Data-driven spatio-temporal analysis of consolidation for rapid reclamation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Original languageEnglish
Journal / PublicationGeotechnique
Online published21 Feb 2023
Publication statusOnline published - 21 Feb 2023


Spatial extension of lands through rapid reclamation is attractive for congested coastal megacities, although reclamations might suffer from project delay and budget overrun, often due to encountering of unforeseen ground conditions. To accelerate reclamations, accurate prediction of soil consolidation over a construction duration of multiple years is needed for reclaimed lands, which often contain spatially varying subsurface stratigraphy and soil parameters. This calls for a spatio-temporal analysis of consolidation with a sound understanding of subsurface stratigraphic alternations of fine/coarse-grained soils and spatial variability of consolidation parameters (e.g., permeability). In this study, a unified framework, capable of simultaneously modelling stratigraphic variation and spatial variability of soil properties through machine learning of limited site investigation data, is combined with finite element method and Monte Carlo simulation for spatio-temporal consolidation analysis of reclaimed lands. The proposed method is applied to a real reclamation project in Hong Kong. Results indicate that the proposed method can accurately characterize subsurface geological cross-sections and spatially varying soil permeability with quantified uncertainty. The ignorance of spatial variability of soil permeability may result in an underestimation of consolidation time and an overestimation of undrained shear strength gain, thus pose significant risks to reclamation projects. © 2023 Thomas Telford Ltd.

Research Area(s)

  • Machine learning, Probabilistic analysis, Random finite element analysis, Soil spatial variability, Stratigraphic uncertainty