Zobrazeno 1 - 10
of 2 242
pro vyhledávání: '"Mostafa, E."'
We present a novel image-based adaptive domain decomposition FEM framework to accelerate the solution of continuum damage mechanics problems. The key idea is to use image-processing techniques in order to identify the moving interface between the hea
Externí odkaz:
http://arxiv.org/abs/2411.04824
Autor:
Ahmed, Bilal, Qiu, Yuqing, Abueidda, Diab W., El-Sekelly, Waleed, de Soto, Borja Garcia, Abdoun, Tarek, Mobasher, Mostafa E.
Finite element modeling is a well-established tool for structural analysis, yet modeling complex structures often requires extensive pre-processing, significant analysis effort, and considerable time. This study addresses this challenge by introducin
Externí odkaz:
http://arxiv.org/abs/2409.00994
Damage identification for bridges using machine learning: Development and application to KW51 bridge
Autor:
Qiu, Yuqing, Ahmed, Bilal, Abueidda, Diab W., El-Sekelly, Waleed, de Soto, Borja Garcia, Abdoun, Tarek, Ji, Hongli, Qiu, Jinhao, Mobasher, Mostafa E.
The available tools for damage identification in civil engineering structures are known to be computationally expensive and data-demanding. This paper proposes a comprehensive machine learning based damage identification (CMLDI) method that integrate
Externí odkaz:
http://arxiv.org/abs/2408.03002
The modern digital engineering design often requires costly repeated simulations for different scenarios. The prediction capability of neural networks (NNs) makes them suitable surrogates for providing design insights. However, only a few NNs can eff
Externí odkaz:
http://arxiv.org/abs/2405.19143
In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors, can efficiently simulate the entire loading history of non-local gradient damage propagation. To
Externí odkaz:
http://arxiv.org/abs/2402.05460
Autor:
Ahmed Ashraf Soliman, Mostafa E. Aboul-Fetouh, Sayed Gomaa, Tarek M. Aboul-Fotouh, Attia Mahmoud Attia
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Unconventional resources, such as heavy oil, are increasingly being explored and exploited due to the declining availability of conventional petroleum resources. Heavy crude oil poses challenges in production, transportation, and refining, d
Externí odkaz:
https://doaj.org/article/8e49816bb0094278b627a6ad2df5d8f9
The numerical solution of continuum damage mechanics (CDM) problems suffers from convergence-related challenges during the material softening stage, and consequently existing iterative solvers are subject to a trade-off between computational expense
Externí odkaz:
http://arxiv.org/abs/2308.13758
Most currently available methods for modeling multiphysics, including thermoelasticity, using machine learning approaches, are focused on solving complete multiphysics problems using data-driven or physics-informed multi-layer perceptron (MLP) networ
Externí odkaz:
http://arxiv.org/abs/2305.17799
Autor:
Shimaa H. El-Sapagh, Nessma A. El-Zawawy, Mostafa E. Elshobary, Mohammed Alquraishi, Hossain M. Zabed, Hoda S. Nouh
Publikováno v:
Microbial Cell Factories, Vol 23, Iss 1, Pp 1-26 (2024)
Abstract Background Biotechnology provides a cost-effective way to produce nanomaterials such as silver oxide nanoparticles (Ag2ONPs), which have emerged as versatile entities with diverse applications. This study investigated the ability of endophyt
Externí odkaz:
https://doaj.org/article/0a4342f345f04c82917d1fe9c4cf5ab0
Autor:
Eman Elish, Mostafa E. AboElsoud
Publikováno v:
Humanities & Social Sciences Communications, Vol 11, Iss 1, Pp 1-14 (2024)
Abstract This empirical study undertakes a rigorous examination of the environmental sustainability impact of the Belt and Road Initiative (BRI) on its member countries. Employing a robust difference-in-difference quasi-natural experimental technique
Externí odkaz:
https://doaj.org/article/9f88e6c39ded4fe9964200d60d78e61f