English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 × 20 m wide × 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.

Benchmark examples for data-driven site characterisation