State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering
Significant uncertainties can be found in the modelling of geotechnical materials. This can
be attributed to the complex behaviour of soils and rocks amidst construction processes …
be attributed to the complex behaviour of soils and rocks amidst construction processes …
Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils
Abstract The Three Gorges Reservoir Area (TGRA) is one of the most important landslide-
prone regions in China, and rational stability evaluation of reservoir slopes in it is of great …
prone regions in China, and rational stability evaluation of reservoir slopes in it is of great …
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 …
Data augmentation for CNN-based probabilistic slope stability analysis in spatially variable soils
A novel methodology that involves the coupling of Convolutional Neural Networks (CNNs)
and a data augmentation technique is proposed for slope reliability calculations. The …
and a data augmentation technique is proposed for slope reliability calculations. The …
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 …
A data-driven method to model stress-strain behaviour of frozen soil considering uncertainty
Various experiments and computational methods have been conducted to describe the
mechanical behaviours of frozen soils. However, due to high nonlinearity and uncertainty of …
mechanical behaviours of frozen soils. However, due to high nonlinearity and uncertainty of …
[HTML][HTML] A hybrid data-driven approach for rainfall-induced landslide susceptibility map**: Physically-based probabilistic model with convolutional neural network
Landslide susceptibility map** (LSM) plays a crucial role in assessing geological risks.
The current LSM techniques face a significant challenge in achieving accurate results due to …
The current LSM techniques face a significant challenge in achieving accurate results due to …
Stochastic simulation of geological cross-sections from boreholes: a random field approach with Markov Chain Monte Carlo method
A reliable geological cross-section is essential to the design and risk assessment of
underground structures. Random fields are commonly employed to model geological …
underground structures. Random fields are commonly employed to model geological …
Deep learning-based methods in structural reliability analysis: a review
SS Afshari, C Zhao, X Zhuang… - … Science and Technology, 2023 - iopscience.iop.org
One of the most significant and growing research fields in mechanical and civil engineering
is structural reliability analysis (SRA). A reliable and precise SRA usually has to deal with …
is structural reliability analysis (SRA). A reliable and precise SRA usually has to deal with …