Comprehensive review of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges
W Zhang, X Gu, L Hong, L Han, L Wang - Applied Soft Computing, 2023 - Elsevier
Geotechnical reliability analysis provides a novel way to rationally take the underlying
geotechnical uncertainties into account and evaluate the stability of geotechnical structures …
geotechnical uncertainties into account and evaluate the stability of geotechnical structures …
[HTML][HTML] Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies
Shallow tunnels in urban areas are close to adjacent buildings and municipal pipelines.
Ground surface settlement (GSS) due to tunnelling can cause damage to those …
Ground surface settlement (GSS) due to tunnelling can cause damage to those …
Prediction of wall deflection induced by braced excavation in spatially variable soils via convolutional neural network
Recently, the random field finite element method (RF-FEM) has attracted significantly
increasing attention in the field of geotechnical engineering, especially for the purpose of …
increasing attention in the field of geotechnical engineering, especially for the purpose of …
[HTML][HTML] Interpreting random fields through the U-Net architecture for failure mechanism and deformation predictions of geosystems
The representation of spatial variation of soil properties in the form of random fields permits
advanced probabilistic assessment of slope stability. In many studies, the safety margin of …
advanced probabilistic assessment of slope stability. In many studies, the safety margin of …
A new active learning Kriging metamodel for structural system reliability analysis with multiple failure modes
SY Huang, SH Zhang, LL Liu - Reliability Engineering & System Safety, 2022 - Elsevier
Recently, the active learning Kriging (ALK) metamodel has proved to be an efficient method
for structural system reliability analysis with multiple failure modes. A key step for enhancing …
for structural system reliability analysis with multiple failure modes. A key step for enhancing …
[HTML][HTML] Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil
The random finite difference method (RFDM) is a popular approach to quantitatively
evaluate the influence of inherent spatial variability of soil on the deformation of embedded …
evaluate the influence of inherent spatial variability of soil on the deformation of embedded …
Horizontal in situ stresses prediction using a CNN-BiLSTM-attention hybrid neural network
Horizontal in situ stress prediction plays an important role in petroleum exploration, such as
wellbore stability analysis and hydraulic fracturing design. However, owing to the …
wellbore stability analysis and hydraulic fracturing design. However, owing to the …
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 …
A maximum entropy method using fractional moments and deep learning for geotechnical reliability analysis
The spatial variability of the properties of natural soils is one of the major sources of
uncertainties that can influence the performance of geotechnical structures. The direct Monte …
uncertainties that can influence the performance of geotechnical structures. The direct Monte …
Deep learning for geotechnical reliability analysis with multiple uncertainties
ZZ Wang - Journal of Geotechnical and Geoenvironmental …, 2022 - ascelibrary.org
Apart from spatial variability of soil properties, a geotechnical system can have many other
sources of uncertainties. To efficiently analyze such a system in a probabilistic manner …
sources of uncertainties. To efficiently analyze such a system in a probabilistic manner …