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

D. Zonta

Research output: Chapter in Book/Report/Conference proceedingChapter

3 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.

LanguageEnglish
Title of host publicationSensor Technologies for Civil Infrastructures
Pages33-66
Number of pages34
Volume1
DOIs
Publication statusPublished - 20 May 2014

Fingerprint

Monitoring
Structural health monitoring
Sensors
Data fusion
Principal component analysis
Data reduction
Statistics
Uncertainty

Keywords

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

Cite this

Zonta, D. / Sensor data analysis, reduction and fusion for assessing and monitoring civil infrastructures. Sensor Technologies for Civil Infrastructures. Vol. 1 2014. pp. 33-66
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Sensor data analysis, reduction and fusion for assessing and monitoring civil infrastructures. / Zonta, D.

Sensor Technologies for Civil Infrastructures. Vol. 1 2014. p. 33-66.

Research output: Chapter in Book/Report/Conference proceedingChapter

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