New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength
The assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by the collective evaluation of experimental databases from the available literature. There is still a lot of uncer...
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MDPI AG
2024-12-01
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| Series: | Computers |
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| author | Anthos I. Ioannou David Galbraith Nikolaos Bakas George Markou John Bellos |
| author_facet | Anthos I. Ioannou David Galbraith Nikolaos Bakas George Markou John Bellos |
| author_sort | Anthos I. Ioannou |
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| description | The assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by the collective evaluation of experimental databases from the available literature. There is still a lot of uncertainty in relation to the strength and deformability criteria that have been derived from tests due to the differences in the experimental test setups of the individual research studies that are being fed into the databases used to derive predictive models. This research work focuses on structural elements that exhibit pronounced strength degradation with plastic deformation and brittle failure characteristics. The study’s focus is on evaluating existing models that predict the shear strength of RC columns, which take into account important factors including the structural element’s ductility and axial load, as well as the contributions of specific resistance mechanisms like that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. According to the numerical findings, POLYREG-HYT- and XGBoost-HYT-CV-derived models outperformed other ML models in predicting the shear strength of rectangular RC columns with the correlation coefficient having a value R greater than 99% and minimal errors. It was also found that the newly proposed predictive model derived a 2-fold improvement in terms of the correlation coefficient compared to the best available equation in international literature. |
| format | Article |
| id | doaj-art-6a5b1638446c479e8e2296797270761c |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Computers |
| spelling | doaj-art-6a5b1638446c479e8e2296797270761c2025-01-24T13:27:50ZengMDPI AGComputers2073-431X2024-12-01141210.3390/computers14010002New Predictive Models for the Computation of Reinforced Concrete Columns Shear StrengthAnthos I. Ioannou0David Galbraith1Nikolaos Bakas2George Markou3John Bellos4Department of Civil Engineering, Neapolis University Pafos, 2 Danais Avenue, Pafos 8042, CyprusDepartment of Civil Engineering, University of Pretoria, Private Bag X20, 002 Hatfield, Pretoria 0028, South AfricaMachine Intelligence Research and Engineering P.C., 1 Tsakasianou, 11141 Athens, GreeceDepartment of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, CyprusDepartment of Civil Engineering, Neapolis University Pafos, 2 Danais Avenue, Pafos 8042, CyprusThe assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by the collective evaluation of experimental databases from the available literature. There is still a lot of uncertainty in relation to the strength and deformability criteria that have been derived from tests due to the differences in the experimental test setups of the individual research studies that are being fed into the databases used to derive predictive models. This research work focuses on structural elements that exhibit pronounced strength degradation with plastic deformation and brittle failure characteristics. The study’s focus is on evaluating existing models that predict the shear strength of RC columns, which take into account important factors including the structural element’s ductility and axial load, as well as the contributions of specific resistance mechanisms like that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. According to the numerical findings, POLYREG-HYT- and XGBoost-HYT-CV-derived models outperformed other ML models in predicting the shear strength of rectangular RC columns with the correlation coefficient having a value R greater than 99% and minimal errors. It was also found that the newly proposed predictive model derived a 2-fold improvement in terms of the correlation coefficient compared to the best available equation in international literature.https://www.mdpi.com/2073-431X/14/1/2seismic assessmentreinforced concrete columnsshear strengthmachine learningdesign equations |
| spellingShingle | Anthos I. Ioannou David Galbraith Nikolaos Bakas George Markou John Bellos New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength Computers seismic assessment reinforced concrete columns shear strength machine learning design equations |
| title | New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength |
| title_full | New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength |
| title_fullStr | New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength |
| title_full_unstemmed | New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength |
| title_short | New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength |
| title_sort | new predictive models for the computation of reinforced concrete columns shear strength |
| topic | seismic assessment reinforced concrete columns shear strength machine learning design equations |
| url | https://www.mdpi.com/2073-431X/14/1/2 |
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