What geotechnical engineers want to know about reliability

KK Phoon - ASCE-ASME Journal of Risk and Uncertainty in …, 2023 - ascelibrary.org
The purpose of this paper is to address the “what,”“why,” and “how” questions posed by
engineers who are not familiar with geotechnical reliability and have not kept abreast of …

Time capsule for geotechnical risk and reliability

M Chwała, KK Phoon, M Uzielli, J Zhang… - … and management of …, 2023 - Taylor & Francis
This paper is motivated by the Time Capsule Project (TCP) of the International Society for
Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of …

Bayesian neural network-based uncertainty modelling: application to soil compressibility and undrained shear strength prediction

P Zhang, ZY Yin, YF Jin - Canadian Geotechnical Journal, 2022 - cdnsciencepub.com
This study adopts the Bayesian neural network (BNN) integrated with a strong non-linear
fitting capability and uncertainty, which has not previously been used in geotechnical …

The story of statistics in geotechnical engineering

KK Phoon - Georisk: Assessment and Management of Risk for …, 2020 - Taylor & Francis
The story of statistics in geotechnical engineering can be traced to Lumb's classical
Canadian Geotechnical Journal paper on “The Variability of Natural Soils” published in …

Characterization of autocovariance parameters of detrended cone tip resistance from a global CPT database

J Ching, M Uzielli, KK Phoon, X Xu - Journal of Geotechnical and …, 2023 - ascelibrary.org
This paper compiles a cone penetration test (CPT) database, named Global-CPT/3/1196. It
contains three CPT parameters (cone tip resistance, sleeve friction, and porewater pressure) …

Hybrid machine learning model with random field and limited CPT data to quantify horizontal scale of fluctuation of soil spatial variability

JZ Zhang, DM Zhang, HW Huang, KK Phoon, C Tang… - Acta Geotechnica, 2022 - Springer
The scale of fluctuation (SOF) is the critical parameter to describe the soil spatial variability,
which significantly influences the embedded geostructures. Due to the limited data in the …

[PDF][PDF] Managing risk in geotechnical engineering–from data to digitalization

KK Phoon, J Ching, Y Wang - Proc., 7th Int. Symp. on …, 2019 - rpsonline.com.sg
If you scan a page from a soil report, this is called digitization. If you deploy digital
technologies, both software such as building information modeling and machine learning …

[HTML][HTML] Quasi-site-specific soil property prediction using a cluster-based hierarchical Bayesian model

S Wu, J Ching, KK Phoon - Structural Safety, 2022 - Elsevier
One of the important goals in geotechnical engineering is to learn the correlation
relationships between different soil properties in order to make predictions for a new site of …

Interpolation and stratification of multilayer soil property profile from sparse measurements using machine learning methods

T Zhao, Y Wang - Engineering Geology, 2020 - Elsevier
Identification of subsurface stratification and characterization of spatially varying soil
properties profiles in multiple soil layers are indispensable in geotechnical site investigation …

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 …