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This book is an up-to-date source for computation applications of
optimization, prediction via artificial intelligence methods, and
evaluation of metaheuristic algorithm with different structural
applications. As the current interest of researcher, metaheuristic
algorithms are a high interest topic area since advance and
non-optimized problems via mathematical methods are challenged by
the development of advance and modified algorithms. The artificial
intelligence (AI) area is also important in predicting optimum
results by skipping long iterative optimization processes. The
machine learning used in generation of AI models also needs optimum
results of metaheuristic-based approaches. This book is a great
source to researcher, graduate students, and bachelor students who
gain project about structural optimization. Differently from the
academic use, the chapter that emphasizes different scopes and
methods can take the interest and help engineer working in design
and production of structural engineering projects.
This book is an up-to-date source for computation applications of
optimization, prediction via artificial intelligence methods, and
evaluation of metaheuristic algorithm with different structural
applications. As the current interest of researcher, metaheuristic
algorithms are a high interest topic area since advance and
non-optimized problems via mathematical methods are challenged by
the development of advance and modified algorithms. The artificial
intelligence (AI) area is also important in predicting optimum
results by skipping long iterative optimization processes. The
machine learning used in generation of AI models also needs optimum
results of metaheuristic-based approaches. This book is a great
source to researcher, graduate students, and bachelor students who
gain project about structural optimization. Differently from the
academic use, the chapter that emphasizes different scopes and
methods can take the interest and help engineer working in design
and production of structural engineering projects.
This book covers different aspects of real-world applications of
optimization algorithms. It provides insights from the Sixth
International Conference on Harmony Search, Soft Computing and
Applications held at Istanbul University, Turkey, in July 2020.
Harmony Search (HS) is one of the most popular metaheuristic
algorithms, developed in 2001 by Prof. Joong Hoon Kim and Prof.
Zong Woo Geem, that mimics the improvisation process of jazz
musicians to seek the best harmony. The book consists of research
articles on novel and newly proposed optimization algorithms; the
theoretical study of nature-inspired optimization algorithms;
numerically established results of nature-inspired optimization
algorithms; and real-world applications of optimization algorithms
and synthetic benchmarking of optimization algorithms.
This timely book deals with a current topic, i.e. the applications
of metaheuristic algorithms, with a primary focus on optimization
problems in civil engineering. The first chapter offers a concise
overview of different kinds of metaheuristic algorithms, explaining
their advantages in solving complex engineering problems that
cannot be effectively tackled by traditional methods, and citing
the most important works for further reading. The remaining
chapters report on advanced studies on the applications of certain
metaheuristic algorithms to specific engineering problems. Genetic
algorithm, bat algorithm, cuckoo search, harmony search and
simulated annealing are just some of the methods presented and
discussed step by step in real-application contexts, in which they
are often used in combination with each other. Thanks to its
synthetic yet meticulous and practice-oriented approach, the book
is a perfect guide for graduate students, researchers and
professionals willing to applying metaheuristic algorithms in civil
engineering and other related engineering fields, such as
mechanical, transport and geotechnical engineering. It is also a
valuable aid for both lectures and advanced engineering students.
This timely book deals with a current topic, i.e. the applications
of metaheuristic algorithms, with a primary focus on optimization
problems in civil engineering. The first chapter offers a concise
overview of different kinds of metaheuristic algorithms, explaining
their advantages in solving complex engineering problems that
cannot be effectively tackled by traditional methods, and citing
the most important works for further reading. The remaining
chapters report on advanced studies on the applications of certain
metaheuristic algorithms to specific engineering problems. Genetic
algorithm, bat algorithm, cuckoo search, harmony search and
simulated annealing are just some of the methods presented and
discussed step by step in real-application contexts, in which they
are often used in combination with each other. Thanks to its
synthetic yet meticulous and practice-oriented approach, the book
is a perfect guide for graduate students, researchers and
professionals willing to applying metaheuristic algorithms in civil
engineering and other related engineering fields, such as
mechanical, transport and geotechnical engineering. It is also a
valuable aid for both lectures and advanced engineering students.
This book is a timely book to summarize the latest developments in
the optimization of tuned mass dampers covering all classical
approaches and new trends including metaheuristic algorithms. Also,
artificial intelligence and machine learning methods are included
to predict optimum results by skipping long optimization processes.
Another difference and advantage of the book are to provide
chapters about several types of control types including passive
tuned mass dampers, active tuned mass dampers, tuned liquid
dampers, tuned liquid column dampers and inerter dampers. Tuned
mass dampers (TMDs) are vibration absorber devices used in all
types of mechanic systems. The key factor in the design is an
effective tuning of TMDs for the desired performance. In practice,
several high-rise structures and bridges were designed by including
TMDs. Also, TMDs were installed after the construction of the
structures after several negative experiences resulting from the
disturbing sway of the structures. In optimum design, several
closed-form expressions have been proposed for optimum frequency
and damping ratio of TMDs, but the exact optimization requires
iterative optimization approaches. The current trend is to use
evolutionary algorithms and metaheuristic optimization methods to
reach the goal.
Reinforced concrete structures are one of the major structural
types and must adhere to design regulation codes. It is ideal to
find the best design (section dimension, material type, and amount
of reinforcement) with the minimum cost providing the design
constraints (design formulation considering loading of structure).
Metaheuristic methods inspired by natural phenomena can consider
design constraints by combining the analyses of formulation of
reinforced concrete structures with an iterative numerical
algorithm using several convergence options of random generation of
candidate design solutions. Metaheuristic Approaches for Optimum
Design of Reinforced Concrete Structures: Emerging Research and
Opportunities is a pivotal reference source that focuses on several
metaheuristic algorithms and the design of several types of
structural members. Additionally, retrofit applications and seismic
design issues are considered for readers in earthquake zones.
Highlighting a wide range of topics including algorithms, design
variables, and retrofit design, this book is ideally designed for
architects, engineers, urban designers, government officials,
policymakers, researchers, academicians, and students.
Reinforced concrete structures are one of the major structural
types and must adhere to design regulation codes. It is ideal to
find the best design (section dimension, material type, and amount
of reinforcement) with the minimum cost providing the design
constraints (design formulation considering loading of structure).
Metaheuristic methods inspired by natural phenomena can consider
design constraints by combining the analyses of formulation of
reinforced concrete structures with an iterative numerical
algorithm using several convergence options of random generation of
candidate design solutions. Metaheuristic Approaches for Optimum
Design of Reinforced Concrete Structures: Emerging Research and
Opportunities is a pivotal reference source that focuses on several
metaheuristic algorithms and the design of several types of
structural members. Additionally, retrofit applications and seismic
design issues are considered for readers in earthquake zones.
Highlighting a wide range of topics including algorithms, design
variables, and retrofit design, this book is ideally designed for
architects, engineers, urban designers, government officials,
policymakers, researchers, academicians, and students.
In today's developing world, industries are constantly required to
improve and advance. New approaches are being implemented to
determine optimum values and solutions for models such as
artificial intelligence and machine learning. Research is a
necessity for determining how these recent methods are being
applied within the engineering field and what effective solutions
they are providing. Artificial Intelligence and Machine Learning
Applications in Civil, Mechanical, and Industrial Engineering is a
collection of innovative research on the methods and implementation
of machine learning and AI in multiple facets of engineering. While
highlighting topics including control devices, geotechnology, and
artificial neural networks, this book is ideally designed for
engineers, academicians, researchers, practitioners, and students
seeking current research on solving engineering problems using
smart technology.
In today's developing world, industries are constantly required to
improve and advance. New approaches are being implemented to
determine optimum values and solutions for models such as
artificial intelligence and machine learning. Research is a
necessity for determining how these recent methods are being
applied within the engineering field and what effective solutions
they are providing. Artificial Intelligence and Machine Learning
Applications in Civil, Mechanical, and Industrial Engineering is a
collection of innovative research on the methods and implementation
of machine learning and AI in multiple facets of engineering. While
highlighting topics including control devices, geotechnology, and
artificial neural networks, this book is ideally designed for
engineers, academicians, researchers, practitioners, and students
seeking current research on solving engineering problems using
smart technology.
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