Download or read book Structural Health Monitoring Based on Data Science Techniques written by Alexandre Cury and published by Springer Nature. This book was released on 2021-10-23 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Real time Structural Health Monitoring Using Machine Learning Algorithm written by Haozhi Tan and published by . This book was released on 2018 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2010–2012 Canterbury earthquakes, the 2013 Cook Strait earthquakes and the 2016 Kaikoura earthquake highlighted that damaging earthquake scenarios are very real and they can occur at anytime and anywhere in New Zealand. It is desirable to ascertain and track the structural performance and integrity for buildings for public safety and emergency management. Seismic instrumentation has often been promoted as the solution to this requirement. Real-time Structural Health Monitoring (SHM) algorithms in a seismic instrumentation system continuously translate structural monitoring data into building state prediction. The overall goal of this study was to evaluate and improve the current damage detection algorithms implemented in civil structures, which made real-time SHM possible. The aim was to improve occupant safety and accelerate the rate of recovery for individual buildings, and by extension improved community resilience to extreme events. A key issue influencing the performance of the current algorithms is varying operational and environmental conditions. This project studied a year’s worth of instrumented building data from the GNS Science building in Lower Hutt, with the aim of evaluating the influence of different environmental conditions on predicted building motion and corresponding dynamic characteristics, including building acceleration amplitudes and modal frequencies. The environmental conditions considered were temperature, wind speed, relative humidity and human activity. The results of the analysis demonstrated operational and environmental conditions have a noticeable effect on building dynamic properties estimation. In 2016, the CentrePort BNZ building suffered severe damage to its structural members and its non-structural members as a result of the 2016 Mw 7.8 Kaikoura earthquake. Coincidently, extensive non-structural damage also occurred in this building in the 2013 Cook Strait earthquakes. The recorded earthquake response of the CentrePort BNZ building during the two earthquakes was analysed to gain a comprehensive understanding of the building dynamic responses during earthquakes. A damage detection algorithm using autoregressive (AR) models with Mahalanobis squared distance (MSD) was applied to the instrumented building data for two years of data. It successfully detected the change in building damage state correlating to actual observations due to the two earthquakes. A parametric study was conducted to consider the effect of AR orders and exceedance probability on an optimum threshold for signalling damage in MSD-based damage detection algorithms. The results indicated that both factors are important parameters affecting the - ii - detection accuracy of the algorithm. Besides, this algorithm detected damage accurately under varying operational and environmental effects. A new damage detection method called the MSDAANN algorithm was proposed based on combining MSD and auto-associative neural network (AANN) approaches. The performance of the proposed algorithm was evaluated using data from the ASCE benchmark structure and through an analysis of receiver operating curves (ROCs). The results showed that the proposed MSD-AANN algorithm performed better than MSD-based algorithm or AANN-based algorithm. In addition, proposed MSD-AANN algorithm is selfcalibrated, it can be automatically applied to datasets and obtained damage detection results in a very short time with high accuracy even when the structure was operating under varying operational and environmental conditions. These represent improvements beyond current solutions and present great potential for real-time SHM applications. Microsoft Azure Machine Learning Studio (MLS) is a powerful machine learning tool, with which data scientists and developers can quickly build, test, and develop predictive models using state-of-the-art machine learning algorithms. But the effective application of machine learning algorithms in SHM applications remains a challenge for researchers. A parametric study of a cloud-based machine learning damage detection algorithm using two-class boosted decision tree was therefore conducted to investigate the effects of input length and the number of sensors on damage detection accuracy of a cloud-based machine learning algorithm. To facilitate a comparison, an MSD-based damage detection algorithm was also applied to the same data sets. The parametric study showed that both input data length and sensor numbers greatly affected the damage detection accuracy. The detection accuracy of both cloud-based machine learning and MSD-based algorithms increased when more data was used. More data in this instance means greater length of input data or longer time-duration preceding a prediction. Cloud-based machine learning algorithm was more accurate than traditional MSD algorithm for the same input data length. Moreover, cloud-based machine learning algorithm reached to 80% of detection accuracy using only 160-second of input data which there is a significant proof of concept and achievement towards real-time damage detection in a real-world SHM scenario. The parametric analysis also found that only three sensors, located at the top, middle, and bottom of the building, were sufficient to achieve over 85% damage detection accuracy when cloud-based machine learning algorithm was used. For 90% accurate damage detection, the cloud-based machine learning algorithm required 10 minutes of input data. Accounting for 2-minute computation time, it meant that 90% accurate damage prediction for a very complex building could be achieved within 12 minutes. The cloud-based machine learning algorithm, therefore, have great potential for achieving very near real-time damage detection. - iii - Significant contributions of this work include (i) understanding how varying operational and environmental conditions affect building response measurements; (ii) demonstrating successful time-critical damage detection in a real-world building damaged in two major earthquakes using MSD-based damage detection algorithm; (iii) providing a framework to guide the selection of input parameters when using MSD-based damage detection algorithm; (iv) proposing a novel real-time MSD-AANN damage detection algorithm that is more accurate and faster than traditional algorithms. (v) introducing a cloud-based machine learning algorithm which utilises Microsoft Azure Machine Learning Studio (MLS) to execute damage detection processes.

Download or read book Structural Health Monitoring written by Charles R. Farrar and published by John Wiley & Sons. This book was released on 2012-11-19 with total page 735 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Long term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning written by ALIREZA. BEHKAMAL ENTEZAMI (BAHAREH. DE MICHELE, CARLO.) and published by Springer Nature. This book was released on 2024 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Civil Structural Health Monitoring written by Carlo Rainieri and published by Springer Nature. This book was released on 2021-08-24 with total page 1015 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book New Trends in Vibration Based Structural Health Monitoring written by Arnaud Deraemaeker and published by Springer Science & Business Media. This book was released on 2012-01-28 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book EASEC16 written by Chien Ming Wang and published by Springer Nature. This book was released on 2020-12-22 with total page 2093 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents articles from The 16th East Asian-Pacific Conference on Structural Engineering and Construction, 2019, held in Brisbane, Australia. It provides a forum for professional engineers, academics, researchers and contractors to present recent research and developments in structural engineering and construction.​

Download or read book Structural Health Monitoring Damage Detection Volume 7 written by Christopher Niezrecki and published by Springer. This book was released on 2017-03-20 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structural Health Monitoring & Damage Detection, Volume 7: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics, 2017, the seventh volume of ten 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 Structural Health Monitoring & Damage Detection, including papers on: Structural Health Monitoring Damage Detection System Identification Active Controls

Download or read book Structural Health Monitoring written by Daniel Balageas and published by John Wiley & Sons. This book was released on 2010-01-05 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Driven Methods for Civil Structural Health Monitoring and Resilience written by Mohammad Noori and published by CRC Press. This book was released on 2023-10-26 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Structural Health Monitoring For Advanced Composite Structures written by M H Ferri Aliabadi and published by World Scientific. This book was released on 2017-12-18 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structural health monitoring (SHM) is a relatively new and alternative way of non-destructive inspection (NDI). It is the process of implementing a damage detection and characterization strategy for composite structures. The basis of SHM is the application of permanent fixed sensors on a structure, combined with minimum manual intervention to monitor its structural integrity. These sensors detect changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance.This book's primary focus is on the diagnostics element of SHM, namely damage detection in composite structures. The techniques covered include the use of Piezoelectric transducers for active and passive Ultrasonics guided waves and electromechanical impedance measurements, and fiber optic sensors for strain sensing. It also includes numerical modeling of wave propagation in composite structures. Contributed chapters written by leading researchers in the field describe each of these techniques, making it a key text for researchers and NDI practitioners as well as postgraduate students in a number of specialties including materials, aerospace, mechanical and computational engineering.

Download or read book Structural Health Monitoring of Large Civil Engineering Structures written by Hua-Peng Chen and published by John Wiley & Sons. This book was released on 2018-01-29 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical review of key developments and latest advances in Structural Health Monitoring technologies applied to civil engineering structures, covering all aspects required for practical application Structural Health Monitoring (SHM) provides the facilities for in-service monitoring of structural performance and damage assessment, and is a key element of condition based maintenance and damage prognosis. This comprehensive book brings readers up to date on the most important changes and advancements in the structural health monitoring technologies applied to civil engineering structures. It covers all aspects required for such monitoring in the field, including sensors and networks, data acquisition and processing, damage detection techniques and damage prognostics techniques. The book also includes a number of case studies showing how the techniques can be applied in the development of sustainable and resilient civil infrastructure systems. Structural Health Monitoring of Large Civil Engineering Structures offers in-depth chapter coverage of: Sensors and Sensing Technology for Structural Monitoring; Data Acquisition, Transmission, and Management; Structural Damage Identification Techniques; Modal Analysis of Civil Engineering Structures; Finite Element Model Updating; Vibration Based Damage Identification Methods; Model Based Damage Assessment Methods; Monitoring Based Reliability Analysis and Damage Prognosis; and Applications of SHM Strategies to Large Civil Structures. Presents state-of-the-art SHM technologies allowing asset managers to evaluate structural performance and make rational decisions Covers all aspects required for the practical application of SHM Includes case studies that show how the techniques can be applied in practice Structural Health Monitoring of Large Civil Engineering Structures is an ideal book for practicing civil engineers, academics and postgraduate students studying civil and structural engineering.

Download or read book Real Time Structural Health Monitoring of Vibrating Systems written by Basuraj Bhowmik and published by CRC Press. This book was released on 2022-09-22 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Targeted at researchers and practitioners in the field of science and engineering, the book provides an introduction to real time structural health monitoring. Most work to date is based on algorithms that require windowing of the accumulated data, this work presents a coherent transition from the traditional batch mode practice to a recently developed array of recursive approaches. The book mainly focuses on the theoretical development and engineering applications of algorithms that are based on first order perturbation (FOP) techniques. The development of real time algorithms aimed at identifying the structural systems and the inflicted damage, online, through theoretical approaches paves the way for an in-depth understanding of the discussed topics. It then continues to demonstrate the solution to a class of inverse dynamic problems through numerically simulated systems. Extensive theoretical derivations supported by mathematical formulations, pivoted around the simple concepts of eigenspace updates, forms the key cornerstone of the book. The output response streaming in real time from multi degree of freedom systems provide key information about the system’s health that is subsequently utilized to identify the modal parameters and the damage, in real time. Damage indicators connotative of the nature, instant and location of damage, identified in a single framework are developed in the light of real time damage case studies. Backed by a comprehensive assortment of experimental test-beds, this book includes demonstrations to emulate real life damage scenarios under controlled laboratory conditions. Applicability of the proposed recursive methods towards practical problems demonstrate their robustness as viable candidates for real time structural health monitoring.

Download or read book Structural Health Monitoring SHM in Aerospace Structures written by Fuh-Gwo Yuan and published by Woodhead Publishing. This book was released on 2016-03-01 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structural Health Monitoring (SHM) in Aerospace Structures provides readers with the spectacular progress that has taken place over the last twenty years with respect to the area of Structural Health Monitoring (SHM). The widespread adoption of SHM could both significantly improve safety and reduce maintenance and repair expenses that are estimated to be about a quarter of an aircraft fleet’s operating costs. The SHM field encompasses transdisciplinary areas, including smart materials, sensors and actuators, damage diagnosis and prognosis, signal and image processing algorithms, wireless intelligent sensing, data fusion, and energy harvesting. This book focuses on how SHM techniques are applied to aircraft structures with particular emphasis on composite materials, and is divided into four main parts. Part One provides an overview of SHM technologies for damage detection, diagnosis, and prognosis in aerospace structures. Part Two moves on to analyze smart materials for SHM in aerospace structures, such as piezoelectric materials, optical fibers, and flexoelectricity. In addition, this also includes two vibration-based energy harvesting techniques for powering wireless sensors based on piezoelectric electromechanical coupling and diamagnetic levitation. Part Three explores innovative SHM technologies for damage diagnosis in aerospace structures. Chapters within this section include sparse array imaging techniques and phase array techniques for damage detection. The final section of the volume details innovative SHM technologies for damage prognosis in aerospace structures. This book serves as a key reference for researchers working within this industry, academic, and government research agencies developing new systems for the SHM of aerospace structures and materials scientists. Provides key information on the potential of SHM in reducing maintenance and repair costs Analyzes current SHM technologies and sensing systems, highlighting the innovation in each area Encompasses chapters on smart materials such as electroactive polymers and optical fibers

Download or read book Human and Machine Learning written by Jianlong Zhou and published by Springer. This book was released on 2018-06-07 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

Download or read book Fibre Optic Methods for Structural Health Monitoring written by Branko Glisic and published by John Wiley & Sons. This book was released on 2008-03-11 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of fibre optic sensors in structural health monitoring has rapidly accelerated in recent years. By embedding fibre optic sensors in structures (e.g. buildings, bridges and pipelines) it is possible to obtain real time data on structural changes such as stress or strain. Engineers use monitoring data to detect deviations from a structure’s original design performance in order to optimise the operation, repair and maintenance of a structure over time. Fibre Optic Methods for Structural Health Monitoring is organised as a step-by-step guide to implementing a monitoring system and includes examples of common structures and their most-frequently monitored parameters. This book: presents a universal method for static structural health monitoring, using a technique with proven effectiveness in hundreds of applications worldwide; discusses a variety of different structures including buildings, bridges, dams, tunnels and pipelines; features case studies which describe common problems and offer solutions to those problems; provides advice on establishing mechanical parameters to monitor (including deformations, rotations and displacements) and on placing sensors to achieve monitoring objectives; identifies methods for interpreting data according to construction material and shows how to apply numerical concepts and formulae to data in order to inform decision making. Fibre Optic Methods for Structural Health Monitoring is an invaluable reference for practising engineers in the fields of civil, structural and geotechnical engineering. It will also be of interest to academics and undergraduate/graduate students studying civil and structural engineering.

Download or read book Deep Learning Applications Volume 2 written by M. Arif Wani and published by Springer. This book was released on 2020-12-14 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.