This book investigates the substructuring technology in structural health monitoring (SHM) to improve the accuracy and efficiency of the present SHM methods. SHM has been developed for monitoring, evaluation, and maintenance of civil structures. As the civil structures are usually large scale and a large number of sensors are deployed on a structure, accurate evaluation and maintenance of civil structures are always time-consuming. The book establishes a fundamental framework of substructuring method for the fast analysis of finite element (FE) model and monitoring data. Several practical civil structures are used for illustration. The book is intended for undergraduate and graduate students who are interested in SHM technology, researchers investigating the accurate, efficient, and effective methods in SHM field, and engineers working on evaluation and maintenance of civil structures or other structural dynamics applications.

In sensitivity-based finite element model updating, the eigensolutions and eigensensitivities are calculated repeatedly, which is a time-consuming process for large-scale structures. In this chapter, a forward substructuring method and an inverse substructuring method are proposed to fulfill the model updating of large-scale structures. In the forward substructuring method, the analytical FE model of the global structure is divided into several independent substructures. The eigensolutions of each independent substructure are used to recover the eigensolutions and eigensensitivities of the global structure. Consequently, only some specific substructures are reanalyzed in model updating and assembled with other untouched substructures to recover the eigensolutions and eigensensitivities of the global structure. In the inverse substructuring method, the experimental modal data of the global structure are disassembled into substructural flexibility. Afterwards, each substructure is treated as an independent structure to reproduce its flexibility through a model-updating process. Employing the substructuring method, the model updating of a substructure can be conducted by measuring the local area of the concerned substructure solely. Finally, application of the proposed methods to a laboratory tested frame structure reveals that the forward and inverse substructuring methods are effective in model updating and damage identification.

Structural health monitoring (SHM) is a new engineering field with a growing tendency, based on technology development focused on data acquisition and analysis, to prevent possible damage in man-made structures and land's natural faults. The data are obtained from sensors and monitoring systems that allow detecting damages on structures, space vehicles, and land natural faults, to model their behavior under adverse scenarios, in order to search the detection of anomalies. Currently, there are many SHM systems with sensors based on different technologies like optical fiber, video cameras, optical scanners, wireless networks, and piezoelectric transducers, among others. In this context, the present book includes selected chapters with theoretical models and applications, to preserve infrastructure and prevent loss of human lives.

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.

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 papers in this volume provide an introduction to well known and established system identification methods for structural health monitoring and to more advanced, state-of-the-art tools, able to tackle the challenges associated with actual implementation. Starting with an overview on fundamental methods, introductory concepts are provided on the general framework of time and frequency domain, parametric and non-parametric methods, input-output or output only techniques. Cutting edge tools are introduced including, nonlinear system identification methods; Bayesian tools; and advanced modal identification techniques (such as the Kalman and particle filters, the fast Bayesian FFT method). Advanced computational tools for uncertainty quantification are discussed to provide a link between monitoring and structural integrity assessment. In addition, full scale applications and field deployments that illustrate the workings and effectiveness of the introduced monitoring schemes are demonstrated.

Comprising 102 papers presented by researchers from all over the world, the proceedings of this workshop contain current information about a variety of structural health monitoring technologies, as well as their current and potential applications in various fields. Emphasis is placed on those technologies that are promising for future applications in industry and government and the infrastructures that are needed to support such technological development. The content of the workshop is divided into keynote presentations (ten altogether), aerospace applications, general applications, civil applications, integration and systems, sensors, and signal processing and diagnostic methods. Includes the editor's summary report on the results of the panel discussions and presentations from the First International Workshop on Structural Health Monitoring held at Stanford U. in September 1997. Annotation c. Book News, Inc., Portland, OR (booknews.com)