This 2-volume set of books, comprising over 2,700 total pages, presents 325 fully original presentations on recent advances in structural health monitoring, as applied to commercial and military aircraft (manned and unmanned), high-rise buildings, wind turbines, civil infrastructure, power plants and ships. One general theme of the books is how SHM can be used for condition-based maintenance, with the goal of developing prediction-based systems, designed to save money over the life of vehicles and structures. A second theme centers on technologies for developing systems comprising sensors, diagnostic data and decision-making, with a focus on intelligent materials able to respond to damage and in some cases repair it. Finally the books discuss the relation among data, data interpretation and decision-making in managing a wide variety of complex structures and vehicles. More recent technologies discussed in the books include SHM and environmental effects, energy harvesting, non-contact sensing, and intelligent networks.Material in these books was first presented in September, 2011 at a conference held at Stanford University and sponsored by the Air Force Office of Scientific Research, the Army Research Office, the Office of Naval Research and the National Science Foundation. Some of the highlights of the books include: SHM technologies for condition-based maintenance (CBM) and predictive maintenance Verification, validation, qualification, data mining, prognostics systems for decision-making Structural health, sensing and materials in closed-loop intelligent networks Military and aerospace, bioinspired sensors, wind turbines, monitoring with MEMS, damage sensing, hot spot monitoring, SHM and ships, high-rise structures Includes a fully-searchable CD-ROM displaying many figures and charts in full color

The increased level of activity on structural health monitoring (SHM) in various universities and research labs has resulted in the development of new methodologies for both identifying the existing damage in structures and predicting the onset of damage that may occur during service. Designers often have to consult a variety of textbooks, journal papers and reports, because many of these methodologies require advanced knowledge of mechanics, dynamics, wave propagation, and material science. Computational Techniques for Structural Health Monitoring gives a one-volume, in-depth introduction to the different computational methodologies available for rapid detection of flaws in structures. Techniques, algorithms and results are presented in a way that allows their direct application. A number of case studies are included to highlight further the practical aspects of the selected topics. Computational Techniques for Structural Health Monitoring also provides the reader with numerical simulation tools that are essential to the development of novel algorithms for the interpretation of experimental measurements, and for the identification of damage and its characterization. Upon reading Computational Techniques for Structural Health Monitoring, graduate students will be able to begin research-level work in the area of structural health monitoring. The level of detail in the description of formulation and implementation also allows engineers to apply the concepts directly in their research.

The Okavango Delta, a globally renowned wetland, is characterised by a mosaic of meandering watercourses, floodplains and islands, and is home to a variety of wildlife and vegetation species. It is a major source of livelihoods for the local communities and also an important attraction for tourism, the second most important economic activity in Botswana after diamonds, contributing 5% to the gross domestic product (GDP). As a globally renowned Ramsar Site and major tourist attraction, the Okavango Delta is a resource of national, regional and international importance. This book examines the results of empirical micro-level studies undertaken in the Okavango Delta and contributes to the formulation of relevant policies for sustainable development in the Okavango Delta.

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.