Vibration Based Techniques For Damage Detection And Localization In Engineering Structures

In the oil and gas industries, large companies are endeavoring to find and utilize efficient structural health monitoring methods in order to reduce maintenance costs and time. Through an examination of the vibration-based techniques, this title addresses theoretical, computational and experimental methods used within this trend. By providing comprehensive and up-to-date coverage of established and emerging processes, this book enables the reader to draw their own conclusions about the field of vibration-controlled damage detection in comparison with other available techniques. The chapters offer a balance between laboratory and practical applications, in addition to detailed case studies, strengths and weakness are drawn from a broad spectrum of information. Contents: Machine Learning Algorithms for Damage Detection (Eloi Figueiredo and Adam Santos)Data-Driven Methods for Vibration-Based Monitoring Based on the Singular Spectrum Analysis (Irina Trendafilova, David Garcia and Hussein Al-Bugharbee)Experimental Investigation of Delamination Effects on Modal Damping of a CFRP Laminate, Using a Statistical Rationalization Approach (Majid Khazaee, Ali Salehzadeh Nobari and M H Ferri Aliabadi)Problem of Detecting Damage Through Natural Frequency Changes (Gilbert-Rainer Gillich, Nuno N N Maia and Ion Cornel Mituletu)Damage Localization Based on Modal Response Measured with Shearography (J V Araújo dos Santos and H Lopes)Novel Techniques for Damage Detection Based on Mode Shape Analysis (Wieslaw Ostachowicz, Maciej Radzieński, Maosen Cao and Wei Xu)Damage Identification Based on Response Functions in Time and Frequency Domains (R P C Sampaio, T A N Silva, N M M Maia and S Zhong) Readership: Engineers, technicians, researchers working in the field of vibration-based techniques. Keywords: Structural Health Monitoring;SHM;Vibration-based SHM;Machine Learning;Time Domain Data Analysis;Frequency Domain Data Analysis;Damage IndexReview: Key Features: The 1st book to address theoretical, computational and experimental methodsThe book provides an up to date and comprehensive coverage of established and emerging techniques within the field of vibration-controlled damage detectionExcellent balance between laboratory and practical applicationsMany case studies in various chapters that help the reader to identify weak and strong points of various techniques

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.

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

Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

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.

This volume presents peer-reviewed contributions from the 10th International Conference on Experimental Vibration Analysis for Civil Engineering Structures (EVACES), held in Milan, Italy on August 30-September 1, 2023. The event brought together engineers, scientists, researchers, and practitioners, providing a forum for discussing and disseminating the latest developments and achievements in all major aspects of dynamic testing for civil engineering structures, including instrumentation, sources of excitation, data analysis, system identification, monitoring and condition assessment, in-situ and laboratory experiments, codes and standards, and vibration mitigation. The topics included but were not limited to: damage identification and structural health monitoring; testing, sensing and modeling; vibration isolation and control; system and model identification; coupled dynamical systems (including human–structure, vehicle–structure, and soil–structure interaction); and application of advanced techniques involving the Internet of Things, robot, UAV, big data and artificial intelligence.

Sensors and Instrumentation, Aircraft/Aerospace and Energy Harvesting, Volume 7: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the seventh volume of nine 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 Shock & Vibration, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing including papers on: Alternative Sensing & Acquisition Active Controls Instrumentation Aircraft/Aerospace & Aerospace Testing Techniques Energy Harvesting