Due to its ability to improve mechanical properties when incorporated into cement, graphene oxide (GO) has received extensive attention from scholars. Graphene oxide is also a filler that improves the self-sensing properties of cement composites (CCs). However, existing studies have not focused sufficient attention on the electric conductivity of cement composites filled with graphene oxide (GO/CCs) and their mechanisms, especially polarization. This study examines the effects of water content and temperature on the electrical conductivity of GO/CCs. GO/CC polarization phenomena are analyzed to reveal the conductive mechanism. The results show that water has a significant influence on the electrical conductivity of GO/CCs. With increasing water loss, the electrical resistivity of GO/CCs increases by four orders of magnitude. For the same water content, a 0.1% GO concentration significantly decreases the resistivity of GO/CCs. Temperature can significantly enhance the current intensity of GO/CCs; furthermore, there is a quadratic relationship between current intensity and temperature. The conductive mechanism of GO/CCs is attributed to the interaction between ionic conductivity and electronic conductivity.

Ultrasonic guided wave (UGW) inspection of small-diameter pipes is complicated by the narrow usable frequency band and by strong susceptibility to measurement noise, both of which degrade the reliability of damage identification. This study proposes a multi-channel attention Mamba-2 (MCA-Mamba-2) network that couples a convolutional front end with a bidirectional Mamba-2 state-space backbone and combines multi-head self-attention with channel attention for feature fusion. The network takes as input the four waveforms recorded simultaneously by a circumferential receiver array so that the input tensor has four physical channels that are subsequently expanded into learned feature channels. A dataset of ten damage states, spanning non-through-wall abrasions, through-wall holes and one multi-defect case, was acquired on a 20 mm outer-diameter steel pipe specimen. Averaged over three independent runs, MCA-Mamba-2 reaches 98.01 ± 0.85% accuracy on noise-free signals and 81.09 ± 1.74%, 83.65 ± 1.34%, 82.49 ± 0.20% and 84.15 ± 1.04% on 0 dB test sets contaminated with white, pink, Gaussian and Laplacian noise respectively, exceeding the Mamba-2 baseline in all sixteen tested noise-type and SNR combinations, by up to 17.87 percentage points. An ablation study attributes the improvement principally to the convolutional mixing block. A cross-noise evaluation reveals that accuracy drops by 59.80 percentage points, on average, at 0 dB when the training and testing noise distributions differ, indicating that the matched-noise accuracies are not indicative of robustness to unknown noise conditions; however, a single model trained on a mixture of all noise types and SNR levels recovers an average accuracy of 91.21% across the sixteen test conditions. Because each damage state was recorded in a single acquisition session, the dataset was partitioned at the level of individual records rather than by session, and the reported accuracies should be interpreted as an upper bound on the performance attainable on independently acquired data. The results were obtained on a single pipe geometry with fixed defect positions and synthetically added noise; extension to other geometries and to field measurements remains to be demonstrated.