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

Predicting Penetration Depth in Ultra-High-Performance Concrete Targets under Ballistic Impact: An Interpretable Machine Learning Approach Augmented by Deep Generative Adversarial Network DOI Creative Commons
Majid Khan,

Muhammad Faisal Javed,

Nashwan Adnan Othman

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103909 - 103909

Published: Jan. 1, 2025

Language: Английский

Citations

2
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