In the Gobi region, concrete structures frequently suffer erosion from wind gravel flow. This erosion notably impairs their longevity. Therefore, creating a predictive model for wind gravel flow-related concrete damage is crucial to proactively address and manage this problem. Traditional theoretical models often fail to predict the erosion rate of concrete (CER) structures accurately. This issue arises from oversimplified assumptions and the failure to account for environmental variations and complex nonlinear relationships between parameters. Consequently, a single traditional model is inadequate for predicting the CER under wind gravel flow conditions in this region. To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. The support vector machine (SVM) model demonstrates superior predictive performance, evidenced by its R² value nearing one and a notable reduction in RMSE 1.123 and 1.573 less than the long short-term memory network (LSTM) and BP neural network (BPNN) models, respectively. Ensuring that the training set comprises at least 80% of the total data volume is crucial for the SVM model’s prediction accuracy. Moreover, erosion time is identified as the most significant factor affecting the CER. An enhanced theoretical erosion model, derived from the Bitter and Oka framework and integrating concrete strength and erosion parameters, was formulated. It showed average relative errors of 22% and 31.6% for the Bitter and Oka models, respectively. The SVM model, however, recorded a minimal average relative error of just −0.5%, markedly surpassing these improved theoretical models in terms of prediction accuracy. Theoretical models often rely on simplifying assumptions, such as linear relationships and homogeneous material properties. In practice, however, factors like concrete materials, wind gravel flow, and climate change are nonlinear and non-homogeneous. This significantly limits the applicability of these models in real-world environments. Ultimately, the SVM algorithm is highly effective in developing a reliable prediction model for CER. This model is crucial for safeguarding concrete structures in wind gravel flow environments.

In high-elevation or high-latitude permafrost areas, persistent subzero temperatures significantly impact the freeze–thaw durability of concrete structures. Traditional methods for studying the frost resistance of concrete in permafrost regions do not provide a complete picture for predicting properties, and new approaches are needed using, for example, machine learning algorithms. This study utilizes four machine learning models—Support Vector Machine (SVM), extreme learning machine (ELM), long short-term memory (LSTM), and radial basis function neural network (RBFNN)—to predict freeze–thaw damage factors in concrete under low and subzero temperature conservation conditions. Building on the prediction results, the optimal model is refined to develop a new machine learning model: the Sparrow Search Algorithm-optimized Extreme Learning Machine (SSA-ELM). Furthermore, the SHapley Additive exPlanations (SHAP) value analysis method is employed to interpret this model, clarifying the relationship between factors affecting the freezing resistance of concrete and freeze–thaw damage factors. In conclusion, the empirical formula for concrete freeze–thaw damage is compared and validated against the prediction results from the SSA-ELM model. The study results indicate that the SSA-ELM model offers the most accurate predictions for concrete freeze–thaw resistance compared to the SVM, ELM, LSTM, and RBFNN models. SHAP value analysis quantitatively confirms that the number of freeze–thaw cycles is the most significant input parameter affecting the freeze–thaw damage coefficient of concrete. Comparative analysis shows that the accuracy of the SSA-ELMDE prediction set is improved by 15.46%, 9.19%, 21.79%, and 11.76%, respectively, compared with the prediction results of SVM, ELM, LSTM, and RBF. This parameter positively influences the prediction results for the freeze–thaw damage coefficient. Curing humidity has the least influence on the freeze–thaw damage factor of concrete. Comparing the prediction results with empirical formulas shows that the machine learning model provides more accurate predictions. This introduces a new approach for predicting the extent of freeze–thaw damage to concrete under low and subzero temperature conservation conditions.

Wind and sand pose a significant threat to operational safety along the route of the Golmud-Korla Railway. To combat the adverse effects of these hazards, numerous sand retaining dikes and sand intercepting ditches have been constructed along the railway corridor. However, the deposition and erosion mechanisms of sand particles in close proximity to these structures have yet to be fully investigated. Therefore, it uses numerical simulations to study the structure of the wind-sand flow field around the sand retaining dike and the sand intercepting ditch, under varying spacing conditions, with an analysis of sand deposition and erosion laws. The results indicate that vortices form on the leeward side and within the sand intercepting ditch. Among these, the vortex flow occurring on the downstream side of the sand retaining dike exhibits a flow reattachment phenomenon at specific locations (i.e., attachment points). As the spacing increases, clockwise vortices Rd1 and Rd2, develop on the leeward side and inside the ditch, respectively. The leeward side of the spacing range of 0–8H is characterized by reverse erosion and deposition processes. When the spacing is 10–15H, a forward erosion zone emerges and expands progressively with the increase in spacing. When the spacing exceeds 10H, i.e., as the sand intercepting ditch is positioned downstream of the vortex reattachment point of the sand retaining dike, its sand interception efficiency is markedly enhanced. It not only elucidates the wind-sand flow and deposition patterns surrounding sand retaining dike and sand intercepting ditch under various spacing configurations but also offers valuable insights for the future design and implementation of protective structures for railways in wind-sand affected regions.