Geotechnical Site Characterizations Using a Bayesian-Optimized Multi-Output Gaussian Process

Abstract

Geotechnical site characterizations aim to determine site-specific subsurface profiles and provide a comprehensive understanding of associated soil properties, which are important for geotechnical engineering design. Traditional methods often neglect the inherent cross-correlations among different soil properties, leading to high bias in site characterization interpretations. This paper introduces a novel data-driven site characterization (DDSC) method that employs the Bayesian-optimized multi-output Gaussian process (BO-MOGP) to capture both the spatial correlations across different site locations and the cross-correlations among various soil properties. By considering the dual-correlation feature, the proposed BO-MOGP method enhances the accuracy of predictions of soil properties by leveraging information as much as possible across multiple soil properties. The superiority of the proposed method is demonstrated through a simulated example and the case study of a Taipei construction site. These examples illustrate that the proposed BO-MOGP method outperforms traditional methods that fail to consider both types of correlations, as evidenced by the reduced prediction uncertainty and the accurate identification of cross-correlations. Furthermore, the ability of the proposed BO-MOGP method to generate conditional random fields supports its effectiveness in geotechnical site characterizations.