Online Damage Detection in Structural Systems

Applications of Proper Orthogonal Decomposition, and Kalman and Particle Filters

Nonfiction, Science & Nature, Technology, Engineering, Mechanical, Electronics
Cover of the book Online Damage Detection in Structural Systems by Saeed Eftekhar Azam, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Saeed Eftekhar Azam ISBN: 9783319025599
Publisher: Springer International Publishing Publication: January 21, 2014
Imprint: Springer Language: English
Author: Saeed Eftekhar Azam
ISBN: 9783319025599
Publisher: Springer International Publishing
Publication: January 21, 2014
Imprint: Springer
Language: English

This monograph assesses in depth the application of recursive Bayesian filters in structural health monitoring. Although the methods and algorithms used here are well established in the field of automatic control, their application in the realm of civil engineering has to date been limited. The monograph is therefore intended as a reference for structural and civil engineers who wish to conduct research in this field. To this end, the main notions underlying the families of Kalman and particle filters are scrutinized through explanations within the text and numerous numerical examples. The main limitations to their application in monitoring of high-rise buildings are discussed and a remedy based on a synergy of reduced order modeling (based on proper orthogonal decomposition) and Bayesian estimation is proposed. The performance and effectiveness of the proposed algorithm is demonstrated via pseudo-experimental evaluations.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This monograph assesses in depth the application of recursive Bayesian filters in structural health monitoring. Although the methods and algorithms used here are well established in the field of automatic control, their application in the realm of civil engineering has to date been limited. The monograph is therefore intended as a reference for structural and civil engineers who wish to conduct research in this field. To this end, the main notions underlying the families of Kalman and particle filters are scrutinized through explanations within the text and numerous numerical examples. The main limitations to their application in monitoring of high-rise buildings are discussed and a remedy based on a synergy of reduced order modeling (based on proper orthogonal decomposition) and Bayesian estimation is proposed. The performance and effectiveness of the proposed algorithm is demonstrated via pseudo-experimental evaluations.

More books from Springer International Publishing

Cover of the book Management and Therapy of Late Pregnancy Complications by Saeed Eftekhar Azam
Cover of the book Partition and the Practice of Memory by Saeed Eftekhar Azam
Cover of the book Computational Collective Intelligence by Saeed Eftekhar Azam
Cover of the book Consensus on Peirce’s Concept of Habit by Saeed Eftekhar Azam
Cover of the book Policy-Based Autonomic Data Governance by Saeed Eftekhar Azam
Cover of the book Foundations of Symmetric Spaces of Measurable Functions by Saeed Eftekhar Azam
Cover of the book Basic Earthquake Engineering by Saeed Eftekhar Azam
Cover of the book Non-destructive Testing and Repair of Pipelines by Saeed Eftekhar Azam
Cover of the book Rehabilitative Surgery by Saeed Eftekhar Azam
Cover of the book Parallel Computing Technologies by Saeed Eftekhar Azam
Cover of the book Chaos: Concepts, Control and Constructive Use by Saeed Eftekhar Azam
Cover of the book National Football League Strategies by Saeed Eftekhar Azam
Cover of the book Landslide Science for a Safer Geoenvironment by Saeed Eftekhar Azam
Cover of the book Actuarial Sciences and Quantitative Finance by Saeed Eftekhar Azam
Cover of the book Risk Assessment by Saeed Eftekhar Azam
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy