10 - Statistical pattern recognition and damage detection in structural health monitoring of civil infrastructure systems

Abstract

The main problems of structural health monitoring (SHM) can be cast or regarded as problems in pattern recognition (PR) or machine learning. This approach makes use of the availability of appropriate data to learn the relationship between measured quantities and a diagnosis of stateof- health. This chapter describes how PR provides a natural framework for addressing questions of SHM from basic detection to damage location and quantification. The basic theory and practice are illustrated via the use of two experimental case studies; the first concerns the classification of acoustic emission data as part of a damage assessment of a bridge box girder and the second considers the problem of damage location within an aircraft wing. A number of PR algorithms are illustrated, based on both statistical and neural network approaches. A modern approach to machine learning based on concepts of statistical learning theory is also illustrated using the aircraft wing data.