In the field of construction, the workability of concrete, specifically its ability to flow, is one of
the most concerned parameters. In recent times, the integration of artificial intelligence (AI) …

This study presents a robust Hybrid ReLU-Activated Backpropagation Neural Network
(BPNN) model for predicting shear strength (VFRCM) in RC beams reinforced with Fiber …

In structural engineering, accurately predicting the load-carrying capacity of columns is
paramount for ensuring the safety and efficiency of construction projects. This study …

Compressive strength (CS) of concrete is one of the most important factors in the
construction industry and various time and effort-consuming tasks are required to measure it …

This study aims to identify the most suitable machine learning model for predicting the
permeability and half-cell potentiometer test readings of hybrid concrete containing varying …

To address the ever-increasing environmental degradation caused by concrete construction,
utilizing recycled aggregate (RA) in concrete mixes offers a significant solution. This study …

This study explores the prediction of mechanical characteristics of linear polyethylene based
on oven residence time, employing various regression models and hyper-parameter tuning …

For the purpose of delivering high-quality structures, efficient project management ensures
better selection of materials and methods and manpower. To successfully traverse hurdles …

The current research delves into enhancing the sustainability of construction materials by
incorporating iron waste into concrete mixtures. The primary aim revolves around predicting …

This study conducts sentiment analysis on 50,000 movie reviews, comparing the traditional
Random Forest classifier with a Long Short-Term Memory (LSTM) network. The Random …