Construction of Quasi-Site-Specific Geotechnical Transformation Models Using Bayesian Sparse Dictionary Learning: Fid-Bau Portal

Construction of Quasi-Site-Specific Geotechnical Transformation Models Using Bayesian Sparse Dictionary Learning

Tian, Hua-Ming / Wang, Yu / Phoon, Kok-Kwang

Transformation models commonly are used in geotechnical practice to estimate design parameters (e.g., friction angle $ϕ$ ) required for geotechnical design and analysis from measurement data obtained from laboratory or in situ tests [e.g., $N$ values obtained from standard penetration tests (SPTs)]. Because many transformation models have been developed in the literature and site investigation data obtained from a specific site often are limited, it is challenging to select a suitable transformation model or develop a site-specific transformation model. To address this challenge, this study proposes a novel data-driven method called Bayesian sparse dictionary learning (SDL) for constructing a quasi-site-specific transformation model using existing transformation models from the literature and limited site-specific measurements. From a signal processing perspective, the proposed approach utilizes existing transformation models as basis functions, or atoms in SDL, and employs limited site-specific data to select nontrivial atoms for construction of a quasi-site-specific model and prediction. Existing transformation models and limited site-specific data are leveraged effectively in a systematic and coherent manner. Prediction uncertainty arising from limited site data is quantified under a Bayesian framework. Illustrative examples showed that the proposed approach efficaciously constructs a quasi-site-specific transformation model (e.g., a $ϕ$ versus SPT- $N$ model) and outperforms existing transformation models and traditional methods in terms of greatly reduced prediction uncertainty and significantly improved model fidelity, e.g., both interpolation and extrapolation of design parameters (e.g., $ϕ$ ) from measurement data (e.g., SPT $N_{60}$ ).