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Synopsis

Written by global leaders and pioneers in the field, this bookis a must-have read for researchers,  practicing engineers anduniversity faculty working in SHM.

Structural Health Monitoring: A Machine LearningPerspective is the first comprehensive book on the generalproblem of structural health monitoring. The authors, renownedexperts in the field, consider structural health monitoring in anew manner by casting the problem in the context of a machinelearning/statistical pattern recognition paradigm, first explainingthe paradigm in general terms then explaining the process in detailwith further insight provided via numerical and experimentalstudies of laboratory test specimens and in-situ structures.This paradigm provides a comprehensive framework for developing SHMsolutions.

Structural Health Monitoring: A Machine LearningPerspective makes extensive use of the authors’ detailedsurveys of the technical literature, the experience they havegained from teaching numerous courses on this subject, and theresults of performing numerous analytical and experimentalstructural health monitoring studies.

  • Considers structural health monitoring in a new manner bycasting the problem in the context of a machinelearning/statistical pattern recognition paradigm
  • Emphasises an integrated approach to the development ofstructural health monitoring solutions by coupling the measurementhardware portion of the problem directly with the datainterrogation algorithms
  • Benefits from extensive use of the authors’ detailedsurveys of 800 papers in the technical literature and theexperience they have gained from teaching numerous short courses onthis subject. 

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