{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:23:52Z","timestamp":1777037032700,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables in this study are rebar type and diameter, elastic modulus and tensile strength of rebars, concrete compressive strength and cover, embedment length, and test method. The dataset contains two test methods: pullout tests and beam tests. Four types of rebar, including carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymer (GFRP), basalt, and steel rebars, were considered. The boosting ML models applied in this study include AdaBoost, CatBoost, Gradient Boosting, XGBoost, and Hist Gradient Boosting. After hyperparameter tuning, these models demonstrated significant improvements in predictive accuracy, with XGBoost achieving the highest R2 score of 0.95 and the lowest Root Mean Square Error (RMSE) of 2.21. Shapley values analysis revealed that tensile strength, elastic modulus, and embedment length are the most critical factors influencing bond strength. The findings offer valuable insights for applying ML models in predicting bond strength in FRP-reinforced UHPC, providing a practical tool for structural engineering.<\/jats:p>","DOI":"10.3390\/computation12100202","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T06:30:03Z","timestamp":1728455403000},"page":"202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete"],"prefix":"10.3390","volume":"12","author":[{"given":"Alireza","family":"Mahmoudian","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran P.O. Box 16788-15811, Iran"}]},{"given":"Maryam","family":"Bypour","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Semnan University, Semnan 1581613711, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7719-7997","authenticated-orcid":false,"given":"Mahdi","family":"Kioumarsi","sequence":"additional","affiliation":[{"name":"Department of Built Environment, OsloMet\u2014Oslo Metropolitan University, 0166 Oslo, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.conbuildmat.2016.03.005","article-title":"Bond-dependent coefficient of glass-and carbon-FRP bars in normal-and high-strength concretes","volume":"113","author":"Ahmed","year":"2016","journal-title":"Constr. Build. 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