Structural Health Monitoring A Machine Learning Perspective Charles R Farrar by Charles R. Farrar, Keith Worden(auth.) 9781118443118, 9781119994336, 111844311X, 1119994330 instant download after payment.

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

Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions.

Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies.

• Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm
• Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms
• Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject. 

Content:
Chapter 1 Introduction (pages 1–16):
Chapter 2 Historical Overview (pages 17–43):
Chapter 3 Operational Evaluation (pages 45–52):
Chapter 4 Sensing and Data Acquisition (pages 53–85):
Chapter 5 Case Studies (pages 87–117):
Chapter 6 Introduction to Probability and Statistics (pages 119–160):
Chapter 7 Damage?Sensitive Features (pages 161–243):
Chapter 8 Features Based on Deviations from Linear Response (pages 245–294):
Chapter 9 Machine Learning and Statistical Pattern Recognition (pages 295–320):
Chapter 10 Unsupervised Learning – Novelty Detection (pages 321–360):
Chapter 11 Supervised Learning – Classification and Regression (pages 361–401):
Chapter 12 Data Normalisation (pages 403–438):
Chapter 13 Fundamental Axioms of Structural Health Monitoring (pages 439–460):
Chapter 14 Damage Prognosis (pages 461–477):