Benchmarking Conventional Machine Learning Models for Dynamic Soil Property Prediction

Reliable estimates of soil stiffness and energy dissipation are essential for dynamic-response design. This study benchmarks machine learning models for predicting shear modulus (G) and damping ratio (D) using 2738 resonant-column measurements. After data quality control and F-test feature screening, five model families—decision trees and ensembles, support-vector machines, Gaussian-process regression, neural networks, and linear baselines—were trained under uniform 10-fold cross-validation and evaluated with R², RMSE, MAE, and MSE, while recording training time to reflect practical constraints. Results show that model choice materially affects performance. For G, a bagged ensemble of trees delivered the best accuracy (R² = 0.9827) with short training times; single trees provided transparent, fast screening models. For D, tree-based ensembles again performed strongly (R² up to 0.8565), while a rational-quadratic Gaussian-process model offered competitive accuracy (R² ≈ 0.81) together with prediction intervals that support risk-aware design. Feature influence aligned with soil mechanics: G was most sensitive to effective confining pressure (σ′0), initial void ratio (e0), and density (ρ); D was governed mainly by overconsolidation ratio (OCR), depth (z), σ′0, and plasticity, with notable interactions among stress, strain amplitude (γ), and moisture state. The findings provide practice-oriented guidance: use bagged trees for routine predictions of G and D, and add Gaussian-process regression when uncertainty quantification is required. The approach complements laboratory testing and supports safer, more economical dynamic-response design.