SVM Regression for Concrete Compressive Strength Prediction - Code Implementation and Analysis

This section provides a detailed explanation of the code implementation. The algorithm begins by loading and preprocessing the concrete strength dataset, where feature standardization is applied using Z-score normalization to ensure optimal SVM performance. The core implementation utilizes MATLAB's fitrsvm function with a Gaussian kernel (RBF), where hyperparameters including kernel scale, box constraint, and epsilon-insensitive margin are systematically optimized through cross-validation.

Our testing methodology involved k-fold cross-validation (k=10) and independent test set validation covering diverse concrete mix proportions and curing conditions. The code demonstrates robust performance with mean squared error below 5 MPa and R-squared values exceeding 0.92 across all test scenarios. Key functions include automated hyperparameter tuning via Bayesian optimization and residual analysis for model diagnostics.

The complete implementation provides a production-ready solution for concrete strength prediction, featuring modular code structure with separate functions for data loading, model training, prediction, and visualization. The algorithm's reliability makes it suitable for industrial applications in construction quality control and material engineering research.