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.

Structural health monitoring is an extremely important methodology in evaluating the 'health' of a structure by assessing the level of deterioration and remaining service life of civil infrastructure systems. This book reviews key developments in research, technologies and applications in this area of civil engineering. It discusses ways of obtaining and analysing data, sensor technologies and methods of sensing changes in structural performance characteristics. It also discusses data transmission and the application of both individual technologies and entire systems to bridges and buildings.With its distinguished editors and international team of contributors, Structural health monitoring of civil infrastructure systems is a valuable reference for students in civil and structural engineering programs as well as those studying sensors, data analysis and transmission at universities. It will also be an important source for practicing civil engineers and designers, engineers and researchers developing sensors, network systems and methods of data transmission and analysis, policy makers, inspectors and those responsible for the safety and service life of civil infrastructure. - Reviews key developments in research, technologies and applications - Discusses systems used to obtain and analyse data and sensor technologies - Assesses methods of sensing changes in structural performance

This book is a printed edition of the Special Issue "Structural Health Monitoring (SHM) of Civil Structures" that was published in Applied Sciences

Civil infrastructure systems are generally the most expensive assets in any country, and these systems are deteriorating at an alarming rate. In addition, these systems have a long service life in comparison to most other commercial products. As well, the introduction of intelligent materials and innovative design approaches in these systems is painfully slow due to heavy relianceon traditional construction and maintenance practices, and the conservative nature of design codes. Feedback on the "state of the health" of constructed systems is practically nonexistent. In the quest for lighter, stronger and corrosion-resistant structures, the replacement of ferrous materials by high-strength fibrous ones is being actively pursued in several countries around the world, both with respect to the design of new structures as well as for the rehabilitation and strengthening of existing ones. In North America, active research in the design of new highway bridges is focused on a number of specialty areas, including the replacement of steel reinforcing bars in concrete deck slabs by randomly distributed low-modulus fibers, and the replacement of steel prestressing cables for concrete components by tendons comprising super-strong fibers. Research is also being conducted on using FRPs to repair and strengthen existing structures.

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.

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.

This book covers some of the most recent developments and application potentials in structural health assessment for non-experts in the subject. Among topics addressed are sensor types, platforms and data conditioning for practical applications, wireless collection of sensor data, sensor power needs and on-site energy harvesting, long-term monitoring of structures, uncertainty in collected data, and future directions in structural health assessment.

"Smart Materials in Structural Health Monitoring, Control and Biomechanics" presents the latest developments in structural health monitoring, vibration control and biomechanics using smart materials. The book mainly focuses on piezoelectric, fibre optic and ionic polymer metal composite materials. It introduces concepts from the very basics and leads to advanced modelling (analytical/ numerical), practical aspects (including software/ hardware issues) and case studies spanning civil, mechanical and aerospace structures, including bridges, rocks and underground structures. This book is intended for practicing engineers, researchers from academic and R&D institutions and postgraduate students in the fields of smart materials and structures, structural health monitoring, vibration control and biomedical engineering. Professor Chee-Kiong Soh and Associate Professor Yaowen Yang both work at the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. Dr. Suresh Bhalla is an Associate Professor at the Department of Civil Engineering, Indian Institute of Technology Delhi, India.

This book includes a collection of state-of-the-art contributions addressing both theoretical developments in, and successful applications of, seismic structural health monitoring (S2HM). Over the past few decades, Seismic SHM has expanded considerably, due to the growing demand among various stakeholders (owners, managers and engineering professionals) and researchers. The discipline has matured in the process, as can be seen by the number of S2HM systems currently installed worldwide. Furthermore, the responses recorded by S2HM systems hold great potential, both with regard to the management of emergency situations and to ordinary maintenance needs. The book’s 17 chapters, prepared by leading international experts, are divided into four major sections. The first comprises six chapters describing the specific requirements of S2HM systems for different types of civil structures and infrastructures (buildings, bridges, cultural heritage, dams, structures with base isolation devices) and for monitoring different phenomena (e.g. soil-structure interaction and excessive drift). The second section describes available methods and computational tools for data processing, while the third is dedicated to hardware and software tools for S2HM. In the book’s closing section, five chapters report on state-of-the-art applications of S2HM around the world.

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.