Structural Health Monitoring
Structural Health Monitoring

Author: Daniel Balageas

Publisher: John Wiley & Sons

Published: 2010-01-05

Total Pages: 496

ISBN-13: 0470394404

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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.

Structural Health Monitoring and Detection of Progressive and Existing Damage Using Artificial Neural Networks-Based System Identification
Structural Health Monitoring and Detection of Progressive and Existing Damage Using Artificial Neural Networks-Based System Identification

Author:

Publisher:

Published: 2003

Total Pages:

ISBN-13:

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In recent decades, the growing number of civil and aerospace structures has accelerated the development of damage detection and health monitoring approaches. Many are based upon non-destructive and non-invasive sensing and analysis of structural characteristics, and most use structural response information to identify the existence, location, and time of damage. Model based techniques such as parametric and non-parametric system identification seek to identify changes in the parameters of a dynamic structural model. Restoring forces in real structures can exhibit highly non-linear characteristics, thus accurate non-linear system identification is critical. Parametric system identification approaches are commonly used, but these require a priori assumptions about restoring force characteristics. Non-parametric approaches do not require such information, but they typically lack direct associations between the model and the structural dynamics, providing limited utility for accurate health monitoring and damage detection. This dissertation presents a novel 'Intelligent Parameter Varying' (IPV) health monitoring and damage detection technique that accurately detects the existence, location, and time of damage occurrence without any assumptions about the constitutive nature of structural non-linearities. This technique combines the advantages of parametric techniques with the non-parametric capabilities of artificial neural networks by incorporating artificial neural networks into a traditional parametric model. This hybrid approach benefits from the effectiveness of traditional modeling approaches and from the adaptation and learning capabilities of artificial neural networks. The generality of this IPV approach makes it suitable to a wide range of dynamic systems, including those with non-linear and time-varying characteristics. This IPV technique is demonstrated using a lumped-mass structural model with an embedded array of artificial neural networks. These networks i.

Deep Learning Applications, Volume 2
Deep Learning Applications, Volume 2

Author: M. Arif Wani

Publisher: Springer

Published: 2020-12-14

Total Pages: 300

ISBN-13: 9789811567582

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This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Structural Health Monitoring Using Emerging Signal Processing Approaches with Artificial Intelligence Algorithms
Structural Health Monitoring Using Emerging Signal Processing Approaches with Artificial Intelligence Algorithms

Author: Chunwei Zhang

Publisher: CRC Press

Published: 2024-11-06

Total Pages: 245

ISBN-13: 1040150063

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Structural health monitoring is a powerful tool across civil, mechanical, automotive, and aerospace engineering, allowing the assessment and measurement of physical parameters in real time. Processing changes in the vibration signals of a dynamic system can detect, locate, and quantify any damage existing in the system. This book presents a comprehensive state‐of‐the‐art review of the applications in time, frequency, and time‐frequency domains of signal‐processing techniques for damage perception, localization, and quantification in various structural systems. Experimental investigations are illustrated, including the development of a set of damage indices based on the signal features extracted through various signal‐processing techniques to evaluate sensitivity in damage identification. Chapters summarize the application of the Hilbert–Huang transform based on three decomposition methods such as empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. Also, the chapters assess the performance and sensitivity of different approaches, including multiple signal classification and empirical wavelet transform techniques in damage detection and quantification. Artificial neural networks for automated damage identification are introduced. This book suits students, engineers, and researchers who are investigating structural health monitoring, signal processing, and damage identification of structures.

Civil Structural Health Monitoring
Civil Structural Health Monitoring

Author: Carlo Rainieri

Publisher: Springer Nature

Published: 2021-08-24

Total Pages: 1015

ISBN-13: 303074258X

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This volume gathers the latest advances and innovations in the field of structural health monitoring, as presented at the 8th Civil Structural Health Monitoring Workshop (CSHM-8), held on March 31–April 2, 2021. It discusses emerging challenges in civil SHM and more broadly in the fields of smart materials and intelligent systems for civil engineering applications. The contributions cover a diverse range of topics, including applications of SHM to civil structures and infrastructures, innovative sensing solutions for SHM, data-driven damage detection techniques, nonlinear systems and analysis techniques, influence of environmental and operational conditions, aging structures and infrastructures in hazardous environments, and SHM in earthquake prone regions. Selected by means of a rigorous peer-review process, they will spur novel research directions and foster future multidisciplinary collaborations.

Structural Health Monitoring Based on Data Science Techniques
Structural Health Monitoring Based on Data Science Techniques

Author: Alexandre Cury

Publisher: Springer Nature

Published: 2021-10-23

Total Pages: 490

ISBN-13: 3030817164

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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.

Applying Neural Networks
Applying Neural Networks

Author: Kevin Swingler

Publisher: Morgan Kaufmann

Published: 1996

Total Pages: 348

ISBN-13: 9780126791709

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This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.