2026-10-30T03:59:32Zhttps://bayburt-rdlopen.rawdatalibrary.net/oai/request

oai:bayburt-rdlopen.rawdatalibrary.net:bayburt/112662026-10-29T19:41:30Zopenairedrivercom_bayburt_10000col_bayburt_10001com_bayburt_10007col_bayburt_10223

Experimental Electromagnetic Shielding Analysis of a Square-Resonator-Integrated Double-Concrete Structure Using Explainable Machine LearningCakir, Mehmetconcrete-based structureelectromagnetic shieldingexplainable artificial intelligencemachine learningS-parameter analysissquare resonatorElectromagnetic shielding has become a practical concern in buildings and structures exposed to persistent interference. This paper reports experimental measurements of the frequency-dependent shielding properties of a square-resonator-integrated double-concrete structure, using a free-space S-parameter setup built around WR229 waveguide adaptors and horn antennas. Three variables were tested: concrete thickness D, relative permittivity εr, and relative magnetic permeability μr. Both εr and μr were characterized experimentally from carbon-fibre- and copper-slag-modified concrete rather than taken from standard tables. The novelty of the study lies in combining experimentally characterized concrete electromagnetic properties, an embedded square-resonator geometry, and explainability-driven machine learning analysis within a single experimental framework for cement-based EMI shielding design. A total of 96 parameter combinations were evaluated using calibrated S11 and reference-corrected S21 responses across 3.3–4.9 GHz. Thickness and electromagnetic material properties interacted—neither governed shielding performance on its own. The strongest transmission attenuation occurred at D = 5, εr = 7, and μr = 1.2, where minimum S21 reached approximately −62.98 dB at 3.6392 GHz. S11 varied considerably less than S21 across the tested combinations, suggesting transmission suppression is the dominant mechanism rather than reflection enhancement. A machine learning analysis confirmed that nonlinear ensemble models outperformed the linear baseline and identified thickness as the most influential predictor of minimum S21.Multidisciplinary Digital Publishing Institute (MDPI)2026Articlehttps://rdlopen.rawdatalibrary.net/r/bayburt/11266https://doi.org/10.3390/electronics151227422079-9292enElectronics (Switzerland)info:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://bayburt-rdlopen.rawdatalibrary.net/tr/items/bayburt/11266