Structural Health Monitoring 2019

Description

Includes recent advances in networked sensor technology and big data Include application areas of traditional aerospace, civil and marine/wind power infrastructure to oil/gas infrastructure, high-speed railway transportation, and autonomous vehicles. Vision-based and machine-learning approaches to expand and improve SHM Special topics on the use of MEMS/NEMS and CNT sensors for SHM systems, human-machine interfaces for structural inspection, dynamic data driven application systems (DDDAS), and more This two-volume book set contains over 425 papers. While offering investigations into how sensors, networks, and signaling systems are used in dozens of civil and military applications, a special feature of this book is its exploration of how to enable intelligent life-cycle health management for the industrial internet of things. It demonstrates how machine-learning and stochastic methods add value to SHM data by taking into account changing environments and conditional events. It offers new insights on interactions between SHM data and big data for improving the safety and integrity of monitored structures. Information is also presented on how SHM sensing interfaces with smart and functional materials operating in dynamic systems. A large number of SHM applications are explained, including additive manufacturing, advanced composites, actuators, corrosion, machinery, power plants, piping, robotics, underground infrastructure, and many more. Chapters in the book are edited presentations from a September 2019 Workshop at Stanford University co-sponsored by the U.S. Air Force Office of Scientific Research and the Office of Naval Research.