Sensor data analysis, reduction and fusion for assessing and monitoring civil infrastructures

D. Zonta

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

Structural health monitoring data analysis is basically a logical inference problem, wherein we attempt to gain information on the structural state based on sensor responses. In this chapter, we first introduce Bayesian logic as the main instrument to formulate the inference problem in rigorous mathematical terms, properly accounting for data and model uncertainties. Next, an overview of the most popular data reduction techniques is provided, with a special focus on principal component analysis (PCA). The chapter then introduces the concept of data fusion and discusses techniques to handle multi-temporal and multi-sensor data based on Bayesian statistics. Alternative non-probabilistic logical models for handling uncertainties are outlined at the end.

Original languageEnglish
Title of host publicationSensor Technologies for Civil Infrastructures
PublisherElsevier Inc.
Pages33-66
Number of pages34
Volume1
ISBN (Print)9781782422433, 9781782422426
DOIs
Publication statusPublished - 20 May 2014

Keywords

  • bayesian inference
  • data fusion
  • data reduction
  • probabilistic data analysis

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  • Cite this

    Zonta, D. (2014). Sensor data analysis, reduction and fusion for assessing and monitoring civil infrastructures. In Sensor Technologies for Civil Infrastructures (Vol. 1, pp. 33-66). Elsevier Inc.. https://doi.org/10.1533/9781782422433.1.33