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Long-term structural health monitoring by remote sensing and advanced machine learninga practical strategy via structural displacements from synthetic aperture radar images /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Long-term structural health monitoring by remote sensing and advanced machine learningby Alireza Entezami, Bahareh Behkamal, Carlo De Michele.
Reminder of title:	a practical strategy via structural displacements from synthetic aperture radar images /
Author:	Entezami, Alireza.
other author:	Behkamal, Bahareh.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xvii, 110 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Structural health monitoringData processing.
Online resource:	https://doi.org/10.1007/978-3-031-53995-4
ISBN:	9783031539954$q(electronic bk.)

Long-term structural health monitoring by remote sensing and advanced machine learninga practical strategy via structural displacements from synthetic aperture radar images /
Entezami, Alireza.

Long-term structural health monitoring by remote sensing and advanced machine learninga practical strategy via structural displacements from synthetic aperture radar images /[electronic resource] :by Alireza Entezami, Bahareh Behkamal, Carlo De Michele. - Cham :Springer Nature Switzerland :2024. - xvii, 110 p. :ill., digital ;24 cm. - PoliMI SpringerBriefs,2282-2585. - PoliMI SpringerBriefs..

Pioneering Remote Sensing in Structural Health Monitoring -- Advanced ML Methods: Bridging SAR Images and Structural Health Monitoring -- Simulating Reality: Numerical Assessments of a Bridge Health Monitoring -- From Theory to Reality: Advanced SHM Methods to the Tadcaster Bridge -- Conclusions and Prospects for Structural Health Monitoring.

This book offers an in-depth investigation into the complexities of long-term structural health monitoring (SHM) in civil structures, specifically focusing on the challenges posed by small data and environmental and operational changes (EOCs) Traditional contact-based sensor networks in SHM produce large amounts of data, complicating big data management. In contrast, synthetic aperture radar (SAR)-aided SHM often faces challenges with small datasets and limited displacement data. Additionally, EOCs can mimic the structural damage, resulting in false errors that can critically affect economic and safety issues. Addressing these challenges, this book introduces seven advanced unsupervised learning methods for SHM, combining AI, data sampling, and statistical analysis. These include techniques for managing datasets and addressing EOCs. Methods range from nearest neighbor searching and Hamiltonian Monte Carlo sampling to innovative offline and online learning frameworks, focusing on data augmentation and normalization. Key approaches involve deep autoencoders for data processing and novel algorithms for damage detection. Validated using simulated data from the I-40 Bridge, USA, and real-world data from the Tadcaster Bridge, UK, these methods show promise in addressing SAR-aided SHM challenges, offering practical tools for real-world applications. The book, thereby, presents a comprehensive suite of innovative strategies to advance the field of SHM.

ISBN: 9783031539954$q(electronic bk.)

Standard No.: 10.1007/978-3-031-53995-4doiSubjects--Topical Terms:

855818
Structural health monitoring
--Data processing.

LC Class. No.: TA656.6 / .E584 2024

Dewey Class. No.: 624.171

Long-term structural health monitoring by remote sensing and advanced machine learninga practical strategy via structural displacements from synthetic aperture radar images /
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