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 …

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 …

Despite the widespread application of data-centric techniques in Geotechnical Engineering,
there is a rising need for building trust in the artificial intelligence (AI)-driven safety …

This study addresses the complexities encountered when integrating site-specific field data
into the Bayesian inverse analysis of soil parameters in geotechnical structures. Traditional …

In engineering practice, Bayesian model updating using field data is often conducted to
reduce the substantial inherent epistemic uncertainties in geomaterial properties resulting …

Recently, multiple Kriging (MK) metamodels have demonstrated their advantages in system
slope reliability analysis. However, the inherent spatial variability of soil properties has not …

The Newmark-type predictive models are extensively used to estimate earthquake-induced
sliding displacements (D) of slopes. Most existing models are designed for shallow slope …

Seismic performance prediction of slope reinforcement measures is an essential and
significant problem in structure design and monitoring stage. Enlightened by the …

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil
parameters, making the slope reliability assessment closer to the engineering reality …

Landslide-induced barrier dams pose a threat to the safety of humans, livestock and nearby
infrastructures. The efficient assessment of landslide blocking river is crucial for disaster …