izpis_h1_title_alt

Determination of relaxation modulus of time-dependent materials using neural networks
Aulova, Alexandra (Avtor), Govekar, Edvard (Avtor), Emri, Igor (Avtor)

.pdfPDF - Predstavitvena datoteka, prenos (620,59 KB)
URLURL - Izvorni URL, za dostop obiščite http://link.springer.com/article/10.1007%2Fs11043-016-9332-x Povezava se odpre v novem oknu

Izvleček
Health monitoring systems for plastic based structures require the capability of real time tracking of changes in response to the time-dependent behavior of polymer based structures. The paper proposes artificial neural networks as a tool of solving inverse problem appearing within time-dependent material characterization, since the conventional methods are computationally demanding and cannot operate in the real time mode. Abilities of a Multilayer Perceptron (MLP) and a Radial Basis Function Neural Network (RBFN) to solve ill-posed inverse problems on an example of determination of a time-dependent relaxation modulus curve segment from constant strain rate tensile test data are investigated. The required modeling data composed of strain rate, tensile and related relaxation modulus were generated using existing closed-form solution. Several neural networks topologies were tested with respect to the structure of input data, and their performance was compared to an exponential fitting technique. Selected optimal topologies of MLP and RBFN were tested for generalization and robustness on noisy data; performance of all the modeling methods with respect to the number of data points in the input vector was analyzed as well. It was shown that MLP and RBFN are capable of solving inverse problems related to the determination of a time dependent relaxation modulus curve segment. Particular topologies demonstrate good generalization and robustness capabilities, where the topology of RBFN with data provided in parallel proved to be superior compared to other methods.

Jezik:Angleški jezik
Ključne besede:relaxation modulus, inverse problem, neural networks, multilayer perceptron, radial basis function neural network, structural health monitoring
Vrsta gradiva:Članek v reviji (dk_c)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Leto izida:2017
Št. strani:str. 331-349
Številčenje:Vol. 21, iss. 3
UDK:681.5(045)
ISSN pri članku:1385-2000
DOI:10.1007/s11043-016-9332-x Povezava se odpre v novem oknu
COBISS.SI-ID:15013147 Povezava se odpre v novem oknu
Število ogledov:170
Število prenosov:273
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
 
Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
:
Objavi na:AddThis
AddThis uporablja piškotke, za katere potrebujemo vaše privoljenje.
Uredi privoljenje...

Gradivo je del revije

Naslov:Mechanics of time-dependent materials
Skrajšan naslov:Mech. time-depend. mater.
Založnik:Kluwer
ISSN:1385-2000
COBISS.SI-ID:3749403 Povezava se odpre v novem oknu

Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Številka projekta:P2-0264, P2-0241
Naslov:Trajnostni polimerni materiali in tehnologije, Sinergetika kompleksnih sistemov in procesov

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:relaksacijski moduli, nevronske mreže, večplastni perceptron, monitoring stanja stuktur

Podobna dela

Podobna dela v RUL:
Podobna dela v drugih slovenskih zbirkah:

Komentarji

Dodaj komentar

Za komentiranje se morate prijaviti.

Komentarji (0)
0 - 0 / 0
 
Ni komentarjev!

Nazaj