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This volume contains the proceedings of the 13th International Conference on Damage Assessment of Structures DAMAS 2019, 9-10 July 2019, Porto, Portugal. It presents the expertise of scientists and engineers in academia and industry in the field of damage assessment, structural health monitoring and non-destructive evaluation. The proceedings covers all research topics relevant to damage assessment of engineering structures and systems including numerical simulations, signal processing of sensor measurements and theoretical techniques as well as experimental case studies.
Sensor Technologies for Civil Infrastructure, Volume 1: Sensing Hardware and Data Collection Methods for Performance Assessment, Second Edition, provides an overview of sensor hardware and its use in data collection. The first chapters provide an introduction to sensing for structural performance assessment and health monitoring, and an overview of commonly used sensors and their data acquisition systems. Further chapters address different types of sensor including piezoelectric transducers, fiber optic sensors, acoustic emission sensors, and electromagnetic sensors, and the use of these sensors for assessing and monitoring civil infrastructures. The new edition now includes chapters on machine learning methods and reliability analysis for structural health monitoring. All chapters have been revised to include the latest advances in materials (such as piezoelectric and mechanoluminescent materials), technologies (such as LIDAR), and applications. - Describes sensing hardware and data collection, covering a variety of sensors including LIDAR - Examines fiber optic systems, acoustic emission, piezoelectric sensors, electromagnetic sensors, terahertz technologies, ultrasonic methods, and radar and millimeter wave technology - Covers strain gauges, micro-electro-mechanical systems (MEMS), multifunctional materials and nanotechnology for sensing, and vision-based sensing and lasers - Includes new chapters on machine learning methods and reliability analysis
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)
Block-oriented Nonlinear System Identification deals with an area of research that has been very active since the turn of the millennium. The book makes a pedagogical and cohesive presentation of the methods developed in that time. These include: iterative and over-parameterization techniques; stochastic and frequency approaches; support-vector-machine, subspace, and separable-least-squares methods; blind identification method; bounded-error method; and decoupling inputs approach. The identification methods are presented by authors who have either invented them or contributed significantly to their development. All the important issues e.g., input design, persistent excitation, and consistency analysis, are discussed. The practical relevance of block-oriented models is illustrated through biomedical/physiological system modelling. The book will be of major interest to all those who are concerned with nonlinear system identification whatever their activity areas. This is particularly the case for educators in electrical, mechanical, chemical and biomedical engineering and for practising engineers in process, aeronautic, aerospace, robotics and vehicles control. Block-oriented Nonlinear System Identification serves as a reference for active researchers, new comers, industrial and education practitioners and graduate students alike.
Modal Analysis provides a detailed overview of the theory of analytical and experimental modal analysis and its applications. Modal Analysis is the processes of determining the inherent dynamic characteristics of any system and using them to formulate a mathematical model of the dynamic behavior of the system. In the past two decades it has become a major technological tool in the quest for determining, improving and optimizing dynamic characteristics of engineering structures. Its main application is in mechanical and aeronautical engineering, but it is also gaining widespread use in civil and structural engineering, biomechanical problems, space structures, acoustic instruments and nuclear engineering. - The only book to focus on the theory of modal analysis before discussing applications - A relatively new technique being utilized more and more in recent years which is now filtering through to undergraduate courses - Leading expert in the field
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.
Structural health monitoring is an extremely important methodology in evaluating the 'health' of a structure by assessing the level of deterioration and remaining service life of civil infrastructure systems. This book reviews key developments in research, technologies and applications in this area of civil engineering. It discusses ways of obtaining and analysing data, sensor technologies and methods of sensing changes in structural performance characteristics. It also discusses data transmission and the application of both individual technologies and entire systems to bridges and buildings.With its distinguished editors and international team of contributors, Structural health monitoring of civil infrastructure systems is a valuable reference for students in civil and structural engineering programs as well as those studying sensors, data analysis and transmission at universities. It will also be an important source for practicing civil engineers and designers, engineers and researchers developing sensors, network systems and methods of data transmission and analysis, policy makers, inspectors and those responsible for the safety and service life of civil infrastructure. - Reviews key developments in research, technologies and applications - Discusses systems used to obtain and analyse data and sensor technologies - Assesses methods of sensing changes in structural performance
Many engineering problems can be solved using a linear approximation. In the Finite Element Analysis (FEA) the set of equations, describing the structural behaviour is then linear K d = F (1.1) In this matrix equation, K is the stiffness matrix of the structure, d is the nodal displacements vector and F is the external nodal force vector. Characteristics of linear problems is that the displacements are proportional to the loads, the stiffness of the structure is independent on the value of the load level. Though behaviour of real structures is nonlinear, e.g. displacements are not proportional to the loads; nonlinearities are usually unimportant and may be neglected in most practical problems.