As a sustainable, environmentally friendly, and green solution, biomineralization/bio-
treatment process has recently emerged for enhancing the strength characteristics of loose …

The primary challenges in metropolitan planning, selection of sites, and develo**
preemptive safety measures lie in the characterization and precise appraisal of underground …

The evaluation of ultimate bearing capacity (Qu) of rock socketed shafts (RSSs) is crucial for
the design of foundation systems. This study proposes a new data-driven multivariate …

This study addresses a critical issue of sample disturbance in predicting the elastic modulus
(Es) of soft cohesive soils using machine learning techniques. Traditional approaches either …

The precise evaluation of subsurface soil information is paramount for effective infrastructure
design and planning. Geotechnical soil maps (GSMs) play a pivotal role in estimating …

Genetic programming (GP) is a machine learning tool to predict the maximum dry density (ρ
dmax) as well as optimum water content (OMC) of expansive soils ('ρ dmax OMC-ES') in …

Expansive soils swell when wet and shrink when dry, causing differential settlements that
can lead to structural failures in roads and buildings. In cases where these soils cannot be …

Abstract The Tiga Dam, a primary hydraulic structure in northern Nigeria, is subjected to
intense hydrological stress during the rainy season, posing potential risks to its structural …

This study developed artificial intelligence models to predict Atterberg's limits, specifically
the liquid (LL) and plastic limits (PL), based on# 200 sieve analysis, which is a laborious and …

The current investigation introduces the optimal performance models for predicting the
optimum moisture content (OMC), maximum dry density (MDD), and confined compressive …