This study aims to develop four ensemble machine learning (ML) models, including Random
Forest (RF), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), and Extreme …

This paper proposes an improved version of the Hunter‐prey optimization (HPO) method to
enhance its search capabilities for solving the Optimal Power Flow (OPF) problem, which …

In reinforced concrete (RC) members, the bond behavior is crucial to transfer the reinforcing
bar stress to the concrete. However, estimating reinforcing bar bonding failure stress is …

This paper, for the first time, develops novel hybrid machine learning models that combine
Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, Random …

The impact effect is a crucial issue in civil engineering and has received considerable
attention for decades. For the first time, this study develops hybrid machine learning models …

Concrete-filled double-skin steel tubular (CFDST) columns have been widely used in
buildings and infrastructures. Therefore, the compressive behavior of CFDST columns is a …

It is crucial for the photovoltaic system to have an accurate model and well‐estimated
parameters to further increase conversion efficiency. Most existing methods for identifying …

Accurate and understandable prediction of concrete compressive strength (CCS) and
determining the optimal mixture to maximize the CCS are crucial tasks in engineering …

The article focuses on predicting rolling force (RF) in the hot rolling process by incorporating
nano-lubrication conditions through strategies available in finite element method (FEM) …

In this study, an Artificial Neural Network (ANN) model to predict the moment capacity of
Fiber Reinforced Plastic (FRP) strengthened Reinforced Concrete (RC) beams exposed to …