Dem-driven investigation and AutoML-Enhanced prediction of Macroscopic behavior in cementitious composites with Variable frictional parameters

Dem-driven investigation and AutoML-Enhanced prediction of Macroscopic behavior in cementitious composites with Variable frictional parameters

This study presents a numerical investigation and predictive modeling framework to evaluate the influence of microscale frictional parameters on the mechanical behavior and failure mechanisms of cementitious composites. In the first phase, discrete element modeling (DEM) was employed to analyze the...

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Bibliographic Details
Main Authors: Vahid Shafaie, Majid Movahedi Rad
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Materials & Design
Subjects:
Discrete Element Modeling
Artificial Neural Networks
AutoML Bonded Friction Angle
Non-bonded Friction Coefficient
Predictive Modeling
Failure Mechanisms
Online Access:http://www.sciencedirect.com/science/article/pii/S0264127525004897
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