Structural Damage Detection Using Advanced Data Processing and Analysis PDF Download

Author: Matthew S. MacRostie
Publisher:
ISBN:
Category : Bridges
Languages : en
Pages : 300

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Book Description

Author: Tadeusz Stepinski
Publisher: John Wiley & Sons
ISBN: 1118536126
Category : Technology & Engineering
Languages : en
Pages : 342

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Book Description
Structural Health Monitoring (SHM) is the interdisciplinary engineering field devoted to the monitoring and assessment of structural health and integrity. SHM technology integrates non-destructive evaluation techniques using remote sensing and smart materials to create smart self-monitoring structures characterized by increased reliability and long life. Its applications are primarily systems with critical demands concerning performance where classical onsite assessment is both difficult and expensive. Advanced Structural Damage Detection: From Theory to Engineering Applications is written by academic experts in the field and provides students, engineers and other technical specialists with a comprehensive review of recent developments in various monitoring techniques and their applications to SHM. Contributing to an area which is the subject of intensive research and development, this book offers both theoretical principles and feasibility studies for a number of SHM techniques. Key features: Takes a multidisciplinary approach and provides a comprehensive review of main SHM techniques Presents real case studies and practical application of techniques for damage detection in different types of structures Presents a number of new/novel data processing algorithms Demonstrates real operating prototypes Advanced Structural Damage Detection: From Theory to Engineering Applications is a comprehensive reference for researchers and engineers and is a useful source of information for graduate students in mechanical and civil engineering

Author: Alexandre Cury
Publisher: Springer Nature
ISBN: 3030817164
Category : Computers
Languages : en
Pages : 490

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Book Description
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.

Author: M. Arif Wani
Publisher: Springer
ISBN: 9789811567582
Category : Technology & Engineering
Languages : en
Pages : 300

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Book Description
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.

Author: Jaime Vitola
Publisher:
ISBN:
Category : Computers
Languages : en
Pages :

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Book Description
Structural health monitoring (SHM) is an important research area, which interest is the damage identification process. Different information about the state of the structure can be obtained in the process, among them, detection, localization and classification of damages are mainly studied in order to avoid unnecessary maintenance procedures in civilian and military structures in several applications. To carry out SHM in practice, two different approaches are used, the first is based on modelling which requires to build a very detailed model of the structure, while the second is by means of data-driven approaches which use information collected from the structure under different structural states and perform an analysis by means of data analysis . For the latter, statistical analysis and pattern recognition have demonstrated its effectiveness in the damage identification process because real information is obtained from the structure through sensors installed permanently to the observed object allowing a real-time monitoring. This chapter describes a damage detection and classification methodology, which makes use of a piezoelectric active system which works in several actuation phases and that is attached to the structure under evaluation, principal component analysis, and machine learning algorithms working as a pattern recognition methodology. In the chapter, the description of the developed approach and the results when it is tested in one aluminum plate are also included.

Author: ALIREZA. BEHKAMAL ENTEZAMI (BAHAREH. DE MICHELE, CARLO.)
Publisher: Springer Nature
ISBN: 3031539958
Category : Machine learning
Languages : en
Pages : 123

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Book Description
This book offers an in-depth investigation into the complexities of long-term structural health monitoring (SHM) in civil structures, specifically focusing on the challenges posed by small data and environmental and operational changes (EOCs). Traditional contact-based sensor networks in SHM produce large amounts of data, complicating big data management. In contrast, synthetic aperture radar (SAR)-aided SHM often faces challenges with small datasets and limited displacement data. Additionally, EOCs can mimic the structural damage, resulting in false errors that can critically affect economic and safety issues. Addressing these challenges, this book introduces seven advanced unsupervised learning methods for SHM, combining AI, data sampling, and statistical analysis. These include techniques for managing datasets and addressing EOCs. Methods range from nearest neighbor searching and Hamiltonian Monte Carlo sampling to innovative offline and online learning frameworks, focusing on data augmentation and normalization. Key approaches involve deep autoencoders for data processing and novel algorithms for damage detection. Validated using simulated data from the I-40 Bridge, USA, and real-world data from the Tadcaster Bridge, UK, these methods show promise in addressing SAR-aided SHM challenges, offering practical tools for real-world applications. The book, thereby, presents a comprehensive suite of innovative strategies to advance the field of SHM.

Author: Nobari Ali Salehzadeh
Publisher: World Scientific
ISBN: 178634498X
Category : Technology & Engineering
Languages : en
Pages : 256

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Book Description
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

Author: Mohammad Noori
Publisher: CRC Press
ISBN: 1000965554
Category : Technology & Engineering
Languages : en
Pages : 358

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Book Description
Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.

Author: James Long (S.M.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 103

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Book Description
Measuring and analysing the vibration of structures using sensors can help identify and detect damage, potentially prolonging the life of structures and preventing disasters. Wireless sensor systems promise to make this technology more affordable and more widely applicable. Data driven structural health monitoring methodologies take raw signals obtained from sensor networks, and process them to obtain damage sensitive features. New measurements are then compared with baselines to detect damage. Because damage-sensitive features also exhibit variation due to environmental and operational changes, these comparisons are not always straightforward and sophisticated statistical analysis is necessary in order to detect abnormal changes in the damage sensitive features. In this thesis, an automated methodology which uses the one-class support vector machine (OCSVM) for damage detection and localisation is proposed. The OCSVM is a nonparametric machine learning method which can accurately classify new data points based only on data from the baseline condition of the structure. This methodology combines feature extraction, by means of autoregressive modeling, and wavelet analysis, with statistical pattern recognition using the OCSVM. The potential for embedding this damage detection methodology at the sensor level is also discussed. Efficacy is demonstrated using real experimental data from a steel frame laboratory structure, for various damage locations and scenarios.

Author: Wieslaw Ostachowicz
Publisher: Springer Science & Business Media
ISBN: 3709113903
Category : Science
Languages : en
Pages : 433

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Book Description
A motivation for structural health monitoring. Structural health monitoring of aircraft structures. Vibration-based damage diagnosis and monitoring of external loads.Statistical time series methods for vibration based structural health monitoring. Fiber optic sensors. Damage localisation using elastic waves propagation methods experimental techniques. Application for wind turbine blades. Experts actively working in structural health monitoring and control techniques present the current research, areas of application and tendencies for the future of this technology, including various design issues involved. Examples using some of the latest hardware and software tools, experimental data from small scale laboratory demonstrators and measurements made on real structures illustrate the book. It will be a reference for professionals and students in the areas of engineering, applied natural sciences and engineering management.