Machine learning-aided selection of CPT-based transformation models using field monitoring data from a specific project

Hua-Ming Tian, Yu Wang*, Chao Shi

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Citations (Scopus)
11 Downloads (CityUHK Scholars)

Abstract

Transformation models have been widely used in geotechnical engineering to relate data from lab or field tests (e.g., cone penetration tests, CPT) to design parameters required in geotechnical analysis and design. Proper selection of transformation models is crucial but challenging for accurate prediction of geotechnical responses (e.g., reclamation-induced settlement) in practice. This study proposes a general machine learning framework that accommodates a wide variety of existing CPT-based transformation models and uses field monitoring data (e.g., settlement data observed from a specific project) to select suitable transformation models for improving prediction of spatiotemporally varying reclamation-induced settlement. The proposed approach takes advantage of sparse dictionary learning (SDL) and achieves prediction of settlement by a linear weighted sum of dictionary atoms that are constructed using outputs from finite element models (FEM) of reclamation-induced consolidation. Input parameters of the FEM models are determined using existing transformation models in literature. A transformation model database that relates multiple soil consolidation parameters with CPT data is also compiled for consolidation analysis and dictionary construction in SDL. The proposed approach is illustrated using a real reclamation project in Hong Kong. Results show that the proposed approach provides an effective and transparent vehicle to leverage existing abundant transformation models, identify appropriate transformation models using field monitoring data, and improve prediction of spatiotemporally varying reclamation-induced settlement, with greatly reduced prediction uncertainty. The transformation model selection and settlement prediction are also improved continuously as more field monitoring data are obtained. © The Author(s) 2024.
Original languageEnglish
Article number100605
Pages (from-to)439–459
JournalActa Geotechnica
Volume20
Issue number1
Online published2 Dec 2024
DOIs
Publication statusPublished - Jan 2025

Funding

The work described in this paper was supported by the grant from the Research Grant Council of Hong Kong Special Administrative Region (Project no. 11203322), the Ministry of Education, Singapore, under its Academic Research Fund (AcRF) Tier 1 Seed Funding Grant (Project no. RS03/23), and AcRF regular Tier 1 Grant (Project no. RG69/23). The financial support is gratefully acknowledged.

Research Keywords

  • Geotechnical transformation model
  • Machine learning
  • Model selection
  • Reclamation-induced settlement
  • Sparse dictionary learning

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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