A new ANN rheological model of a comply polymer in temperature spectrum
Authors:
- Anna M. Stręk
Abstract
The article presents modelling using artificial neural networks (ANN) of the phenomenon of creep of comply polymer SIKA PS which can be used in various applications in civil engineering. Data for modelling was gathered in compressive experiments conveyed under a set of fixed conditions of compressive stress and temperature. Part of the data was pre-processed by smoothing and rediscretisation and served as inputs and targets for network training and part of the data was left raw as control set for verification of prognosing capability. Assumed neural network architectures were one- and two-layer feedforward networks with Bayesian regularisation as a learning method. Altogether 55 networks with 8 to 12 neurons in varying structural configurations were trained. Fitting and prognosing verification was performed using mean absolute relative error as a measure; also, results were plotted and assessed visually. In result, the research allowed for formulation of a new rheological model for comply polymer SIKA PS in time, stress and temperature field domain with fitting quality of mean absolute relative error 1.3% and prognosis quality of mean absolute relative error 8.73%. The model was formulated with the use of a two-layer network with 5 + 5 neurons.
- Record ID
- CUTe33ce1388a5b46db8427d8a5dc34cc2c
- Publication categories
- ;
- Author
- Other language title versions
- Nowy model reologiczny dla polimeru podatnego w spektrum temperatur uzyskany za pomocą sztucznych sieci neuronowych
- Journal series
- Archives of Civil Engineering, ISSN 1230-2945, e-ISSN 2300-3103
- Issue year
- 2023
- Vol
- 69
- No
- 1
- Pages
- 231-243
- Other elements of collation
- tab.; wykr.; Bibliografia (na s.) - 240-241; Bibliografia (liczba pozycji) - 21; Oznaczenie streszczenia - Abstr., Streszcz.; Numeracja w czasopiśmie - Vol. 69, Iss. 1
- Keywords in Polish
- model reologiczny, pełzanie, polimer podatny, sieci neuronowe, spektrum temperatur
- Keywords in English
- comply polimer, creep, neural networks, rheological model, temperature spectrum
- ASJC Classification
- DOI
- DOI:10.24425/ace.2023.144170 Opening in a new tab
- URL
- https://journals.pan.pl/ace/144155 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTe33ce1388a5b46db8427d8a5dc34cc2c/
- URN
urn:pkr-prod:CUTe33ce1388a5b46db8427d8a5dc34cc2c
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.