Author: Jaime Vitola Publisher: ISBN: Category : Computers Languages : en Pages :
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: Alexandre Cury Publisher: Springer Nature ISBN: 3030817164 Category : Computers Languages : en Pages : 490
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: Mohammad Noori Publisher: CRC Press ISBN: 1000965554 Category : Technology & Engineering Languages : en Pages : 358
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: Daniel Balageas Publisher: John Wiley & Sons ISBN: 0470394404 Category : Technology & Engineering Languages : en Pages : 496
Book Description
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.
Author: M. Arif Wani Publisher: Springer ISBN: 9789811567582 Category : Technology & Engineering Languages : en Pages : 300
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: Ramin Madarshahian Publisher: Springer Nature ISBN: 3031041224 Category : Computers Languages : en Pages : 158
Book Description
Data Science in Engineering, Volume 9: Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022, the nineth 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 Data Science in Engineering, including papers on: Novel Data-driven Analysis Methods Deep Learning Gaussian Process Analysis Real-time Video-based Analysis Applications to Nonlinear Dynamics and Damage Detection High-rate Structural Monitoring and Prognostics
Author: James Long (S.M.) Publisher: ISBN: Category : Languages : en Pages : 103
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: Charles R. Farrar Publisher: John Wiley & Sons ISBN: 1118443217 Category : Technology & Engineering Languages : en Pages : 735
Book Description
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.
Author: Zilong Wang Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Many data-driven approaches have been developed in recent decades to address problems with damage detection for civil infrastructure. According to training modes of the statistical models or neural networks adopted in the studies, these data-driven damage detection methods can be roughly categorized into supervised modes and unsupervised modes. Supervised damage detection approaches require the recorded data (i.e., ground truth data) from the undamaged and various damaged structural scenarios to train statistical models or neural networks. Then, the trained models or networks can be utilized to detect damage using future data measured from unknown structural scenarios. However, acquiring numerous training datasets from various damage scenarios for the monitored structures is time-consuming and costly, and it is hard to obtain many damage scenarios for the infrastructures in service. To address these challenges encountered in practice, structural damage detection in unsupervised learning mode has become increasingly interesting to researchers. The proposed unsupervised damage detection methods in my study require only the data measured from undamaged structural scenarios or baseline structures in their training processes. This thesis aims to propose novel unsupervised damage detection methods to address the problems facing structural damage detection and localization. Specifically, a novel unsupervised damage detection approach using a deep learning technique is proposed for detecting damage in a simulated multi-story frame and a laboratory-scale steel bridge model in Chapter 3. Additionally, a comparative study with an advanced unsupervised damage detection approach using deep restricted Boltzmann machines is carried out to evaluate their effectiveness of detecting light damage in the steel bridge. In Chapter 4, an unsupervised novelty detection method based on an original technique of fast clustering is developed to roughly locate the damage positions in a small-scale building frame. To verify the effectiveness of the developed method for structural damage localization, several existing machine learning and deep learning methods are developed and converted to the uniform unsupervised novelty detection mode in Chapter 5 for extensive comparative studies.
Author: Ali Salehzadeh Nobari Publisher: World Scientific ISBN: 178634498X Category : Technology & Engineering Languages : en Pages : 256
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.
Author: Jaime Vitola
Author: Alexandre Cury
Author: Mohammad Noori
Author: Daniel Balageas
Author: M. Arif Wani
Author: Ramin Madarshahian
Author: James Long (S.M.)
Author: Charles R. Farrar
Author: Zilong Wang
Author: Ali Salehzadeh Nobari