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Structural health monitoring based on data science techniques

Record Type:	Electronic resources : Monograph/item
Title/Author:	Structural health monitoring based on data science techniquesedited by Alexandre Cury ... [et al.].
other author:	Cury, Alexandre.
Published:	Cham :Springer International Publishing :2022.
Description:	xv, 484 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Structural health monitoring.
Online resource:	https://doi.org/10.1007/978-3-030-81716-9
ISBN:	9783030817169$q(electronic bk.)

Structural health monitoring based on data science techniques
Structural health monitoring based on data science techniques[electronic resource] /edited by Alexandre Cury ... [et al.]. - Cham :Springer International Publishing :2022. - xv, 484 p. :ill. (some col.), digital ;24 cm. - Structural integrity,v. 212522-5618 ;. - Structural integrity ;v.1..

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.

ISBN: 9783030817169$q(electronic bk.)

Standard No.: 10.1007/978-3-030-81716-9doiSubjects--Topical Terms:

522561
Structural health monitoring.

LC Class. No.: TA656.6 / .S77 2022

Dewey Class. No.: 624.17

Structural health monitoring based on data science techniques
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520 $a 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.
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