Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC
(Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With …

In this study, we used four different machine learning models-artificial neural network (ANN),
support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF)-to …

Climate change is a serious global issue causing more extreme weather patterns, resulting
in more frequent and severe events like urban flooding. This review explores the connection …

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

Streamflow forecasting is essential for effective water resource planning and early warning
systems. Streamflow and related parameters are often characterized by uncertainties and …

This study introduces the first-ever self-explanatory interface for diagnosing diabetes
patients using machine learning. We propose four classification models (Decision Tree (DT) …

Streamflow forecasting is crucial for effective water resource planning and early warning
systems, especially in regions with complex hydrological behaviors and uncertainties. While …

Abstract Machine learning (ML) as a subset of artificial intelligence (AI), has gained
significant attention in wind engineering applications over the past decade. Wind load …

This research explores the use of machine learning to predict the mechanical properties of
cementitious materials enhanced with carbon nanotubes (CNTs). Specifically, the study …

This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …