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

Author(s): 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: journal article
Language(s): English
Published in: Infrastructures, , n. 4, v. 8
Page(s): 64
DOI: 10.3390/infrastructures8040064
Abstract:

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.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10722707
  • Published on:
    22/04/2023
  • Last updated on:
    10/05/2023
 
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