[HTML][HTML] Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review

R He, W Zhang, J Dou, N Jiang, H **ao, J Zhou - Rock Mechanics Bulletin, 2024 - Elsevier
Landslides are one of the geological disasters with wide distribution, high impact and
serious damage around the world. Landslide risk assessment can help us know the risk of …

GFII: A new index to identify geological features during shield tunnelling

T Yan, SL Shen, A Zhou - Tunnelling and Underground Space Technology, 2023 - Elsevier
Geological features play an essential role in ensuring the safety and enhancing the
construction efficiency of shield tunnelling. However, owing to the concealed nature of the …

Probabilistic analysis and design of stabilizing piles in slope considering stratigraphic uncertainty

W Gong, H Tang, H Wang, X Wang, CH Juang - Engineering Geology, 2019 - Elsevier
The uncertainty involved in the interpreted geological model may be categorized as the
stratigraphic uncertainty and the properties uncertainty. Note that although the influence of …

Statistical interpretation of soil property profiles from sparse data using Bayesian compressive sampling

Y Wang, T Zhao - Géotechnique, 2017 - icevirtuallibrary.com
In geotechnical engineering, the number of measurement data obtained from in situ or
laboratory tests is usually sparse, especially for projects of small or medium size …

Probabilistic slope stability analysis: state-of-the-art review and future prospects

R Chakraborty, A Dey - Innovative Infrastructure Solutions, 2022 - Springer
Conventionally adopted deterministic slope stability analyses do not consider the influence
of uncertainties related to geotechnical properties as well as failure mechanism in slope …

Data-driven and physics-informed Bayesian learning of spatiotemporally varying consolidation settlement from sparse site investigation and settlement monitoring …

H Tian, Y Wang - Computers and Geotechnics, 2023 - Elsevier
A digital twin of a geotechnical project (eg, a reclamation or ground improvement project) is
a virtual model that aims to continuously learn from actual observations (eg, site …

Estimating locations of soil–rock interfaces based on vibration data during shield tunnelling

SL Shen, T Yan, A Zhou - Automation in Construction, 2023 - Elsevier
This paper proposed an approach for estimating the locations of the soil–rock interfaces
(SRI) based on vibration data during shield tunnelling. Vibration data were collected using …

Stochastic stratigraphic modeling using Bayesian machine learning

X Wei, H Wang - Engineering Geology, 2022 - Elsevier
Stratigraphic modeling with quantified uncertainty is an open question in engineering
geology. In this study, a novel stratigraphic stochastic simulation approach is developed by …

Data-driven development of three-dimensional subsurface models from sparse measurements using Bayesian compressive sampling: A benchmarking study

B Lyu, Y Hu, Y Wang - ASCE-ASME Journal of Risk and Uncertainty …, 2023 - ascelibrary.org
With the rapid development of computing and digital technologies recently, three-
dimensional (3D) subsurface models for accurate site characterization have received …

Nonparametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics

C Shi, Y Wang - Canadian Geotechnical Journal, 2021 - cdnsciencepub.com
An essential task in many geotechnical projects is delineation of subsurface soil stratigraphy
from scatter measurements. Geotechnical engineers often use their knowledge on local …