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.