Название: Artificial Intelligence in Mechatronics and Civil Engineering: Bridging the Gap
Автор: Ehsan Momeni, Danial Jahed Armaghani, Aydin Azizi
Издательство: Springer
Серия: Emerging Trends in Mechatronics
Год: 2023
Страниц: 254
Язык: английский
Формат: pdf (true), epub
Размер: 36.4 MB

This book aimed to cover the application of Artificial Intelligence, Machine Learning, and simulation techniques in Engineering. The book highlights the successful implementation of different soft computing techniques in various areas of engineering more especially in Civil, Electronic, Mechatronic, and Mining Engineering. The power of Artificial Intelligence and Machine Learning techniques in solving some examples of real-life problems in engineering will be highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book.

This book comprises nine chapters which, in overall, shed lights on the importance of simulation techniques in solving complex engineering problems.

Chapter “A Review on the Feasibility of Artificial Intelligence in Mechatronics” deals with a review on the application of artificial intelligence in mechatronic. Artificial Intelligence has become a valuable tool in various fields with the increasing progress of science and information production. With the processing of Big Data and increased productivity, new challenges have appeared in the design and application of control systems. In some systems, the interaction between humans and robots is essential, while real-time decision-making plays a vital role in other types of systems. This chapter presents a review of artificial intelligence methods in mechatronics. For this purpose, the leading intelligent control methods in technical systems are reviewed and discussed in the initial part of this chapter, including reinforcement learning, fuzzy logic method, artificial neural networks, optimization techniques, and adaptive control methods.

Chapter “A Review on the Application of Soft Computing Techniques in Foundation Engineering” reviews the application of artificial intelligence methods in foundation engineering. This chapter shed light on many studies which underline the feasibility of simulation-based techniques in assessing the bearing capacity and settlement of various types of foundation including shallow, deep, and skirted foundations.

Chapter “Machine Learning in Mechatronics and Robotics and Its Application in Face-Related Projects” deals with various aspects of face-related projects. Implementing non-verbal information is one of the ways to create communication between people. Using this type of information can improve the interaction process in human–robot interaction. One of the aspects of people’s non-verbal information is facial images, which can play an essential role in the development of mechatronic and robotic systems. This chapter discusses some uses of facial images, such as facial recognition, and facial expression recognition. Using these images and applications, authors explored and developed mechatronic and robotic systems which were based on special access for different persons and changes in facial expressions. As mentioned earlier, in chapter nine, different aspects of face-related projects are explored, apart from that, some ideas that can be applied to create new approaches are discussed. Since these projects perform based on a procedure, the flow of the facial projects from face detection to facial expression recognition and other applications are discussed. This chapter helps the readers get acquainted with using facial images in human–robot interaction.

Contents:
Optical Resistance Switch for Optical Sensing
Empirical, Statistical, and Machine Learning Techniques for Predicting Surface Settlement Induced by Tunnelling
A Review on the Feasibility of Artificial Intelligence in Mechatronics
Feasibility of Artificial Intelligence Techniques in Rock Characterization
A Review on the Application of Soft Computing Techniques in Foundation Engineering
Application of a Data Augmentation Technique on Blast-Induced Fly-Rock Distance Prediction
Forecast of Modern Concrete Properties Using Machine Learning Methods
Reliability-Based Design Optimization of Detention Rockfill Dams and Investigation of the Effect of Uncertainty on Their Performance Using Meta-Heuristic Algorithm
Machine Learning in Mechatronics and Robotics and Its Application in Face-Related Projects ​