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Calibration of Micromechanical Parameters for the Discrete Element Simulation of a Masonry Arch using Artificial Intelligence

Autor(en): ORCID (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rakpart 3., H-1111 Budapest, Hungary)
ORCID (Department of Machine and Product Design, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rakpart 3., H-1111 Budapest, Hungary)
ORCID (Department of Artificial Intelligence, Faculty of Informatics, Eötvös Loránd University, Pázmány Péter Sétány 1/A, H-1117 Budapest, Hungary)
(Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rakpart 3., H-1111 Budapest, Hungary)
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Infrastructures, , n. 4, v. 8
Seite(n): 64
DOI: 10.3390/infrastructures8040064
Abstrakt:

This study focuses on an old but still unresolved problem of automatically calibrating the constitutive parameters of discrete element models. Instead of the troublesome and time-consuming manual trial-and-error method, which is typical today, the authors suggest using artificial intelligence techniques. A masonry arch is analysed, whose experimental static load–displacement behaviour is known from the literature. An attempt is made to match this behaviour with discrete element models, through finding appropriate quantitative values for the parameters. Two methods (Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO)) are tested and, since PSO turns out to be more reliable, a further improved version, ‘Trust-Based Particle Swarm Optimisation’ (TBPSO), is proposed. The results show that (1) TBPSO quickly leads to suitable alternative parameter sets that make the discrete element model match the behaviour of the real experiments and (2) the optimal values of the parameters strongly depend on the loading velocity and the discretisation method used.

Copyright: © 2023 the Authors. Licensee MDPI, Basel, Switzerland.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10722707
  • Veröffentlicht am:
    22.04.2023
  • Geändert am:
    10.05.2023
 
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