Structural Health Monitoring of Large Structures Using Acoustic Emission

This book aims to illustrate the background and applications of structural health monitoring (SHM) from both sensing and processing approaches. Its main objective is to summarize the advantages and disadvantages of SHM methodologies and their applications, which may provide a new perspective in understanding SHM for readers from diverse engineering fields.

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.

This book investigates the substructuring technology in structural health monitoring (SHM) to improve the accuracy and efficiency of the present SHM methods. SHM has been developed for monitoring, evaluation, and maintenance of civil structures. As the civil structures are usually large scale and a large number of sensors are deployed on a structure, accurate evaluation and maintenance of civil structures are always time-consuming. The book establishes a fundamental framework of substructuring method for the fast analysis of finite element (FE) model and monitoring data. Several practical civil structures are used for illustration. The book is intended for undergraduate and graduate students who are interested in SHM technology, researchers investigating the accurate, efficient, and effective methods in SHM field, and engineers working on evaluation and maintenance of civil structures or other structural dynamics applications.

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 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.

The book covers a variety of multidisciplinary topics, including structural health monitoring (SHM), risk analysis, seismic analysis, and various modeling and simulation methodologies. This book is an excellent resource for undergraduate and postgraduate students, academics, and researchers across a wide variety of engineering disciplines, as well as for practicing engineers and other professionals in the engineering industry

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.
It covers highly diverse topics, including signal processing, smart sensors, energy saving and harvesting, SHM system design and cyber physical systems, autonomous systems, remote sensing and remote support, UAV platforms for SHM, advanced manufacturing, Internet of Things, Industry 4.0, and SHM for civil structures and infrastructures. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists. The contents reflect the outcomes of the activities of EWSHM (European Workshop on Structural Health Monitoring) in 2020.

This book conducts effective research on data-driven Structural Health Monitoring (SHM), and accordingly presents many novel feature extraction methods by time series analysis and signal processing, to extract reliable damage sensitive features from vibration responses. In this regard, some limitations of time series modeling are dealt with.