## 1 Introduction
This technology mainly relies on algorithmic frameworks and mathematical models, enabling computers to possess intelligent analytical capabilities to complete complex tasks, thereby achieving accurate trend prediction and decision support. In the civil engineering industry, machine learning has become an important tool for all kinds of prediction work by virtue of its superior data mining and intelligent analysis advantages and has shown significant application value from the study of the durability evolution of building materials to the long-term performance monitoring and evaluation of large structures [13,14]. Specifically, [PERSON] et al. [15] collected 420 sets of data to construct GA-BP and PSO-BP models to predict the compressive strength of coral aggregate concrete (CAC-CS) and analyzed influencing factors through SHAP and univariate fluctuation analysis. The results show that the GA-BP model outperforms other tested models in five evaluation indicators, providing effective guidance for the practical application of CAC. [PERSON] et al. [16] proposed using a multilayer perceptron (MLP) model to predict pile bearing capacity (PBC) and optimized it by combining the African Vulture Optimization algorithm (AVO) and the Reptile Search Algorithm (RSA). Experiments were conducted using a dataset containing various soil and pile characteristic parameters, and the results showed that the optimized model significantly improved prediction accuracy. The determination coefficients (R\({}^{2}\)) of the MLPS2 (MLP + RSA in the second layer of MLP) and MLAV2 (MLP + AVO in the second layer of MLP) models reached 0.995 and 0.988, respectively. In the field of pipeline corrosion prediction, [PERSON] et al. [17] used the random forest algorithm to rank the importance of each corrosion factor and established a prediction model for the corrosion product category and corrosion rate of ferrosulfur corrosion. [PERSON] et al. [18] developed a new data-driven model by combining the kernel principal component analysis (KPCA) and Bayesian regularized artificial neural network (BRANN), which can eliminate the redundant information in the original monitoring data and improve the model's robustness through regularization constraints. In recent years, deep learning techniques have demonstrated remarkable potential in time series forecasting applications, particularly in addressing complex corrosion prediction challenges. Compared with traditional machine learning approaches, deep learning architectures exhibit superior capabilities in processing high-dimensional datasets, automatically extracting hierarchical features, and capturing long-term temporal dependencies through recurrent or attention-based mechanisms [19, 20, 21, 22, 23]. These advantages effectively overcome the limitations of conventional prediction methods that rely on linear assumptions and struggle with nonlinear temporal correlations in corrosion evolution processes. The above studies are mainly oriented to the modeling of a single monitoring point, and the pipeline corrosion prediction is regarded as a time series prediction problem, without fully considering the mutual influence of the corrosion state changes at different locations. However, many scholars have found the presence of spatio-temporal correlation effects during the corrosion evolution process through experiments. [PERSON] et al. [24] found the existence of time and space dependence of competitive antagonism in combined chloride-sulfate erosion and proposed a model considering the spatial and temporal correlation of the coupled erosion to study its diffusion reaction process, which solved the problem that the previous static model could not capture the spatial and temporal changes of ions and the evolution of microstructures. [PERSON] [25] proposed a stochastic procedure based on a gamma process and covariance for predicting the cracking time of concrete cover due to inhomogeneous corrosion, considering the temporal and spatial variations of the corrosion process.