Structural Health Monitoring Based on Data Science Techniques
Title | Structural Health Monitoring Based on Data Science Techniques PDF eBook |
Author | Alexandre Cury |
Publisher | Springer Nature |
Pages | 490 |
Release | 2021-10-23 |
Genre | Computers |
ISBN | 3030817164 |
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.
Data Driven Methods for Civil Structural Health Monitoring and Resilience
Title | Data Driven Methods for Civil Structural Health Monitoring and Resilience PDF eBook |
Author | Mohammad Noori |
Publisher | CRC Press |
Pages | 358 |
Release | 2023-10-26 |
Genre | Technology & Engineering |
ISBN | 1000965554 |
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.
Structural Health Monitoring
Title | Structural Health Monitoring PDF eBook |
Author | Charles R. Farrar |
Publisher | John Wiley & Sons |
Pages | 735 |
Release | 2012-11-19 |
Genre | Technology & Engineering |
ISBN | 1118443217 |
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.
Data-Centric Structural Health Monitoring
Title | Data-Centric Structural Health Monitoring PDF eBook |
Author | Mohammad Noori |
Publisher | Walter de Gruyter GmbH & Co KG |
Pages | 274 |
Release | 2023-09-04 |
Genre | Technology & Engineering |
ISBN | 3110791420 |
Data Driven Methods for Civil Structural Health Monitoring and Resilience
Title | Data Driven Methods for Civil Structural Health Monitoring and Resilience PDF eBook |
Author | Mohammad Noori |
Publisher | CRC Press |
Pages | 0 |
Release | 2023-10 |
Genre | |
ISBN | 9781032308371 |
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.
Long-term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning
Title | Long-term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning PDF eBook |
Author | ALIREZA. BEHKAMAL ENTEZAMI (BAHAREH. DE MICHELE, CARLO.) |
Publisher | Springer Nature |
Pages | 123 |
Release | 2024 |
Genre | Machine learning |
ISBN | 3031539958 |
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.
European Workshop on Structural Health Monitoring
Title | European Workshop on Structural Health Monitoring PDF eBook |
Author | Piervincenzo Rizzo |
Publisher | Springer Nature |
Pages | 1129 |
Release | 2022-06-15 |
Genre | Technology & Engineering |
ISBN | 3031072588 |
This volume gathers the latest advances, innovations, and applications in the field of structural health monitoring (SHM) and more broadly in the fields of smart materials and intelligent systems, as presented by leading international researchers and engineers at the 10th European Workshop on Structural Health Monitoring (EWSHM), held in Palermo, Italy on July 4-7, 2022. The volume covers highly diverse topics, including signal processing, smart sensors, autonomous systems, remote sensing and support, UAV platforms for SHM, Internet of Things, Industry 4.0, and SHM for civil structures and infrastructures. The contributions, which are published after a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists.