## 1 Introduction
Additionally, the literature found that the internal corrosive medium diffusion process and post-erosion structural performance degradation of concrete sewage pipes have spatio-temporal effects [26; 27; 28]. These studies demonstrate the significance of integrating spatio-temporal correlation features in pipeline corrosion prediction and also emphasize the necessity of quantitative research on the spatio-temporal changes in the pipeline corrosion process, which is currently under-researched. To address the above problems, this paper proposes a corrosion rate prediction framework based on depth-adaptive MTGNN [29], from the perspective of pipeline corrosion spatio-temporal correlation. First, the corrosion monitoring points at different locations inside the pipeline are regarded as nodes in the non-Euclidean spatial structure graph, and the auxiliary features are screened with correlation analysis to construct the node input sequence. Then, the graph convolution module and the temporal convolution module are used to extract the spatial and temporal features of the nodes respectively, and the training strategy of \"chunked progressive\" is adopted in the multi-step prediction, so as to realize the short-term prediction of the node corrosion rate by integrating the multi-dimensional corrosion information. Finally, the internal corrosion monitoring data of concrete pipes from two different cities are taken as an example to verify the superiority of the prediction method proposed in this paper. The results show that compared with the traditional time series prediction model, the adaptive MTGNN considering spatio-temporal correlation shows a greater improvement in prediction accuracy and stability. The main contributions of this study can be summarized as follows:  
1. Dynamic spatio-temporal graph modeling framework: A multi-feature spatio-temporal correlation analysis framework based on adaptive MTGNN is proposed, which innovatively introduces the dynamic graph learning mechanism into the field of pipeline corrosion prediction. The spatial heterogeneity of pipeline monitoring points is modeled by the adaptive adjacency matrix, and multi-scale temporal dynamic features are captured by combining with time domain convolution, which breaks through the limitation of the traditional temporal model that ignores the spatial correlation. 2. Multi-physical feature fusion: In this study, strong correlation auxiliary features such as corrosion current and concrete resistivity are integrated by carrying out correlation coefficient analysis, which significantly improves the model's ability to characterize the coupling effects of complex environments compared with a single feature. 3. Progressive training strategy: A \"chunked progressive\" training strategy is proposed to expand the prediction horizons in phases, which effectively alleviates the error accumulation problem in multi-step prediction. 4. Verification of cross-engineering generalization capability: In view of the shortcomings of existing studies that rely on single-system data, cross-engineering tests on prestressed steel-cylinder concrete pipelines in coastal areas are added. Experiments show that the prediction framework proposed in this paper has good adaptability to similar projects.