Concrete Compressive Strength Prediction System Using K-Nearest Neighbor (KNN) Regression with k-Parameter Tuning Implementation
DOI:
https://doi.org/10.54065/likelihood.1461Keywords:
Concrete Compressive Strength, KNN Regression, K-Parameter Tuning, Machine Learning, Concrete PredictionAbstract
Concrete compressive strength is a principal indicator of concrete quality, but standardized testing requires specimen preparation, controlled curing, equipment, and time. This study develops and internally evaluates a K-Nearest Neighbor (KNN) regression model for rapid strength estimation from mixture composition and curing age. The UCI Concrete Compressive Strength dataset contained 1,030 records; removing 25 repeated records yielded 1,005 unique observations with eight original predictors and compressive strength as the response. LogAge, Water-Cement Ratio, and Total Aggregate were added as engineered predictors. The cleaned data were divided into 80% training and 20% testing subsets, while data-dependent preprocessing and hyperparameter selection were performed using the training data only. Odd k values from 1 to 41 were assessed by 10-fold cross-validation repeated five times. The minimum cross-validated error occurred at k = 3. On the held-out test subset, the final model achieved an R² of 0.88, RMSE of 5.56 MPa, and MAE of 3.74 MPa. LogAge, Water-Cement Ratio, and cement content had the largest absolute correlations with strength, although these correlations are descriptive rather than KNN-specific importance measures. The results show that a tuned and standardized KNN model can provide useful preliminary strength estimates within the represented data domain. However, the model is intended to support mixture screening and quality-control decisions, not replace standardized laboratory testing. External validation and comparison with alternative algorithms are required before broader deployment.
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