Prognosis of flow of fly ash and blast furnace slag-based concrete: leveraging advanced machine learning algorithms
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) …
the most concerned parameters. In recent times, the integration of artificial intelligence (AI) …
Shear capacity prediction for FRCM-strengthened RC beams using Hybrid ReLU-Activated BPNN model
This study presents a robust Hybrid ReLU-Activated Backpropagation Neural Network
(BPNN) model for predicting shear strength (VFRCM) in RC beams reinforced with Fiber …
(BPNN) model for predicting shear strength (VFRCM) in RC beams reinforced with Fiber …
Enhancing load capacity prediction of column using eReLU-activated BPNN model
In structural engineering, accurately predicting the load-carrying capacity of columns is
paramount for ensuring the safety and efficiency of construction projects. This study …
paramount for ensuring the safety and efficiency of construction projects. This study …
A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete
S Aggarwal, R Singh, A Rathore, K Kapoor… - Materials Today …, 2024 - Elsevier
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 …
construction industry and various time and effort-consuming tasks are required to measure it …
Data-driven machine learning approaches for predicting permeability and corrosion risk in hybrid concrete incorporating blast furnace slag and fly ash
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 …
permeability and half-cell potentiometer test readings of hybrid concrete containing varying …
Tree-based machine learning models for predicting the bond strength in reinforced recycled aggregate concrete
To address the ever-increasing environmental degradation caused by concrete construction,
utilizing recycled aggregate (RA) in concrete mixes offers a significant solution. This study …
utilizing recycled aggregate (RA) in concrete mixes offers a significant solution. This study …
A theoretical approach based on machine learning for estimation of physical properties of LLDPE in moulding process
F Zhong - Scientific Reports, 2024 - nature.com
This study explores the prediction of mechanical characteristics of linear polyethylene based
on oven residence time, employing various regression models and hyper-parameter tuning …
on oven residence time, employing various regression models and hyper-parameter tuning …
Influence of machine learning approaches for partial replacement of cement content through waste in construction sector
For the purpose of delivering high-quality structures, efficient project management ensures
better selection of materials and methods and manpower. To successfully traverse hurdles …
better selection of materials and methods and manpower. To successfully traverse hurdles …
Optimizing compressive strength in sustainable concrete: a machine learning approach with iron waste integration
The current research delves into enhancing the sustainability of construction materials by
incorporating iron waste into concrete mixtures. The primary aim revolves around predicting …
incorporating iron waste into concrete mixtures. The primary aim revolves around predicting …
Comparative Analysis of LSTM and Random Forest Algorithms for Sentiment Classification in Movie Reviews
S Purohit, A Rajput, S Vats, R Mudgal… - … on Applied Artificial …, 2024 - ieeexplore.ieee.org
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 …
Random Forest classifier with a Long Short-Term Memory (LSTM) network. The Random …