{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:23:59Z","timestamp":1760059439618,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Middle-aged and Young Teachers\u2019 Basic Ability Promotion Project of Guangxi","award":["2024KY1893","2021KY1810","2025KY1203"],"award-info":[{"award-number":["2024KY1893","2021KY1810","2025KY1203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The strengthening of reinforced concrete (RC) beams with aluminum alloy was typically implemented in a symmetrical configuration. To evaluate the flexural performance of strengthened beams, four machine learning (ML)-based models, namely Random Forest (RF), Xtreme Gradient Boosting (XGBoost), Adaptive Boosting (Adaboost), and Light Gradient Boosting Machine (LightGBM), were developed for predicting the flexural bearing capacity of aluminum-alloy-strengthened RC beams. A total of 124 experimental samples were collected from the literature to establish a database for the prediction models, with 70% and 30% of the data allocated as the training and testing sets, respectively. The K-fold cross-validation method and random search method were used to adjust the hyperparameters of the algorithm, thereby improving the performance of the models. The effectiveness of the models was evaluated through statistical indicators, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Additionally, absolute error boxplots and Taylor diagrams were used for statistical comparisons of the ML models. SHAP (Shapley Additive Explanations) was employed to analyze the importance of each input parameter in the predictive capability of the ML models and further examine the influence of feature variables on the model prediction results. The results showed that the predicted values of all models had a good correlation with the experimental values, especially the LightGBM model, which can effectively predict the flexural bearing capacity behavior of aluminum-alloy-strengthened RC beams. The research achievements provided a reliable prediction framework for optimizing aluminum-alloy-strengthened concrete structures and offered references for the design of future strengthened structures.<\/jats:p>","DOI":"10.3390\/sym17060944","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T04:20:23Z","timestamp":1750134023000},"page":"944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Flexural Bearing Capacity of Aluminum-Alloy-Reinforced RC Beams Based on Machine Learning"],"prefix":"10.3390","volume":"17","author":[{"given":"Chunmei","family":"Mo","sequence":"first","affiliation":[{"name":"College of Architecture and Civil Engineering, Nanning University, Nanning 530200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7930-2495","authenticated-orcid":false,"given":"Jun","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Architecture and Civil Engineering, Nanning University, Nanning 530200, China"}]},{"given":"Junzhong","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangxi Construction Engineering Group No.4 Construction Engineering Co., Ltd., Guilin 541000, China"}]},{"given":"Tian","family":"Li","sequence":"additional","affiliation":[{"name":"College of Architecture and Civil Engineering, Nanning University, Nanning 530200, China"}]},{"given":"Yanxi","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Architecture and Civil Engineering, Nanning University, Nanning 530200, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106573","DOI":"10.1016\/j.compstruc.2021.106573","article-title":"Nonlinear finite element models of reinforced concrete beams strengthened in bending with mechanically fastened aluminum alloy plates","volume":"253","author":"Abuodeh","year":"2021","journal-title":"Comput. 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