The prediction of liquefaction-induced lateral spreading/displacement (D h) is a challenging
task for civil/geotechnical engineers. In this study, a new approach is proposed to predict D h …

Featured Application The presented paper conducted a comparative analysis based on well-
known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (FS) of …

Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble
learning (EL) are four outstanding approaches that enable algorithms to extract information …

In traditional slope stability analysis, it is assumed that some “average” or appropriately
“conservative” properties operate over the entire region of interest. This kind of deterministic …

Landslide susceptibility assessment is considered the first step in landslide risk assessment,
but current studies mostly rely on GIS platforms or other software for data preprocessing. The …

Soil liquefaction during earthquakes is a complex geotechnical engineering problem.
Although various analytical approaches exist for predicting liquefaction risk, their limitations …

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 …

The hazards and consequences of slope collapse can be reduced by obtaining a reliable
and accurate prediction of slope safety, hence, developing effective tools for foreseeing their …

Field monitoring data from multiple monitoring points often contain redundant and/or dirty
data, which provide less value for slope safety evaluation. Existing slope safety evaluation …

In this study, twelve machine learning (ML) techniques are used to accurately estimate the
safety factor of rock slopes (SFRS). The dataset used for developing these models consists …