Structural Health Monitoring Based on Data Science Techniques - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Structural Health Monitoring Based on Data Science Techniques write by Alexandre Cury. This book was released on 2021-10-23. Structural Health Monitoring Based on Data Science Techniques available in PDF, EPUB and Kindle. 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
Data Driven Methods for Civil Structural Health Monitoring and Resilience - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Driven Methods for Civil Structural Health Monitoring and Resilience write by Mohammad Noori. This book was released on 2023-10-26. Data Driven Methods for Civil Structural Health Monitoring and Resilience available in PDF, EPUB and Kindle. 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.
Data-Centric Structural Health Monitoring
Data-Centric Structural Health Monitoring - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Centric Structural Health Monitoring write by Mohammad Noori. This book was released on 2023-09-04. Data-Centric Structural Health Monitoring available in PDF, EPUB and Kindle.
Structural Health Monitoring
Structural Health Monitoring - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Structural Health Monitoring write by Charles R. Farrar. This book was released on 2012-11-19. Structural Health Monitoring available in PDF, EPUB and Kindle. 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 Driven Methods for Civil Structural Health Monitoring and Resilience
Data Driven Methods for Civil Structural Health Monitoring and Resilience - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Driven Methods for Civil Structural Health Monitoring and Resilience write by Mohammad Noori. This book was released on 2023-10. Data Driven Methods for Civil Structural Health Monitoring and Resilience available in PDF, EPUB and Kindle. 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.
