Design of surrogate models in civil engineering by neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00378761" target="_blank" >RIV/68407700:21230/24:00378761 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/SEEDA-CECNSM63478.2024.00017" target="_blank" >https://doi.org/10.1109/SEEDA-CECNSM63478.2024.00017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SEEDA-CECNSM63478.2024.00017" target="_blank" >10.1109/SEEDA-CECNSM63478.2024.00017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Design of surrogate models in civil engineering by neural networks

Popis výsledku v původním jazyce

We present a task from the critical infrastructure field in materials engineering. We created a surrogate model for the bridge construction object to determine the material parameters' values. The work aims to use neural networks to conduct an initial investigation of the task and to find out the aspects of machine learning application. To reduce the computational complexity of the models, we designed specific neural networks whose architecture corresponds to the structure and characteristics of the processed data. Furthermore, we outcome also interpretability and justification of the model's decision-making. The main contribution of the work is the replacement of the unknown or too complex physical, mathematical description of material objects with a neural network model. 2024 IEEE.

Název v anglickém jazyce

Design of surrogate models in civil engineering by neural networks

Popis výsledku anglicky

We present a task from the critical infrastructure field in materials engineering. We created a surrogate model for the bridge construction object to determine the material parameters' values. The work aims to use neural networks to conduct an initial investigation of the task and to find out the aspects of machine learning application. To reduce the computational complexity of the models, we designed specific neural networks whose architecture corresponds to the structure and characteristics of the processed data. Furthermore, we outcome also interpretability and justification of the model's decision-making. The main contribution of the work is the replacement of the unknown or too complex physical, mathematical description of material objects with a neural network model. 2024 IEEE.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/CK03000023" target="_blank" >CK03000023: Digitální dvojče pro zvýšení spolehlivosti a životnosti betonových mostů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 9th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)

ISBN

979-8-3315-0448-9

ISSN

—

e-ISSN

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Počet stran výsledku

8

Strana od-do

42-49

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

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Místo konání akce

Agiou Spiridonos 28, Egaleo, Athens 12243

Datum konání akce

20. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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