1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
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Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Stochastic optimization of carbon nanotube reinforced concrete for enhanced structural performance
Συγγραφέας:	[EL] Καλογερής, Ιωάννης[EN] Kalogeris, Ioannis [EL] Πυριαλάκος, Στέφανος[EN] Pyrialakos, Stefanos [EL] Κόκκινος, Οδυσσέας[EN] Kokkinos, Odysseas [EL] Παπαδόπουλος, Βησσαρίων[EN] Papadopoulos, Vissarion
Ημερομηνία:	11/07/2022
Περίληψη:	This paper presents a material optimization framework for identifying optimal material typologies to improve structural performance under the presence of uncertainties. Specifically, the focus in this work is on carbon nanotube (CNT)-reinforced concrete with the optimization problem consisting in finding the optimal CNT orientation in the host material so as to minimize the total deformation of structures made up from the composite. Regarding the material modeling, a two-level approach is considered to characterize the mechanical properties of the reinforced concrete. Specifically, cement mortar enhanced with carbon nanotubes is studied at a microscale level where a Drucker-Prager plasticity model is assumed to describe its inelastic behavior. Subsequently, the reinforced mortar along with the concrete’s larger aggregates is studied at a mesoscale level using continuum micromechanics. For the analysis of structural systems comprised of this composite material, an extension of the FE2 technique, termed FE3, is employed. To overcome the immense computational demands associated with FE3, efficient neural network-based surrogates are developed to approximate the nonlinear constitutive law of the composite. In this setting, the stochastic optimization problem equates to finding the optimal orientation of CNTs in the cement mortar, so as to achieve small structural deformations with low variability, under the presence of uncertainty in the loading conditions. To solve this problem, the Covariance Matrix Adaptation Evolution Strategy is chosen herein, and even though this approach requires a massive number of model runs, it is performed at a reasonable computational cost by virtue of the elaborated surrogate modeling scheme.
Γλώσσα:	Αγγλικά
Σελίδες:	17
DOI:	10.1007/s00366-022-01693-8
EISSN:	1435-5663
Θεματική κατηγορία:	[EL] Μηχανική, γενικά[EN] Engineering, general
Λέξεις-κλειδιά:	concrete; carbon nanotubes; composite materials; structural performance; neural networks
Κάτοχος πνευματικών δικαιωμάτων:	© Springer-Verlag London Ltd., part of Springer Nature
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://link.springer.com/article/10.1007/s00366-022-01693-8
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.springer.com/journal/366
Τίτλος πηγής δημοσίευσης:	Engineering with Computers
Τόμος:	39
Σελίδες τεκμηρίου (στην πηγή):	2927–2943
Σημειώσεις:	This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme Human Resources Development, Education and Lifelong Learning 2014–2020 in the context of the project “Design of Hyperconcrete reinforced with nanomaterials” (MIS 5049082).
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