TY  - GEN
AB  - 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
AU  - Cury, Alexandre,
AU  - Ribeiro, Diogo,
AU  - Ubertini, Filippo,
AU  - Todd, Michael D.,
CN  - TA656.6
DO  - 10.1007/978-3-030-81716-9
DO  - doi
ID  - 1442535
KW  - Structural health monitoring.
KW  - Structural health monitoring
KW  - Surveillance de l'état des structures.
KW  - Surveillance de l'état des structures
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-81716-9
N2  - 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
SN  - 9783030817169
SN  - 3030817164
T1  - Structural health monitoring based on data science techniques /
TI  - Structural health monitoring based on data science techniques /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-81716-9
VL  - volume 21
ER  -