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 …

To encourage the consumption of crumb rubber (CR), gene expression programming (GEP)
has been exercised in this paper to establish empirical models for estimation of mechanical …

Water quality indexes (WQI) are pivotal in assessing aquatic systems. Conventional
modeling approaches rely on extensive datasets with numerous unspecified inputs, leading …

In order to reduce the CO 2 associated with cement production, this study explored the
potential of rice husk ash (RHA) and fly ash (FA) as supplementary cementitios materials for …

Abstract Three-dimensional (3D) printing in the construction industry is growing rapidly due
to its inherent advantages, including intricate geometries, reduced waste, accelerated …

By replacing cement in concrete production with rice husk ash (RHA), the amount of cement
used and its environmental impact can be reduced. The objective of this study is to …

This research suggests a robust integration of artificial neural networks (ANN) for predicting
swell pressure and the unconfined compression strength of expansive soils (P s UCS-ES) …

This study presents a comparative analysis of individual and ensemble learning algorithms
(ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic …

One of the largest sources of greenhouse gas (GHG) emissions is the construction concrete
industry which has alone 50% of the world's emissions. One possible remedy to mitigate the …

This study used three artificial intelligence-based algorithms–adaptive neuro-fuzzy inference
system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) …