This article presents two innovative methods to enhance electromagnetic (EM) shielding in nonbearing masonry walls constructed from normal-weight concrete (NC) hollow blocks. Two different block sizes are investigated, 400 × 200 × 200 mm³ and 400 × 200 × 150 mm³. The first method involves filling hollow NC blocks with either NC or two conductive concrete mixes (M1 and M2), incorporating carbon and fiber additives. It achieves shielding effectiveness of 20–36 dB across 1–10 GHz, significantly exceeding that of NC-filled blocks (1–25 dB). The second method applies a 2 cm fiber-reinforced conductive composite (FRCC) coating, in planar or corrugated configurations, to one or both sides of masonry walls. Double-sided FRCC coatings, particularly with corrugated surfaces, deliver superior SE up to 50 dB, compared to 10–25 dB for uncoated walls, with scattering reducing reflections. Simulations using ANSYS high-frequency structure simulator confirm these findings, indicating power transmission of less than 1% for FRCC-coated walls and up to 96% absorption for conical corrugated designs. Both methods demonstrate robust EM-shielding across diverse frequencies and illumination directions, offering practical solutions for nonbearing masonry walls in civil engineering applications requiring enhanced EM interference protection.

This study investigates the application of Ka-band (35 GHz) microwave non-destructive testing (NDT) to detect and classify surface-breaking defects in metallic pipelines and both internal and external defects in non-metallic pipelines. A microwave scanning system utilizing an open-ended rectangular waveguide sensor collects 420 one-dimensional voltage line-scan measurements across six defect types (hole, cut, corrosion, internal wear, external wear, and inclusion) in PVC, HDPE, and metallic pipe specimens at three standoff distances (1 mm, 2 mm, and 3 mm). A one-dimensional convolutional neural network (1D CNN), trained with SMOTE resampling and $4\times$ data augmentation, is evaluated under a grouped protocol in which all six scans of any physical defect specimen are confined to a single partition. The network takes the scan through a convolutional branch and receives the standoff distance and material code as separate auxiliary inputs. Grouped five-fold cross-validation, repeated five times over independent partitions, gives a mean accuracy of 82.19 ± 5.93% with a macro-averaged F1-score of 0.85. Errors are concentrated among hole, cut, and external wear, while corrosion, inclusion, and internal wear are recovered with recall at or above 0.90. Validation used artificially introduced, isolated defects in laboratory specimens under controlled acquisition conditions. These results establish laboratory feasibility for Ka-band microwave NDT combined with a 1D CNN as a noninvasive route for automated pipeline defect classification, and show that a learned classifier resolves signal ambiguity that makes manual interpretation of amplitude-only microwave sensor outputs difficult.