A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping
Landslide susceptibility mapping (LSM) is critical for risk assessment and mitigation.
Generalization ability and prediction uncertainty are the current challenges for LSM but have …
Generalization ability and prediction uncertainty are the current challenges for LSM but have …
Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data
The characterization of in situ ground conditions is essential for geotechnical practice. The
probabilistic estimation of soil parameters can be achieved via updating with monitoring …
probabilistic estimation of soil parameters can be achieved via updating with monitoring …
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 …
Bayesian probabilistic characterization of consolidation behavior of clays using CPTU data
Z Zhao, SSC Congress, G Cai, W Duan - Acta Geotechnica, 2022 - Springer
The coefficient of consolidation (ch) of clay interpreted based on piezocone penetration test
(CPTU) usually deviates from the actual values. This can be due to the inherent variability of …
(CPTU) usually deviates from the actual values. This can be due to the inherent variability of …
Quantitative risk assessment of landslides with direct simulation of pre-failure to post-failure behaviors
Most previous studies on the quantitative risk assessment (QRA) of landslides focused on
the probability of slope failure at the pre-failure stage and adopted empirical models for …
the probability of slope failure at the pre-failure stage and adopted empirical models for …
Development of two-dimensional ground models by combining geotechnical and geophysical data
Geotechnical and geophysical testing data are conventionally considered as separated
information or combined based on deterministic methods in site investigation programs …
information or combined based on deterministic methods in site investigation programs …
A generalized Bayesian approach for prediction of strength and elastic properties of rock
Rock mass elastic and strength properties are needed for calculation of deformation and
determination of stability of underground structures. Most available models for prediction of …
determination of stability of underground structures. Most available models for prediction of …
Interpolation of extremely sparse geo-data by data fusion and collaborative Bayesian compressive sampling
In geotechnical or geological engineering, geo-data interpolation based on measurements
is often needed for engineering design and analysis. However, measurements are …
is often needed for engineering design and analysis. However, measurements are …
Reliability-based design in spatially variable soils using deep learning: An illustration using shallow foundation
This paper presents the reliability-based design of a strip footing against bearing capacity
failure in spatially variable soils using a deep learning approach. In this method …
failure in spatially variable soils using a deep learning approach. In this method …
An efficient Bayesian method for estimating runout distance of region-specific landslides using sparse data
T Zhao, J Lei, L Xu - Georisk: Assessment and Management of Risk …, 2022 - Taylor & Francis
The runout distance of landslides is a critical factor that influences landslide risk
quantification and mitigation designs. Nevertheless, empirical correlation models developed …
quantification and mitigation designs. Nevertheless, empirical correlation models developed …