Predicting the Loading Parameters of a Square Panel Upon Linear Deflection

Leonardo Espinosa-Leal; Silas Gebrehiwot; Heikki Remes

doi:10.1007/978-3-031-21678-7_8

Predicting the Loading Parameters of a Square Panel Upon Linear Deflection

Leonardo Espinosa-Leal^*, Silas Gebrehiwot, Heikki Remes

^*Corresponding author for this work

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

This paper presents a machine learning methodology for predicting the static deflection of an isotropic squared panel in the linear-elastic regime. Our method uses a series of simulations via FEM (Finite Element Method) for obtaining the contour plot images of flexural deflections due to point loads at different locations on the panel. From here, we obtain, using sliding windows of different sizes, segments of images that are transformed into features using a state-of-the-art pre-trained deep neural network. We trained three different machine learning models for multi-output regression. Our results show that it is possible to predict with a high score the coordinates of the point of load and the amount of deflection with partial visual information.

Original language	English
Title of host publication	Proceedings of ELM 2021
Subtitle of host publication	Theory, Algorithms and Applications
Editors	Kaj-Mikael Björk
Publisher	Springer
Pages	84-91
ISBN (Electronic)	978-3-031-21678-7
ISBN (Print)	978-3-031-21677-0, 978-3-031-21680-0
DOIs	https://doi.org/10.1007/978-3-031-21678-7_8
Publication status	Published - 2023
MoE publication type	A4 Article in a conference publication
Event	11th International Conference on Extreme Learning Machines (ELM2021) - On-line, Helsinki, Finland Duration: 15 Dec 2021 → 16 Dec 2021 Conference number: 11 https://risklab.fi/events/

Publication series

Series	Proceedings in Adaptation, Learning and Optimization
Volume	16
ISSN	2363-6084

Conference

Conference	11th International Conference on Extreme Learning Machines (ELM2021)
Abbreviated title	ELM2021
Country/Territory	Finland
City	Helsinki
Period	15/12/21 → 16/12/21
Internet address	https://risklab.fi/events/

Access to Document

10.1007/978-3-031-21678-7_8

Cite this

@inproceedings{31e4940466f04c148b7f82102898d709,

title = "Predicting the Loading Parameters of a Square Panel Upon Linear Deflection",

abstract = "This paper presents a machine learning methodology for predicting the static deflection of an isotropic squared panel in the linear-elastic regime. Our method uses a series of simulations via FEM (Finite Element Method) for obtaining the contour plot images of flexural deflections due to point loads at different locations on the panel. From here, we obtain, using sliding windows of different sizes, segments of images that are transformed into features using a state-of-the-art pre-trained deep neural network. We trained three different machine learning models for multi-output regression. Our results show that it is possible to predict with a high score the coordinates of the point of load and the amount of deflection with partial visual information.",

author = "Leonardo Espinosa-Leal and Silas Gebrehiwot and Heikki Remes",

year = "2023",

doi = "10.1007/978-3-031-21678-7_8",

language = "English",

isbn = "978-3-031-21677-0",

series = "Proceedings in Adaptation, Learning and Optimization",

publisher = "Springer",

pages = "84--91",

editor = "Kaj-Mikael Bj{\"o}rk",

booktitle = "Proceedings of ELM 2021",

address = "Germany",

note = "11th International Conference on Extreme Learning Machines (ELM2021), ELM2021 ; Conference date: 15-12-2021 Through 16-12-2021",

url = "https://risklab.fi/events/",

}

Espinosa-Leal, L, Gebrehiwot, S & Remes, H 2023, Predicting the Loading Parameters of a Square Panel Upon Linear Deflection. in K-M Björk (ed.), Proceedings of ELM 2021: Theory, Algorithms and Applications. Springer, Proceedings in Adaptation, Learning and Optimization, vol. 16, pp. 84-91, 11th International Conference on Extreme Learning Machines (ELM2021), Helsinki, Finland, 15/12/21. https://doi.org/10.1007/978-3-031-21678-7_8

Predicting the Loading Parameters of a Square Panel Upon Linear Deflection. / Espinosa-Leal, Leonardo; Gebrehiwot, Silas; Remes, Heikki.
Proceedings of ELM 2021: Theory, Algorithms and Applications. ed. / Kaj-Mikael Björk. Springer, 2023. p. 84-91 (Proceedings in Adaptation, Learning and Optimization, Vol. 16).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Predicting the Loading Parameters of a Square Panel Upon Linear Deflection

AU - Espinosa-Leal, Leonardo

AU - Gebrehiwot, Silas

AU - Remes, Heikki

N1 - Conference code: 11

PY - 2023

Y1 - 2023

N2 - This paper presents a machine learning methodology for predicting the static deflection of an isotropic squared panel in the linear-elastic regime. Our method uses a series of simulations via FEM (Finite Element Method) for obtaining the contour plot images of flexural deflections due to point loads at different locations on the panel. From here, we obtain, using sliding windows of different sizes, segments of images that are transformed into features using a state-of-the-art pre-trained deep neural network. We trained three different machine learning models for multi-output regression. Our results show that it is possible to predict with a high score the coordinates of the point of load and the amount of deflection with partial visual information.

AB - This paper presents a machine learning methodology for predicting the static deflection of an isotropic squared panel in the linear-elastic regime. Our method uses a series of simulations via FEM (Finite Element Method) for obtaining the contour plot images of flexural deflections due to point loads at different locations on the panel. From here, we obtain, using sliding windows of different sizes, segments of images that are transformed into features using a state-of-the-art pre-trained deep neural network. We trained three different machine learning models for multi-output regression. Our results show that it is possible to predict with a high score the coordinates of the point of load and the amount of deflection with partial visual information.

U2 - 10.1007/978-3-031-21678-7_8

DO - 10.1007/978-3-031-21678-7_8

M3 - Conference article in proceedings

SN - 978-3-031-21677-0

SN - 978-3-031-21680-0

T3 - Proceedings in Adaptation, Learning and Optimization

SP - 84

EP - 91

BT - Proceedings of ELM 2021

A2 - Björk, Kaj-Mikael

PB - Springer

T2 - 11th International Conference on Extreme Learning Machines (ELM2021)

Y2 - 15 December 2021 through 16 December 2021

ER -

Predicting the Loading Parameters of a Square Panel Upon Linear Deflection

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