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

Author

Listed:
  • Ming-Qing Peng

    (School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Zhi-Chao Qiu

    (School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Si-Liang Shen

    (School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Yu-Cheng Li

    (School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Jia-Jie Zhou

    (School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Hui Xu

    (School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China)

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.

Suggested Citation

  • Ming-Qing Peng & Zhi-Chao Qiu & Si-Liang Shen & Yu-Cheng Li & Jia-Jie Zhou & Hui Xu, 2024. "Geotechnical Site Characterizations Using a Bayesian-Optimized Multi-Output Gaussian Process," Sustainability, MDPI, vol. 16(13), pages 1-14, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:13:p:5759-:d:1429871
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