Ahmad Ghattas

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.