Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F21%3A00351514" target="_blank" >RIV/68407700:21110/21:00351514 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.istruc.2021.05.096" target="_blank" >https://doi.org/10.1016/j.istruc.2021.05.096</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.istruc.2021.05.096" target="_blank" >10.1016/j.istruc.2021.05.096</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Popis výsledku v původním jazyce

In this study, a deep learning-based axial capacity prediction for cold-formed steel channel sections is developed using Deep Belief Network (DBN). A total of 10,500 data points for training the DBN are generated from nonlinear elasto plastic finite element analysis, which incorporated both initial imperfections, as recommended by the Australian/New Zealand Standard (AS/NZS 4600:2018) and residual stresses as recommended by Moen et al. A comparison against experimental results found in the literature was conducted. It was found that the DBN was conservative by 9%, 6% and 8% for stub columns, intermediate columns, and slender columns, respectively. When compared against a typical shallow artificial neural network (Backpropagation Neural Network) and a typical machine learning model (Linear regression model based on PaddlePaddle), it was shown that DBN performed around 2% better than both with the same training data. When the same comparison was made for both the Effective Width Method and the Direct Strength Method, it was found that they were conservative by 15%, 13%, and 15%, respectively. Based on the DBN output data, new and improved design equations for AS/NZS 4600:2018 were proposed.

Název v anglickém jazyce

Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Popis výsledku anglicky

In this study, a deep learning-based axial capacity prediction for cold-formed steel channel sections is developed using Deep Belief Network (DBN). A total of 10,500 data points for training the DBN are generated from nonlinear elasto plastic finite element analysis, which incorporated both initial imperfections, as recommended by the Australian/New Zealand Standard (AS/NZS 4600:2018) and residual stresses as recommended by Moen et al. A comparison against experimental results found in the literature was conducted. It was found that the DBN was conservative by 9%, 6% and 8% for stub columns, intermediate columns, and slender columns, respectively. When compared against a typical shallow artificial neural network (Backpropagation Neural Network) and a typical machine learning model (Linear regression model based on PaddlePaddle), it was shown that DBN performed around 2% better than both with the same training data. When the same comparison was made for both the Effective Width Method and the Direct Strength Method, it was found that they were conservative by 15%, 13%, and 15%, respectively. Based on the DBN output data, new and improved design equations for AS/NZS 4600:2018 were proposed.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Structures

ISSN

2352-0124

e-ISSN

2352-0124

Svazek periodika

33

Číslo periodika v rámci svazku

2021

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

2792-2802

Kód UT WoS článku

000701963100005

EID výsledku v databázi Scopus

2-s2.0-85108251667