2 resultados para Multilayer Perceptron

em AMS Tesi di Dottorato - Alm@DL - Università di Bologna


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The instability of river bank can result in considerable human and land losses. The Po river is the most important in Italy, characterized by main banks of significant and constantly increasing height. This study presents multilayer perceptron of artificial neural network (ANN) to construct prediction models for the stability analysis of river banks along the Po River, under various river and groundwater boundary conditions. For this aim, a number of networks of threshold logic unit are tested using different combinations of the input parameters. Factor of safety (FS), as an index of slope stability, is formulated in terms of several influencing geometrical and geotechnical parameters. In order to obtain a comprehensive geotechnical database, several cone penetration tests from the study site have been interpreted. The proposed models are developed upon stability analyses using finite element code over different representative sections of river embankments. For the validity verification, the ANN models are employed to predict the FS values of a part of the database beyond the calibration data domain. The results indicate that the proposed ANN models are effective tools for evaluating the slope stability. The ANN models notably outperform the derived multiple linear regression models.

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The work presented in this thesis is focused on the open-ended coaxial-probe frequency-domain reflectometry technique for complex permittivity measurement at microwave frequencies of dispersive dielectric multilayer materials. An effective dielectric model is introduced and validated to extend the applicability of this technique to multilayer materials in on-line system context. In addition, the thesis presents: 1) a numerical study regarding the imperfectness of the contact at the probe-material interface, 2) a review of the available models and techniques, 3) a new classification of the extraction schemes with guidelines on how they can be used to improve the overall performance of the probe according to the problem requirements.