The development of nuclear technologies has directed environmental radioactivity research
toward continuously improving existing and developing new models for different …

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

Seismic response assessment requires reliable information about subsurface conditions,
including soil shear wave velocity (V s). To properly assess seismic response, engineers …

Conventional soil classification methods are expensive and demand extensive field and
laboratory work. This research evaluates the efficiency of various machine learning (ML) …

The interface shear behavior is quite beneficial for explaining the stress–strain response of
geosynthetics. A series of interface direct shear tests are carried out between three distinct …

The present study proposes a novel ML methodology for differentiating between
unstabilized aggregate specimens and those stabilized with triangular and rectangular …

The identification of indoor harmful gases is imperative due to their significant threats to
human health and safety. To achieve accurate identification, an effective strategy of …

Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of
peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly …

Expansive soils are widely used as compacted clay liner materials in landfills applications.
The evaluation of these soils as contaminant barriers involves tedious experimental work …

Abstract Machine learning methods have a wide range of applications, including predicting
soil compression coefficients for offshore wind power projects. This study compared three …