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Agarwal, Harsha; Shariff, Mohammad Najeeb; Mangalathu, Sujith (2025) Prediction of flexural crack width of reinforced concrete beams using interpretable machine learning algorithms. Construction and Building Materials, 485. doi:10.1016/j.conbuildmat.2025.141628

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Reference TypeJournal (article/letter/editorial)
TitlePrediction of flexural crack width of reinforced concrete beams using interpretable machine learning algorithms
JournalConstruction and Building Materials
AuthorsAgarwal, HarshaAuthor
Shariff, Mohammad NajeebAuthor
Mangalathu, SujithAuthor
Year2025 (July)Volume485
PublisherElsevier BV
DOIdoi:10.1016/j.conbuildmat.2025.141628Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID18483732Long-form Identifiermindat:1:5:18483732:8
GUID0
Full ReferenceAgarwal, Harsha; Shariff, Mohammad Najeeb; Mangalathu, Sujith (2025) Prediction of flexural crack width of reinforced concrete beams using interpretable machine learning algorithms. Construction and Building Materials, 485. doi:10.1016/j.conbuildmat.2025.141628
Plain TextAgarwal, Harsha; Shariff, Mohammad Najeeb; Mangalathu, Sujith (2025) Prediction of flexural crack width of reinforced concrete beams using interpretable machine learning algorithms. Construction and Building Materials, 485. doi:10.1016/j.conbuildmat.2025.141628
In(2025) Construction and Building Materials Vol. 485. Elsevier BV

References Listed

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See Also

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Pal, Avijit, Ahmed, Khondaker Sakil, Mangalathu, Sujith (2024) Data-driven machine learning approaches for predicting slump of fiber-reinforced concrete containing waste rubber and recycled aggregate. Construction and Building Materials, 417. 135369 doi:10.1016/j.conbuildmat.2024.135369
Cakiroglu, Celal, Islam, Kamrul, BekdaÅŸ, Gebrail, Isikdag, Umit, Mangalathu, Sujith (2022) Explainable machine learning models for predicting the axial compression capacity of concrete filled steel tubular columns. Construction and Building Materials, 356. 129227 doi:10.1016/j.conbuildmat.2022.129227
Jayasinghe, Thushara, wei Chen, Bo, Zhang, Zhaorui, Meng, Xinchen, Li, Yajie, Gunawardena, Tharaka, Mangalathu, Sujith, Mendis, Priyan (2023) Data-driven shear strength predictions of recycled aggregate concrete beams with /without shear reinforcement by applying machine learning approaches. Construction and Building Materials, 387. 131604 doi:10.1016/j.conbuildmat.2023.131604
Wang, Chenxin, Zou, Xingxing, Sneed, Lesley H., Zhang, Feng, Zheng, Kaiqi, Xu, Hao, Li, Guofen (2023) Shear strength prediction of FRP-strengthened concrete beams using interpretable machine learning. Construction and Building Materials, 407. 133553 doi:10.1016/j.conbuildmat.2023.133553
Bouras, Y., Li, L. (2023) Prediction of high-temperature creep in concrete using supervised machine learning algorithms. Construction and Building Materials, 400. 132828 doi:10.1016/j.conbuildmat.2023.132828
Luo, Xianzhi; Zhang, Sumei; Zhang, Yuchen (2025) Flexural behavior and crack width prediction of UHPC-NC layered composite members. Construction and Building Materials, 494. doi:10.1016/j.conbuildmat.2025.143341
Siva Chidambaram, R., Agarwal, Pankaj (2015) Flexural and shear behavior of geo-grid confined RC beams with steel fiber reinforced concrete. Construction and Building Materials, 78. 271-280 doi:10.1016/j.conbuildmat.2015.01.021
Bolbolvand, Milad; Tavakkoli, Seyed Mehdi; Alaee, Farshid Jandaghi (2025) Prediction of compressive and flexural strengths of ultra-high-performance concrete (UHPC) using machine learning for various fiber types. Construction and Building Materials, 493. doi:10.1016/j.conbuildmat.2025.143135
Bayar, Gökhan, Bilir, Turhan (2019) A novel study for the estimation of crack propagation in concrete using machine learning algorithms. Construction and Building Materials, 215. 670-685 doi:10.1016/j.conbuildmat.2019.04.227
Toutanji, Houssam, Deng, Yong (2003) Deflection and crack-width prediction of concrete beams reinforced with glass FRP rods. Construction and Building Materials, 17. 69-74 doi:10.1016/s0950-0618(02)00094-6
Taffese, Woubishet Zewdu, Sistonen, Esko, Puttonen, Jari (2015) CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods. Construction and Building Materials, 100. 70-82 doi:10.1016/j.conbuildmat.2015.09.058
 
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