A hybrid residual correction framework that integrates a physics-based carbonation model with a stacked ensemble of machine learning algorithms: gradient boosted regression trees (GBRT), support vector regression (SVR), and Gaussian process regression (GPR), combined through an XGBoost metamodel, demonstrating that the...
A novel hybrid method integrating physics-informed neural network (PINN) and Gaussian process regression (GPR) that enforces the B4 creep model as a physics-informed constraint by embedding its governing equations into the loss function, effectively incorporating physical knowledge into data-driven training.
This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soi...
Uniaxial compressive strength (UCS) is critical for geotechnical design, yet conventional testing is costly and empirical formulas fail to adequately capture inherent nonlinearities. This study proposes an interpretable predictive framework that combines a two-stage heterogeneous stacking ensemble with SHapley Additi...
This study develops a physics-guided machine learning framework for predicting frozen soil thermal conductivity using a multisource database that improves data efficiency and predictive stability, especially for generalization to unseen soil specimens.
This study proposes a Gaussian Process Regression (GPR) method for estimating the compressive strength of soil mixtures containing bentonite and basalt fibers. GPR is a preferred probabilistic machine learning approach, especially for limited datasets, due to its high generalization ability and its ability to directly...
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