AI-guided design framework for bond behavior of steel-concrete in steel reinforced concrete composites: From dataset cleaning to feature engineering

Basit Öğe Kaydı
dc.contributor.authorKatlav, Metin
dc.contributor.authorTabar, Mehmet Emin
dc.contributor.authorTurk, Kazim
dc.date.accessioned2026-04-04T13:34:52Z
dc.date.available2026-04-04T13:34:52Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThis research focuses on establishing an artificial intelligence (AI)-guided design approach for predicting bond behavior of profiled steel-concrete in steel-reinforced concrete (SRC) composites. For that, an extensive literature survey was undertaken, and datasets for three main characteristic bond stresses-bond stress at initial slip (zs), ultimate bond stress (zu), and residual bond stress (zr) -were gathered. In total, it was gathered data points 150 for zs, 251 for zu, and 215 for zr. In addition, the isolation forest algorithm was used to detect and clean the anomalous data in the dataset, resulting in exhaustive and trustworthy data for training the models. As AI models, four popular machine learning algorithms like RF, XGBoost, LightGBM, and CatBoost are adopted. To improve the prediction performance of the models, three cases are established by Shapley additive explanations (SHAP)-based feature engineering. Additionally, SHAP and feature importance analyses were used to examine the impact of each feature on the bond behavior in SRC composites to ensure the explainability of the model. Meanwhile, to enhance the applicability of the study in real-world applications, a graphical user interface (GUI) was designed. According to the results, the CatBoost model proved its superior predictive ability specifically for zs and zr output values; in the test phase, the RMSE values were 0.07 and 0.05, R2 values were 0.904 and 0.934, MAPE were 10.02% and 8.33%, and MAE values were 0.04 and 0.03, respectively. On the other hand, the XGBoost model had the best predictive efficiency in the test phase for the zu output value with RMSE = 0.06, R2 = 0.833, MAPE = 8.32 % and MAE = 0.04. Lastly, based on SHAP and feature importance assessments, the most impactful features on bond behavior were identified as follows: the ratio of side cover to steel section height (cv/ hs), the compressive strength of concrete (fcu), and the ratio of bonded length to steel section height (lb/hs), stirrup ratio (psv), and the yield strength of profiled steel (fy). This knowledge can guide engineers in paying focus to specific features in their design and evaluation processes, resulting in more reliable and optimized outcomes.
dc.identifier.doi10.1016/j.mtcomm.2024.111286
dc.identifier.issn2352-4928
dc.identifier.orcid0000-0001-9093-7195
dc.identifier.orcid0000-0002-3234-5340
dc.identifier.scopus2-s2.0-85211706388
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.mtcomm.2024.111286
dc.identifier.urihttps://hdl.handle.net/11616/109464
dc.identifier.volume42
dc.identifier.wosWOS:001389672600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofMaterials Today Communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectSteel reinforced concrete
dc.subjectBond behavior
dc.subjectMachine learning
dc.subjectUnsupervised anomaly detection
dc.subjectFeature engineering
dc.titleAI-guided design framework for bond behavior of steel-concrete in steel reinforced concrete composites: From dataset cleaning to feature engineering
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu