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Online Damage Detection in Structural Systems

Applications of Proper Orthogonal Decomposition, and Kalman and Particle Filters

  • Book
  • © 2014

Overview

Authors:
  1. Saeed Eftekhar Azam

    1. Road, Housing and Urban Development Research Center, Tehran, Iran

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  • Focuses on the development of fast and robust algorithms for online damage detection in structural systems
  • Compares the performances of Kalman, particle and hybrid filters using numerical examples
  • Assesses a method based on proper orthogonal decomposition in terms of speed-up and accuracy of estimations
  • Will aid structural and civil engineers wishing to conduct research in structural health monitoring
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

Part of the book sub series: PoliMI SpringerBriefs (BRIEFSPOLIMI)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xii
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  2. Introduction

    • Saeed Eftekhar Azam
    Pages 1-5

  3. Recursive Bayesian Estimation of Partially Observed Dynamic Systems

    • Saeed Eftekhar Azam
    Pages 7-55

  4. Model Order Reduction of Dynamic Systems via Proper Orthogonal Decomposition

    • Saeed Eftekhar Azam
    Pages 57-86

  5. POD-Kalman Observer for Linear Time Invariant Dynamic Systems

    • Saeed Eftekhar Azam
    Pages 87-103

  6. Dual Estimation and Reduced Order Modeling of Damaging Structures

    • Saeed Eftekhar Azam
    Pages 105-121

  7. Conclusions

    • Saeed Eftekhar Azam
    Pages 123-127

  8. Back Matter

    Pages 129-135
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Keywords

About this book

This monograph assesses in depth the application of recursive Bayesian filters in structural health monitoring. Although the methods and algorithms used here are well established in the field of automatic control, their application in the realm of civil engineering has to date been limited. The monograph is therefore intended as a reference for structural and civil engineers who wish to conduct research in this field. To this end, the main notions underlying the families of Kalman and particle filters are scrutinized through explanations within the text and numerous numerical examples. The main limitations to their application in monitoring of high-rise buildings are discussed and a remedy based on a synergy of reduced order modeling (based on proper orthogonal decomposition) and Bayesian estimation is proposed. The performance and effectiveness of the proposed algorithm is demonstrated via pseudo-experimental evaluations.

Authors and Affiliations

  • Road, Housing and Urban Development Research Center, Tehran, Iran

    Saeed Eftekhar Azam

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