Machine and deep learning-based prediction of flexural moment capacity of ultra-high performance concrete beams with/out steel fiber

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dc.authorscopusid58776617800
dc.authorscopusid57845945600
dc.contributor.authorErgen F.
dc.contributor.authorKatlav M.
dc.date.accessioned2024-08-04T20:02:28Z
dc.date.available2024-08-04T20:02:28Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis paper presents a study to develop the most appropriate machine learning (ML) and deep learning (DL) models for predicting the flexural moment capacity of ultra-high performance concrete beams (UHPC-Bs) with/out steel fibers. As prediction models, five different ML models (support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), random forest (RF), and extremely randomized trees (ERT)) and three different DL models (long short-term memory (LSTM), bidirectional LSTM (BILSTM), and gated recurrent unit (GRU)) are adopted in this study. To train and evaluate these prediction models, a total of 82 experimental results from the literature on rectangular UHPC-Bs with/out steel fibers are used as a database. On the other hand, the Shapley Additive Explanations (SHAP) analysis is used to evaluate the effects of input features on the prediction results. Furthermore, based on the best prediction model obtained in this study, a graphical user interface is designed to facilitate the use of the flexural moment capacity of UHPC-Bs with/out steel fibers in practical applications. The results obtained indicated that both ML and DL models can be effectively used to predict the flexural moment capacity of UHPC-Bs with/out steel fibers. Particularly, ML algorithm such as XGBoost and DL algorithm like BILSTM stand out for their ability to predict the flexural moment capacity with high accuracy. Furthermore, SHAP analysis revealed that the longitudinal tensile reinforcing steel bar area (As), effective depth (d), and fiber-reinforcing index (Vf × lf/df) are the most effective parameters for prediction performance, while the compressive strength (fc) of concrete is less effective. Consequently, the developed prediction models and graphical user interface (GUI) will help structural engineers and designers quickly predict the flexural moment capacity of UHPC-Bs with high accuracy and make the design process more efficient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.en_US
dc.identifier.doi10.1007/s42107-024-01064-2
dc.identifier.endpage4562en_US
dc.identifier.issn1563-0854
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85192765671en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage4541en_US
dc.identifier.urihttps://doi.org/10.1007/s42107-024-01064-2
dc.identifier.urihttps://hdl.handle.net/11616/91721
dc.identifier.volume25en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofAsian Journal of Civil Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectFlexural performanceen_US
dc.subjectMachine learningen_US
dc.subjectRC beamsen_US
dc.subjectUltra-high performance concreteen_US
dc.titleMachine and deep learning-based prediction of flexural moment capacity of ultra-high performance concrete beams with/out steel fiberen_US
dc.typeArticleen_US

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