Bayesian neural networks to investigate the effects of corner radius on the performance of frp confined columns

Abstract eng:
In recent years, the use of fiber reinforced polymer (FRP) composites has become increasingly popular for retrofitting concrete elements. One important application regards the confinement of reinforced concrete columns: jacketing with FRP wraps results in a remarkable enhancement of their strength and ductility. The confinement action depends on the column shape: tests reported in the literature have recognized that the corner radius significantly affects the confinement effects. A significant amount of research has been devoted to circular columns retrofitted with FRP, but less is known about rectangular/square columns where the concrete is non-uniformly confined and the effectiveness of confinement is reduced. Some studies and structural codes suggest that a sharp corner offers no confinements. However, published results indicate that a jacket with sharp corners provides a certain degree of effective confinement. In the first part of the paper, some existent models for strength prediction in case of square confined sections are discussed. The second part of the work aims at developing a statistical model that can predict the confinement effect, given the corner radius and the concrete grade. For this purpose, a Bayesian neural network model is chosen because it can effectively deal with uncertain information, making it highly promising for handling prediction problems. Some published data from experimental tests are used for building and training a neural network model. Then, the proposed model is tested on other experimental samples that were not included in the training set. The model shows good approximation properties: the average percentage error is about 8.9%. The results obtained with the proposed model are also compared with those obtained by applying the models presented in the first part on the same data set.

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