Structural damage detection using deep learning networks

Bearzotti, Riccardo (2018) Structural damage detection using deep learning networks. [Laurea magistrale], Università di Bologna, Corso di Studio in Ingegneria civile [LM-DM270], Documento full-text non disponibile
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Research on damage detection of structures using image process- ing techniques has been actively conducted, specially on infrastruc- tures as road pavements, achieving considerably high detection accu- racies. These techniques are more and more studied all over the world cause seems be a powerful method able to replace, in some conditions, the experience and the visual ability of humans. This thesis has the purpose to introduce how the development in the last few years of the image processing can be useful to avoid some costs on structure monitoring and predict some disaster, that the most of times we listened call them as announced disasters that could be avoided. This thesis introduce the deep learning method implemented on Mat- lab to solve this problems trying to understand, in the first part, what machine learning and deep learning consist of, which is the best way to use the convolution neural networks and in which parameters work on. This we the purpose to give some background about this tech- nique in order to implement it on a large number of problems. There will be also some examples of basic codes and the outcomes are discussed, in order to figure out which is the best tool or combi- nation of tool to solve a problem of more complexity. At the end there are some consideration about useful future works that can be studied in order to help in structure monitoring in lab tests, during the life cycle and in case of collapse.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Bearzotti, Riccardo
Relatore della tesi
Correlatore della tesi
Corso di studio
Curriculum: Strutture
Ordinamento Cds
Parole chiave
Machine Learning,deep learning,Convolutional neural network,structural damage detection
Data di discussione della Tesi
5 Ottobre 2018

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