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.

Many books on new smart materials are available, but specialized analysis of particular topics is still in high demand. This multiauthor book focuses on applying nanotechnology to cement-based materials to make numerous engineering applications possible. The addition of novel smart nanofillers allows the development of multifunctional composite materials, not just limited to improving mechanical strength, but also including several enhanced features. Special attention is devoted to types of nano-inclusions, novel techniques to mix components, and analysis of properties that can be achieved by paste, mortar, or concrete if added with nanofillers. Among these properties, the capability of self-sensing is very promising. Moreover, the use of phase-changing materials improves the energy efficiency of nanocomposites, resulting in important applications in engineering. Particular attention is also focused on energy harvesting and electromagnetic shielding properties. Comprehensive and up to date, this is an important reference book that not only provides in-depth information about recent developments and perspectives in this field but also discusses topics that promise major developments in the near future.

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.