Research on wavelets and artificial intelligence algorithms for structural health monitoring of concrete  bridges

Daniele Storni; Harpal Singh; Kristoffer Tangrand; Niklas Grip

Daniele Storni UiT - The Arctic University of Norway Campus Narvik \\ Department of Computer Science and Computational Engineering \\ Lodve Langesgate 2, Narvik, 8514, Norway
Harpal Singh UiT - The Arctic University of Norway Campus Narvik \\ Department of Computer Science and Computational Engineering \\ Lodve Langesgate 2, Narvik, 8514, Norway
Kristoffer Tangrand UiT - The Arctic University of Norway Campus Narvik \\ Department of Computer Science and Computational Engineering \\ Lodve Langesgate 2, Narvik, 8514, Norway
Niklas Grip Lule{\aa} University of Technology, Lule{\aa}, 97187, Sweden

Abstract

In this paper, we present a comprehensive study of wavelet theory with a focus on structural damage detection. A new example of the application of operational modal analysis (OMA) techniques to a concrete railway arch bridge located over the Kalix river in L{\aa}ngforsen, Sweden is presented. Results from the OMA techniques are used for finite element model (FEM) updating of this concrete railway arch bridge. Further, a new case study for sensor placement on Her{\o}ysund bridge located in Nordland, Norway to conduct OMA is discussed in detail. Moreover, artificial intelligence algorithms that can be useful for addressing the problem of missing data sets in structural health monitoring technologies are reviewed.