This study investigates the non-linear capabilities of two machine learning prediction
models, namely Light GBM and XGBoost, for predicting the values of Rapid Chloride …

According to an extensive evaluation of published studies, there is a shortage of research on
systematic literature reviews related to machine learning prediction techniques and …

The use of three artificial neural network (ANN)-based models for the prediction of
unconfined compressive strength (UCS) of granite using three non-destructive test …

The purpose of this study is to offer a high-performance machine learning model for
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …

In order to ensure safe and sustainable design of geosynthetic-reinforced soil foundation
(GRSF), settlement prediction is a challenging task for practising civil/geotechnical …

This study aims to propose hybrid adaptive neuro swarm intelligence (HANSI) techniques for
predicting the thermal conductivity of unsaturated soils. The novel contribution is made by …

Little research can be found in relation to the stability of anisotropic and heterogenous soils
in three dimensions. In this paper, we propose a study on the three-dimensional (3D) …

In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become
popular for constructing safe and sustainable pavement structures. The strength of the …

Settlement prediction of geosynthetic-reinforced soil (GRS) abutments under service loading
conditions is an arduous and challenging task for practicing geotechnical/civil engineers …

With the rising freight demand, specialized heavy-haul railway corridors allow heavier trains
to transport heavy freight, improving productivity and lowering unit costs. Generally, a heavy …