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Integrated SHM Systems: Damage Detection Through Unsupervised Learning and Data Fusion

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Structural Health Monitoring Based on Data Science Techniques

Part of the book series: Structural Integrity ((STIN,volume 21))

Abstract

One of the most daunting challenges of modern structural engineering concerns the management and maintenance of ageing infrastructure. The technical response to this challenge falls within the framework of structural health monitoring (SHM), which pursues the automated diagnosis and prognosis of structures from continuously acquired sensor data. In the last years, particular attention has been devoted in the literature to ambient vibration-based SHM owing to its minimal intrusiveness and global damage identification capabilities. Nevertheless, the sheer variety of failure mechanisms that large-scale civil engineering structures may experience, some of which may be of local nature, compels the use of integrated SHM systems and data fusion for comprehensive damage identification. As a result, such systems must deal with extensive databases of heterogeneous monitoring data, being the selection of critical features a key step to link signals to decisions. This chapter presents an overview of some of the most recent statistical pattern recognition, data fusion, feature extraction and damage detection techniques for integrated SHM systems. Under an application-oriented philosophy, the theoretical basis and implementation details of these techniques are illustrated through real case studies of Italian historic buildings.

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Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti 93, 06125, Perugia, Italy

    Enrique García-Macías & Filippo Ubertini

  2. ETS de Ingeniería de Caminos, Canales y Puertos, Av. Fuentenueva sn 18002, Granada, Spain

    Enrique García-Macías

Authors
  1. Enrique García-Macías
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  2. Filippo Ubertini
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Corresponding author

Correspondence to Enrique García-Macías .

Editor information

Editors and Affiliations

  1. Department of Applied and Computational Mechanics, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Alexandre Cury

  2. Department of Civil Engineering, School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

    Diogo Ribeiro

  3. Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy

    Filippo Ubertini

  4. Structural Engineering, University of California San Diego, La Jolla, CA, USA

    Michael D. Todd

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García-Macías, E., Ubertini, F. (2022). Integrated SHM Systems: Damage Detection Through Unsupervised Learning and Data Fusion. In: Cury, A., Ribeiro, D., Ubertini, F., Todd, M.D. (eds) Structural Health Monitoring Based on Data Science Techniques. Structural Integrity, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-81716-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-81716-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81715-2

  • Online ISBN: 978-3-030-81716-9

  • eBook Packages: EngineeringEngineering (R0)

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