Miloš D Bogdanović, Žarko Petrović, Bojan Milošević, Marina Mijalković, Leonid Stoimenov

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Limit analysis is a structural analysis field which is dedicated to the development of efficient methods to directly determine estimates of the collapse load of a given structural model. For this purpose, the field of limit analysis is based on a set of theorems, referred to as limit (bound) theorems, which are a set of theorems based on the law of conservation of energy and are used for a direct definition of the limit state function for failure by plastic collapse or by inadaptation. This study proposes an artificial neural network (ANN) model in order to approximate the residual bending moment, limit and the incremental failure force of continuous beams. The neural network structure applied here is a radial-Gaussian network architecture (RGIN) and complementary training procedure. It is shown on the example of the two-span continuous beam loaded in the middle of the span that the limit and the incremental failure force can be obtained using neural network approach with sufficient precision and is especially suitable in analysis when some of the model parameters are variable.

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