Alexandra Aulova (Avtor), Edvard Govekar (Avtor), Igor Emri (Avtor)

Povzetek

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.

Ključne besede

relaxation modulus;inverse problem;neural networks;multilayer perceptron;radial basis function neural network;structural health monitoring;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 681.5(045)
COBISS: 15013147 Povezava se bo odprla v novem oknu
ISSN: 1385-2000
Št. ogledov: 1822
Št. prenosov: 950
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: relaksacijski moduli;nevronske mreže;večplastni perceptron;monitoring stanja stuktur;
Vrsta dela (COBISS): Članek v reviji
Konec prepovedi (OpenAIRE): 2018-10-21
Strani: str. 331-349
Letnik: ǂVol. ǂ21
Zvezek: ǂiss. ǂ3
Čas izdaje: avg. 2017
DOI: 10.1007/s11043-016-9332-x
ID: 10999473