System identification (SI) techniques are important in reducing gaps between the constructed structural systems and their structural design models and in health monitoring for damage detection. Modal-parameter SI and physical-parameter SI are two major branches in SI.Special character of this book: (1) The physical-parameter SI method explained in this book requires only two accelerometers for measurement of records. Furthermore only a simple manipulation of Fourier transformation is required.(2) The stiffness and damping can be identified simultaneously.(3) The modal parameter SI can supplement or support the result by the physical-parameter SI method.(4) In place of usual low-pass or high-pass filter techniques, a novel noise-bias compensation method is explained. Because the noise itself is not known in many cases, the identification and elimination of noise is a tough problem.(5) A new technique of system identification is explained in the case where an inner vibration source exists.(6) The accuracy of the explained SI methods is examined by the actual recorded data.(7) MATLAB codes are available.This book is intended for Structural Engineers, Mechanical Engineers, Researchers, Graduate and undergraduate students.

This book is organized around the various sensing techniques used to achieve structural health monitoring. Its main focus is on sensors, signal and data reduction methods and inverse techniques, which enable the identification of the physical parameters, affected by the presence of the damage, on which a diagnostic is established. Structural Health Monitoring is not oriented by the type of applications or linked to special classes of problems, but rather presents broader families of techniques: vibration and modal analysis; optical fibre sensing; acousto-ultrasonics, using piezoelectric transducers; and electric and electromagnetic techniques. Each chapter has been written by specialists in the subject area who possess a broad range of practical experience. The book will be accessible to students and those new to the field, but the exhaustive overview of present research and development, as well as the numerous references provided, also make it required reading for experienced researchers and engineers.

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.

The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.

The first complete introduction to health monitoring, encapsulating both technical information and practical case studies spanning the breadth of the subject. Written by a highly-respected figure in structural health monitoring, this book provides readers with the technical skills and practical understanding required to solve new problems encountered in the emerging field of health monitoring. The book presents a suite of methods and applications in loads identification (usage monitoring), in-situ damage identification (diagnostics), and damage and performance prediction (prognostics). Concepts in modelling, measurements, and data analysis are applied through real-world case studies to identify loading, assess damage, and predict the performance of structural components, as well as examine engine components, automotive accessories, aircraft parts, spacecraft components, civil structures and defence system components. In particular the book: provides the reader with a fundamental and practical understanding of the material; discusses models demonstrating the physical basis for health monitoring techniques; gives a detailed review of the best practices in dynamic measurements including sensing; presents numerous data analysis techniques using model- and signal-based methods; discusses case studies involving real-world applications of health monitoring; offers end-of-chapter problems to enhance the study of the topic for students and instructors; and includes an accompanying website with MATLAB programs providing hands-on training to readers for writing health monitoring model simulation and data analysis algorithms. Health Monitoring of Structural Materials and Components is an excellent introductory text for newcomers to the subject as well as an excellent study tool for students and lecturers. Practitioners and researchers, those with a greater understanding and application of the technical skills involved, will also find this essential reading as a reference text to address current and future challenges in this field. The wide variety of case studies will appeal to a broad spectrum of engineers in the aerospace, civil, mechanical, machinery and defence communities.

Structural Health Management (SHM) is a key part of the Integrated Vehicle Health Management (IVHM) approach, whose main aim is to develop an integrated end-to-end system to monitor the overall health of a vehicle. Structural Health Monitoring: Current State and Future Trends, edited by Professor Alessandro Pegoretti, a scholar from the University of Trento in Italy, introduces the reader to recent developments involved in health monitoring of aerospace structures. The chapters, represented by seminal SAE International technical papers, offer an overview of the most recent advances in the sensing techniques for SHM, analysis of SHM data and its applications in aerospace. SHM can allow a continuous in-service inspection of the vehicle, thus reducing the cost associated with manual inspection at predetermined time intervals. The availability of reliable information on the loading conditions and health state of structural components by the implementation of SHM can be beneficial for several reasons, such as: • To prevent catastrophic failure • To reduce the number and the cost of unnecessary inspections • To improve the design of structural parts, with a reduction of the weight and the costs of overdesigned components Structural Health Monitoring: Current State and Future Trends offers a unique perspective on this field.

Proceedings of the Tenth International Workshop on Structural Health Monitoring, September 1–3, 2015. Selected research on the entire spectrum of structural health techniques and areas of applicationAvailable in print, complete online text download or individual articles. Series book comprising two volumes provides selected international research on the entire spectrum of structural health monitoring techniques used to diagnose and safeguard aircraft, vehicles, buildings, civil infrastructure, ships and railroads, as well as their components such as joints, bondlines, coatings and more. Includes special sections on system design, signal processing, multifunctional materials, sensor distribution, embedded sensors for monitoring composites, reliability and applicability in extreme environments. The extensive contents can be viewed below.

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.

This book is a collection of articles covering the six lecture courses given at the CISM School on this topic in 2008. It features contributions by established international experts and offers a coherent and comprehensive overview of the state-of-the art research in the field, thus addressing both postgraduate students and researchers in aerospace, mechanical and civil engineering.

Special Topics in Structural Dynamics & Experimental Techniques, Volume 5: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019, the fifth volume of eight from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Analytical Methods Emerging Technologies for Structural Dynamics Engineering Extremes Experimental Techniques Finite Element Techniques General Topics