Name: Data Driven Methods for Civil Structural Health Monitoring and Resilience (Resilience and Sustainability in Civil, Mechanical, Aerospace and Manufacturing Engineering Systems) (en Inglés)
Price: 286821.00 COP
Availability: InStock
Author: Mohammad Noori (Editor) Carlo Rainieri (Editor) Marco Domaneschi (Editor) Vasilis Sarhosis (Editor)
ISBN: 9781032308371

portada Data Driven Methods for Civil Structural Health Monitoring and Resilience (Resilience and Sustainability in Civil, Mechanical, Aerospace and Manufacturing Engineering Systems) (en Inglés)

Formato

Libro Físico

Editorial

Crc Press

Autor

Mohammad Noori (Editor) Carlo Rainieri (Editor) Marco Domaneschi (Editor) Vasilis Sarhosis (Editor)

Idioma

Inglés

N° páginas

358

Encuadernación

Tapa Dura

ISBN13

9781032308371

N° edición

Data Driven Methods for Civil Structural Health Monitoring and Resilience (Resilience and Sustainability in Civil, Mechanical, Aerospace and Manufacturing Engineering Systems) (en Inglés)

Mohammad Noori (Editor) Carlo Rainieri (Editor) Marco Domaneschi (Editor) Vasilis Sarhosis (Editor) (Autor) · Crc Press · Tapa Dura

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$ 573.642

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Data Driven Methods for Civil Structural Health Monitoring and Resilience (Resilience and Sustainability in Civil, Mechanical, Aerospace and Manufacturing Engineering Systems) (en Inglés)"

Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.