Accurate corrosion-rate prediction for buried pipelines is fundamental to sustainable integrity management, yet industrial corrosion datasets are typically small and heterogeneous, making reliable model training challenging. This study proposes CARE-Boost (Context-Aware Restrained-Ensemble Boosting), a compact method designed for exactly this setting. The algorithm fuses three complementary components: a practical-variable gradient-boosting branch trained on directly measurable pipeline predictors; a spatial-neighborhood context branch that encodes short-range continuity from adjacent stake-point predictors; and a restrained regime-focused augmentation scheme stabilized by fixed convex blending. The engineering dataset was collected from a natural-gas pipeline in Central Asia and organized as a one-dimensional spatial sequence. Under repeated 5×2 cross-validation, CARE-Boost achieves RMSE =0.0577mm/year, MAE =0.0314mm/year, and R2=0.472, outperforming XGBoost (0.0599, 0.0320, 0.432) and LightGBM (0.0618, 0.0333, 0.385); the improvement over XGBoost is statistically significant (p=0.0068, splitwise Wilcoxon). Split-conformal intervals achieve 95.0% empirical coverage at the nominal 90% level. SHAP attribution identifies soil aggressiveness, pH, water content, and bicarbonate as the dominant corrosion drivers, and the mean fit–predict cycle completes in 1.80 s, supporting deployment in routine integrity workflows. These findings position CARE-Boost as a practically viable uncertainty-aware corrosion predictor for sustainable integrity management under small-sample conditions, with its primary evidence lying in improved point prediction, calibrated uncertainty, and interpretable spatially informed inference.
Liu et al. (2026) studied this question.