Progress and opportunities for machine learning in materials and processes of additive manufacturing
In recent years, there has been widespread adoption of machine learning (ML) technologies
to unravel intricate relationships among diverse parameters in various additive …
to unravel intricate relationships among diverse parameters in various additive …
A critical review on modeling and prediction on properties of fresh and hardened geopolymer composites
P Zhang, Y Mao, W Yuan, J Zheng, S Hu… - Journal of Building …, 2024 - Elsevier
Geopolymer is an environmentally friendly material that is recognized as a potential
alternative binder to ordinary Portland cement. However, accurate prediction of the …
alternative binder to ordinary Portland cement. However, accurate prediction of the …
Estimation of strength, rheological parameters, and impact of raw constituents of alkali-activated mortar using machine learning and SHapely Additive exPlanations …
One-part alkali-activated material (AAM) is a new eco-friendly developed low-carbon binder
that utilizes alkaline activators in solid form. This study deals with the experimental synthesis …
that utilizes alkaline activators in solid form. This study deals with the experimental synthesis …
[HTML][HTML] A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive …
This research used gene expression programming (GEP) and multi expression
programming (MEP) to determine the compressive strength (CS) of alkali-activated material …
programming (MEP) to determine the compressive strength (CS) of alkali-activated material …
[HTML][HTML] Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants
Airborne contaminants pose significant environmental and health challenges. Titanium
dioxide (TiO 2) has emerged as a leading photocatalyst in the degradation of air …
dioxide (TiO 2) has emerged as a leading photocatalyst in the degradation of air …
[HTML][HTML] Forecasting the strength characteristics of concrete incorporating waste foundry sand using advance machine algorithms including deep learning
The incorporation of waste foundry sand (WFS) into concrete has been recognized as a
sustainable approach to improve the strength properties of waste foundry sand concrete …
sustainable approach to improve the strength properties of waste foundry sand concrete …
[HTML][HTML] Optimizing compressive strength prediction models for rice husk ash concrete with evolutionary machine intelligence techniques
This research intended to increase the understanding of using modern machine intelligence
techniques, including multi-expression programming (MEP) and gene expression …
techniques, including multi-expression programming (MEP) and gene expression …
[HTML][HTML] Formulation and characterization of cleaner one-part novel fly ash/lime-based alkali-activated material
With the advanced development in sustainable cementitious materials, cleaner one-part
alkali-activated materials (AAM) have shown a significant effect on increasing compressive …
alkali-activated materials (AAM) have shown a significant effect on increasing compressive …
Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms
Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of
concrete structures, either through external bonding or internal reinforcement. However, the …
concrete structures, either through external bonding or internal reinforcement. However, the …
Forecasting the strength of micro/nano silica in cementitious matrix by machine learning approaches
This work provides an intelligent machine learning-based technique for anticipating cement
paste compressive strength incorporating nanocomposite. In this approach, artificial neural …
paste compressive strength incorporating nanocomposite. In this approach, artificial neural …