Predictive Modelling of Mechanical Properties of Concrete Using Machine Learning with Secondary Treated Waste Water and Fly Ash DOI Creative Commons

Kumar Rajiv,

Y Ramalinga Reddy,

G Shiva Kumar

et al.

Cleaner Waste Systems, Journal Year: 2025, Volume and Issue: unknown, P. 100296 - 100296

Published: April 1, 2025

Language: Английский

A review on properties and multi-objective performance predictions of concrete based on machine learning models DOI

Bowen Ni,

Md Zillur Rahman, Shuaicheng Guo

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112017 - 112017

Published: Feb. 1, 2025

Language: Английский

Citations

1
Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete DOI Creative Commons
Mana Alyami, Kennedy C. Onyelowe, Ali H. AlAteah

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03869 - e03869

Published: Oct. 16, 2024

Language: Английский

Citations

7
AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements DOI Open Access
Mouhcine Benaicha

Sustainability, Journal Year: 2024, Volume and Issue: 16(15), P. 6644 - 6644

Published: Aug. 3, 2024

This study investigates the application of artificial intelligence (AI) to predict compressive strength self-compacting concrete (SCC) through ultrasonic measurements, thereby contributing sustainable construction practices. By leveraging advancements in computational techniques, specifically neural networks (ANNs), we developed highly accurate predictive models forecast SCC based on pulse velocity (UPV) measurements. Our findings demonstrate a clear correlation between higher UPV readings and improved quality, despite general trend decreased with increased air-entraining admixture (AEA) concentrations. The ANN show exceptional effectiveness predicting strength, coefficient (R2) 0.99 predicted actual values, providing robust tool for optimizing mix designs ensuring quality control. AI-driven approach enhances sustainability by improving material efficiency significantly reducing need traditional destructive testing methods, thus offering rapid, reliable, non-destructive alternative assessing properties.

Language: Английский

Citations

5
Machine learning approach to predict the early-age flexural strength of sensor-embedded 3D-printed structures DOI Creative Commons

Kasra Banijamali,

Mary Dempsey,

Jianhua Chen

et al.

Progress in Additive Manufacturing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Abstract The absence of formwork in 3D-printed concrete, unlike conventional mold-cast introduces greater variability curing conditions, posing significant challenges accurately estimating the early-age mechanical strength. Therefore, common non-destructive techniques such as maturity method fail to deliver a generalized predictive model for strength structures. In this study, multiple machine learning (ML) algorithms, including linear regression (LR), support vector (SVR), and artificial neural network (ANN), were developed estimate flexural beams under varying utilizing data collected from embedded sensors. Six input variables employed ML models, relative permittivity, internal temperature, method. For development, 144 points an extensive experimental statistical metrics evaluate proposed models. ANN outperformed other models predicting strength, achieving coefficient determination 95.1%. Furthermore, variable analysis highlighted most influential factor affecting beams.

Language: Английский

Citations

0
Predictive Modelling of Mechanical Properties of Concrete Using Machine Learning with Secondary Treated Waste Water and Fly Ash DOI Creative Commons

Kumar Rajiv,

Y Ramalinga Reddy,

G Shiva Kumar

et al.

Cleaner Waste Systems, Journal Year: 2025, Volume and Issue: unknown, P. 100296 - 100296

Published: April 1, 2025

Language: Английский

Citations

0