverse reinforcements). To compute the last input variable, the units of each parameter are the same as the ones given in Table A1 in Appendix A.…”

Section: Input and Output Variablesmentioning

confidence: 99%

“…In parallel, during the last decades, a huge amount of data has been accumulated in the field of civil engineering due to the advance in experimental programs, monitoring, data acquisition, and processing. For these reasons, ML-based models have been successfully used in complex civil engineering problems, including geotechnical, materials and structural [27][28][29][30][31][32][33][34][35][36][37][38][39], and some of them already focus on the torsional capacity of RC beams, including externally strengthened and combined RC beams [40][41][42][43][44][45][46][47][48]. The referred studies on the torsional capacity of RC beams are still very limited and they generally apply different ML techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Perhaps the main key challenge is related with the selection process of the most appropriate and effective ML models to solve a particular civil engineering problem. For instance, some studies indicated that the best performances were reached with extreme gradient boosting (XGBoost) for problems involving the shear strength of RC beams [49,50], whereas others have mentioned other ML models to be more performant for the same types of problems [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55], namely Artificial Neural Networks (ANN). This includes the analysis of RC beams under torsion, where the shear effect is predominant [40,41,[43][44][45][46]48].…”

Section: Introductionmentioning

confidence: 99%

“…Ensembles of Trees (ET) have also been used in such problems, although to a lesser extent and with limited success [44]. Explicit ML techniques, such as the M5P model tree [47], were also successfully used. The problem related with the selection process of the most appropriate effective ML model for RC beams under torsion needs more studies to compare the performance of different ML models.…”

Section: Introductionmentioning

confidence: 99%

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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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