Automation and Computational Intelligence for Road Maintenance and Management
A comprehensive computational intelligence toolbox for solving problems in infrastructure management
In Automation and Computational Intelligence for Road Maintenance and Management, a team of accomplished researchers delivers an incisive reference that covers the latest developments in computer technology infrastructure management. The book contains an overview of foundational and emerging technologies and methods in both automation and computational intelligence, as well as detailed presentations of specific methodologies.
The distinguished authors emphasize the most recent advances in the maintenance and management of infrastructure robotics, automated inspection, remote sensing, and the applications of new and emerging computing technologies, including artificial intelligence, evolutionary computing, fuzzy logic, genetic algorithms, knowledge discovery and engineering, and more.
Automation and Computational Intelligence for Road Maintenance and Management explores a universal synthesis of the cutting edge in parameters and indices to evaluate models. It also includes:
* Thorough introductions to management science and the latest methods of automation and the structure and framework of automation and computing intelligence
* Comprehensive explorations of advanced image processing techniques, recent advances in fuzzy, and diagnosis automation
* Practical discussions of segmentation and fragmentation and different types of features and feature extraction methods
* In-depth examinations of methods of classification along with various developed methodologies and models of quantification, evaluation, and indexing in automation
Perfect for postgraduate students in road and transportation engineering, evaluation, and assessment, Automation and Computational Intelligence for Road Maintenance and Management will also earn a place in the libraries of researchers interested in or working with the evaluation and assessment of infrastructure.
Sobre el autor
Hamzeh Zakeri, Ph D, is Adjunct Research Professor for the Department of Civil and Environment Engineering, Amirkabir University of Technology. His research interests include Automation, and Fuzzy type 2, Image Processing, Remote sensing, Machine Learning, Knowledge extraction, Hybrid Meta-heuristic Application in the field of pavement engineering.
Fereidoon Moghadas Nejad, Ph D, is Professor and Head of Transportation Group at Amirkabir University of Technology. His research interests include Materials, and Testing, Image Processing, Automation, Fuzzy and Numerical Methods in Pavement and Railway Engineering.
Amir H. Gandomi, Ph D, is Professor of Data Science and an ARC DECRA Fellow for the Faculty of Engineering and Information Technology at the University of Technology, Sydney. His research interests include Global Optimisation and (Big) Data Analytics using Machine Learning and Evolutionary Computations in particular.
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