Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Data-driven computational homogenization using neural networks: FE2-NN application on damaged masonry

Drosopoulos Georgios, Stavroulakis Georgios

Simple record


URIhttp://purl.tuc.gr/dl/dias/FD0B3D27-98F7-417E-B4D9-3CC4E00E80CF-
Identifierhttps://doi.org/10.1145/3423154-
Identifierhttps://dl.acm.org/doi/10.1145/3423154-
Languageen-
Extent19 pagesen
TitleData-driven computational homogenization using neural networks: FE2-NN application on damaged masonryen
CreatorDrosopoulos Georgiosen
CreatorΔροσοπουλος Γεωργιοςel
CreatorStavroulakis Georgiosen
CreatorΣταυρουλακης Γεωργιοςel
PublisherAssociation for Computing Machinery (ACM)en
Content SummaryFusion of data mining and computational mechanics is a modern approach for the exploitation of available data within rigorous modeling. First steps in this direction have been focused on the usage of neural networks and other soft computing tools as metamodeling tools. This framework seems suitable for numerical homogenization techniques realized within the so-called FE2 environment, where the lower-level analysis of a detailed representative volume element is replaced by a prediction based on a previously prepared database. Numerically prepared data are used here, although the method can be used with experimental data as well. In this case, the need for a constitutive description of the fine scale is bypassed. Extraction of material properties from the database, required by the upper-level finite element analysis, is based on backpropagation artificial neural networks. The method is applicable to monuments and masonry structural systems. We investigate this approach here for the analysis of masonry structures with elastoplastic behavior. Results indicate a satisfactory comparison with published research.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-01-31-
Date of Publication2021-
SubjectNumerical homogenizationen
SubjectMasonry structuresen
SubjectData-driven multiscale analysisen
SubjectNeural network metamodelsen
Bibliographic CitationG. A. Drosopoulos and G. E. Stavroulakis, “Data-driven computational homogenization using neural networks: FE2-NN application on damaged masonry,” ACM J. Comput. Cult. Herit., vol. 14, no. 1, Feb. 2021, doi: 10.1145/3423154.en

Services

Statistics