Active learning inspired multi-fidelity probabilistic modelling of geomaterial property

The identification of geomaterial properties is costly but pivotal for engineering design. A wide range of approaches perform well with sufficiently measured data but their performance is problematic for sparse data. To address this issue, this study proposes an active learning based multi-fidelity residual Gaussian process (AL-MR-GP) modelling framework. A low-fidelity (LF) prediction model is first trained using extensive LF data collected from worldwide sites to generate preliminary estimations. Subsequent training employs active learning to prioritize high-fidelity data from the specific site of interest with larger information gain for calibrating the LF model to make ultimate predictions. The compression index of clays is selected as an example to examine the capability of the proposed framework. The results indicate that using the same number of site-specific datasets, the compression index of clays can be well captured by AL-MR-GP, exhibiting superior accuracy and reliability than models without incorporating multi-fidelity data or active learning. Based on unified LF data, the proposed framework becomes data-efficient for the model development of three sites and is significantly competitive in extrapolation, compared with site-specific models even with active learning. These promising characteristics indicate substantial potential to be extended to broader applications in geotechnical engineering.