Data-driven geotechnical site recognition using machine learning and sparse representation

doi:10.1016/j.enggeo.2024.107893

Data-driven geotechnical site recognition using machine learning and sparse representation

2025-02-01

2

PRE

AI

DOI：10.1016/j.enggeo.2024.107893

Original

Original request for help

Save

Abstract

En 中文

To harness available generic geotechnical databases (i.e., the so-called big indirect data) as a supplement to sparse site-specific geotechnical data from a given site, it is crucial to first address the site recognition challenge (i.e., identification of sites similar to a target site from the generic database). Existing methods often quantify site similarity based solely on the multivariate distribution (or cross-correlations) of geotechnical properties, without accounting for similarity in spatial variation of geotechnical properties among different sites, potentially resulting in incomplete identification outcomes. To overcome this limitation, this study proposes a novel site recognition method for automatically identifying sites similar to a target site from a generic geotechnical database, based on similarity in spatial variation of geotechnical properties among different sites in a data-driven manner. In the proposed method, spatial variation basis modes of geotechnical properties for different sites are first extracted from existing geotechnical databases using machine learning methods. Then, geotechnical data from the target site is used to identify the site with similar spatial variation patterns from the databases using sparse representation and sparsity-promotion techniques. The effectiveness of the proposed method is demonstrated using a real geotechnical database (i.e., the ISSMGE TC304 database).

Keywords:

Geotechnical site characterization

Spatial variability

Site recognition

Sparse representation

Proper orthogonal decomposition

Data-driven geotechnical site recognition using machine learning and sparse representation

Abstract

Journal

Organization