20.500.12556/RUL-105925 Determination of relaxation modulus of time-dependent materials using neural networks 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. relaxation modulus inverse problem neural networks multilayer perceptron radial basis function neural network structural health monitoring relaksacijski moduli nevronske mreže večplastni perceptron monitoring stanja stuktur true false true Angleški jezik Slovenski jezik Članek v reviji 2018-12-24 09:05:01 2018-12-24 09:05:04 2025-02-24 12:34:13 0000-00-00 00:00:00 2017 0 0 Str. 331-349 iss. 3 Vol. 21 avg. 2017 0000-00-00 PostprintKoncna NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 2018-10-21 681.5(045) 1385-2000 10.1007/s11043-016-9332-x 15013147 3749403 RAZ_Aulova_Alexandra_i2017.pdf RAZ_Aulova_Alexandra_i2017.pdf 1 8B56DC99FB8FF348B5EE3E43ED6DE5FE ce708580230d8b85f01dd9e94abecf35548208e7af09a1dde016387039609807 3dc9c26f-a1b6-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=116621 http://link.springer.com/article/10.1007%2Fs11043-016-9332-x 1 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=116620 Fakulteta za strojništvo 0 0 0