 Artificial Intelligence Applied to Soil Compaction Control for the Light Dynamic Penetrometer MethodJorge Rojas-Vivanco (),
José García (),
Gabriel Villavicencio,
Miguel Benz,
Antonio Herrera,
Pierre Breul,
German Varas,
Paola Moraga,
Jose Gornall and
Hernan Pinto
Mathematics, 2025, vol. 13, issue 21, 1-34 Abstract: Compaction quality control in earthworks and pavements still relies mainly on density-based acceptance referenced to laboratory Proctor tests, which are costly, time-consuming, and spatially sparse. Lightweight dynamic cone penetrometer (LDCP) provides rapid indices, such as q d 0 and q d 1 , yet acceptance thresholds commonly depend on ad hoc, site-specific calibrations. This study develops and validates a supervised machine learning framework that estimates q d 0 , q d 1 , and Z c directly from readily available soil descriptors (gradation, plasticity/activity, moisture/state variables, and GTR class) using a multi-campaign dataset of n = 360 observations. While the framework does not remove the need for the standard soil characterization performed during design (e.g., W , γ d , field , and R C SPC ), it reduces reliance on additional LDCP calibration campaigns to obtain device-specific reference curves. Models compared under a unified pipeline include regularized linear baselines, support vector regression, Random Forest, XGBoost, and a compact multilayer perceptron (MLP). The evaluation used a fixed 80/20 train–test split with 5-fold cross-validation on the training set and multiple error metrics ( R 2 , RMSE, MAE, and MAPE). Interpretability combined SHAP with permutation importance, 1D partial dependence (PDP), and accumulated local effects (ALE); calibration diagnostics and split-conformal prediction intervals connected the predictions to QA/QC decisions. A naïve GTR-average baseline was added for reference. Computation was lightweight. On the test set, the MLP attained the best accuracy for q d 1 ( R 2 = 0.794 , RMSE = 5.866 ), with XGBoost close behind ( R 2 = 0.773 , RMSE = 6.155 ). Paired bootstrap contrasts with Holm correction indicated that the MLP–XGBoost difference was not statistically significant. Explanations consistently highlighted density- and moisture-related variables ( γ d , field , R C SPC , and W ) as dominant, with gradation/plasticity contributing second-order adjustments; these attributions are model-based and associational rather than causal. The results support interpretable, computationally efficient surrogates of LDCP indices that can complement density-based acceptance and enable risk-aware QA/QC via conformal prediction intervals. Keywords: compaction control; dynamic penetrometer; soils; machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:21:p:3359-:d:1776728 Access Statistics for this article Mathematics is currently edited by Ms. Emma He More articles in Mathematics from MDPI |
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