Optimization of Tuned Mass Dampers: Using Active and Passive Control

This book provides an up-to-date overview of the latest developments in the optimization of tuned mass dampers, including classical approaches, new trends, and artificial intelligence and machine learning methods. It covers various types of control types and highlights the importance of TMDs in the design of high-rise structures and bridges.

Format: Hardback
Length: 187 pages
Publication date: 08 April 2022
Publisher: Springer Nature Switzerland AG

This book is a timely and comprehensive resource that summarizes the latest developments in the optimization of tuned mass dampers (TMDs), encompassing all classical approaches and emerging trends, including metaheuristic algorithms. Moreover, artificial intelligence and machine learning methods are integrated to predict optimal results by bypassing lengthy optimization processes. Another notable distinction of this book is its coverage of various types of control, including passive tuned mass dampers, active tuned mass dampers, tuned liquid dampers, tuned liquid column dampers, and inerter dampers.

TMDs are vibration absorber devices employed in diverse mechanical systems. The design of TMDs is crucial for achieving desired performance. In practice, numerous high-rise structures and bridges have incorporated TMDs to mitigate sway disturbances. After negative experiences with structures' swaying, TMDs were retrofitted post-construction. Optimal design for TMDs involves closed-form expressions for frequency and damping ratio, but precise optimization necessitates iterative approaches. The current trend in the field is to employ evolutionary algorithms and metaheuristic optimization methods to attain the desired outcomes.

The book is organized into five chapters, each dedicated to a specific aspect of TMD optimization. Chapter 1 provides an introduction to TMDs, their principles, and applications. Chapter 2 explores the theoretical background and mathematical models of TMDs. Chapter 3 discusses the design and analysis of TMDs, including closed-form expressions and iterative optimization methods. Chapter 4 explores the use of artificial intelligence and machine learning methods in TMD optimization. Chapter 5 presents case studies and practical applications of TMD optimization, highlighting the benefits and challenges of these methods in real-world scenarios.

In conclusion, this book serves as a valuable resource for engineers, researchers, and practitioners interested in the optimization of tuned mass dampers. It provides a comprehensive and up-to-date overview of classical and emerging approaches, including metaheuristic algorithms, artificial intelligence, and machine learning methods. The inclusion of chapters on various types of control and case studies makes it particularly useful for practitioners seeking to implement TMDs in real-world applications.

Weight: 465g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030983420
Edition number: 1st ed. 2022