Research on Concrete Compressive Strength Prediction Based on DE-Optimized LSSVM and Multi-Level Heterogeneous Ensemble Residual Fusion

Concrete compressive strength is critical to structural safety, durability, and material cost. Conventional machine learning models are often limited in capturing complex nonlinear dependencies and generalizing. To address this, a residual fusion framework is proposed that combines a least squares support vector machine (LSSVM) optimized by DE with multi-level residual structure bagged decision trees (TreeBagger) and least squares boosting (LSBoost). DE-tuned LSSVM hyperparameters are followed by a multi-level residual scheme that compensates errors layer by layer, with LSBoost performing adaptive nonlinear fusion. Experiments under varied splits, ablation, and multiple seeds show the model outperforms traditional single and ensemble methods in accuracy, generalization, and stability. The ablation attributes the improvements to complementary residual mechanisms and the fusion architecture, rather than simply adding learners. Across multiple runs, an average coefficient of determination (R2) of 0.9490, a mean absolute error (MAE) of 3.7873 MPa, a root mean square error (RMSE) of 2.4998 MPa, and an R2 standard deviation of 0.0029 were obtained, confirming stability. Shapley additive explanations (SHAP) analysis further reveals that age and water–cement parameters dominate, with patterns consistent with hydration and water–binder theory. The proposed framework thus offers high accuracy, physical interpretability, and engineering applicability.