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 …
engineers who are not familiar with geotechnical reliability and have not kept abreast of …
Time capsule for geotechnical risk and reliability
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 …
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 …
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 …
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
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) …
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
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 …
which significantly influences the embedded geostructures. Due to the limited data in the …
[PDF][PDF] Managing risk in geotechnical engineering–from data to digitalization
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 …
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
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 …
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
Identification of subsurface stratification and characterization of spatially varying soil
properties profiles in multiple soil layers are indispensable in geotechnical site investigation …
properties profiles in multiple soil layers are indispensable in geotechnical site investigation …
Stochastic stratigraphic modeling using Bayesian machine learning
Stratigraphic modeling with quantified uncertainty is an open question in engineering
geology. In this study, a novel stratigraphic stochastic simulation approach is developed by …
geology. In this study, a novel stratigraphic stochastic simulation approach is developed by …