Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites
<p dir="ltr">The application of inorganic composites has proven to be an effective strengthening technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of the shear capacity of RC beams strengthened with inorganic composites has been a challenging p...
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2022
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| _version_ | 1864513548728664064 |
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| author | Tadesse Gemeda Wakjira (21347729) |
| author2 | Usama Ebead (14779168) M. Shahria Alam (17128834) |
| author2_role | author author |
| author_facet | Tadesse Gemeda Wakjira (21347729) Usama Ebead (14779168) M. Shahria Alam (17128834) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tadesse Gemeda Wakjira (21347729) Usama Ebead (14779168) M. Shahria Alam (17128834) |
| dc.date.none.fl_str_mv | 2022-03-26T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.cscm.2022.e01008 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_learning-based_shear_capacity_prediction_and_reliability_analysis_of_shear-critical_RC_beams_strengthened_with_inorganic_composites/29045204 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Civil engineering Materials engineering Inorganic composites Retrofitting Machine learning Modeling Reliability analysis |
| dc.title.none.fl_str_mv | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The application of inorganic composites has proven to be an effective strengthening technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of the shear capacity of RC beams strengthened with inorganic composites has been a challenging problem due to its complex failure mechanism and the interaction between the internal and external shear reinforcements. Besides, the <u>predictive capabilities </u>of the existing models are not satisfactory. Thus, this research proposed machine learning (ML) based models for predicting the shear capacity of RC beams strengthened in shear with inorganic composites, for the first time, considering all important variables. The results of the analyses evidenced that the proposed ML models can be successfully used to predict the shear capacity of shear-critical RC beams strengthened with inorganic composites. Among the ML models examined herein, the extreme gradient boosting (xgBoost) model showed the highest prediction capability. The comparison among the predictions of the proposed xgBoost and existing models evidenced that the efficacy of the xgBoost model is superior to the existing models in terms of accuracy, safety, and economic aspects. Finally, reliability analysis is performed to calibrate the resistance reduction factors in order to attain target reliability indices of 3.5 and 4.0 for the proposed model.</p><h2>Other Information</h2><p dir="ltr">Published in: Case Studies in Construction Materials<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cscm.2022.e01008" target="_blank">https://dx.doi.org/10.1016/j.cscm.2022.e01008</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_197da2cf1bdd98a7c7a858fca4aa79dc |
| identifier_str_mv | 10.1016/j.cscm.2022.e01008 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29045204 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic compositesTadesse Gemeda Wakjira (21347729)Usama Ebead (14779168)M. Shahria Alam (17128834)EngineeringCivil engineeringMaterials engineeringInorganic compositesRetrofittingMachine learningModelingReliability analysis<p dir="ltr">The application of inorganic composites has proven to be an effective strengthening technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of the shear capacity of RC beams strengthened with inorganic composites has been a challenging problem due to its complex failure mechanism and the interaction between the internal and external shear reinforcements. Besides, the <u>predictive capabilities </u>of the existing models are not satisfactory. Thus, this research proposed machine learning (ML) based models for predicting the shear capacity of RC beams strengthened in shear with inorganic composites, for the first time, considering all important variables. The results of the analyses evidenced that the proposed ML models can be successfully used to predict the shear capacity of shear-critical RC beams strengthened with inorganic composites. Among the ML models examined herein, the extreme gradient boosting (xgBoost) model showed the highest prediction capability. The comparison among the predictions of the proposed xgBoost and existing models evidenced that the efficacy of the xgBoost model is superior to the existing models in terms of accuracy, safety, and economic aspects. Finally, reliability analysis is performed to calibrate the resistance reduction factors in order to attain target reliability indices of 3.5 and 4.0 for the proposed model.</p><h2>Other Information</h2><p dir="ltr">Published in: Case Studies in Construction Materials<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cscm.2022.e01008" target="_blank">https://dx.doi.org/10.1016/j.cscm.2022.e01008</a></p>2022-03-26T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.cscm.2022.e01008https://figshare.com/articles/journal_contribution/Machine_learning-based_shear_capacity_prediction_and_reliability_analysis_of_shear-critical_RC_beams_strengthened_with_inorganic_composites/29045204CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290452042022-03-26T12:00:00Z |
| spellingShingle | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites Tadesse Gemeda Wakjira (21347729) Engineering Civil engineering Materials engineering Inorganic composites Retrofitting Machine learning Modeling Reliability analysis |
| status_str | publishedVersion |
| title | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites |
| title_full | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites |
| title_fullStr | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites |
| title_full_unstemmed | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites |
| title_short | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites |
| title_sort | Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites |
| topic | Engineering Civil engineering Materials engineering Inorganic composites Retrofitting Machine learning Modeling Reliability analysis |