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Cited by in F6Publishing
For: Truong GT, Hwang H, Kim C. Assessment of punching shear strength of FRP-RC slab-column connections using machine learning algorithms. Engineering Structures 2022;255:113898. [DOI: 10.1016/j.engstruct.2022.113898] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Truong GT, Choi K, Kim C. Implementation of boosting algorithms for prediction of punching shear strength of RC column footings. Structures 2022;46:521-538. [DOI: 10.1016/j.istruc.2022.10.085] [Reference Citation Analysis]
2 Shen L, Shen Y, Liang S. Reliability Analysis of RC Slab-Column Joints under Punching Shear Load Using a Machine Learning-Based Surrogate Model. Buildings 2022;12:1750. [DOI: 10.3390/buildings12101750] [Reference Citation Analysis]
3 Shenggang C, Quanquan G, Yingying Z, Hexiang H, Bei S. Machine learning models for cracking torque and pre-cracking stiffness of RC beams. Archiv Civ Mech Eng 2023;23. [DOI: 10.1007/s43452-022-00541-2] [Reference Citation Analysis]
4 Shi T, Lou P, Zheng W, Sheng X. A hybrid approach to predict vertical temperature gradient of ballastless track caused by solar radiation. Construction and Building Materials 2022;352:129063. [DOI: 10.1016/j.conbuildmat.2022.129063] [Reference Citation Analysis]
5 Salem NM, Deifalla A. Evaluation of the Strength of Slab–Column Connections with FRPs Using Machine Learning Algorithms. Polymers 2022;14:1517. [DOI: 10.3390/polym14081517] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 10.0] [Reference Citation Analysis]