Benchmark examples for data-driven site characterisation

Kok‐Kwang Phoon

doi:10.1080/17499518.2022.2025541

Benchmark examples for data-driven site characterisation

2022-01-13

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AI

DOI：10.1080/17499518.2022.2025541

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Decision making in geotechnical engineering is always related to a project carried out at a specific site. It is natural for data-driven site characterization (DDSC) to attract the most attention in data-centric geotechnics. This paper proposed eight benchmark examples and a benchmarking procedure to support unbiased and competitive evaluation of emerging ML methods. The primary goal of DDSC is to bring the value of a data first agenda to practice, specifically to produce a 3D stratigraphic map of the subsurface volume below a full-scale project site and to estimate relevant engineering properties at each spatial point based on site investigation data and other relevant Big Indirect Data (BID). A reasonable full-scale ground 20 m long x 20 m wide x 10 m deep is adopted. Virtual grounds containing horizontal, inclined, or discontinuous soil layers and spatially varying synthetic cone penetration test data are created to test the performance of DDSC methods over a range of ground conditions. A benchmark example is defined by a combination of a virtual ground (reality) and a training dataset (measured reality). An additional benchmark example based on actual CPT data is included to check whether performance under virtual ground conditions holds under real ground conditions.

Keywords:

data-driven site characterisation (DDSC)

benchmark examples

data-centric geotechnics

virtual ground

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Benchmark examples for data-driven site characterisation

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