Submission Deadline: 31 July 2026 View: 198 Submit to Special Issue
Dr. Danial Jahed Armaghani
Email: danial.jahedarmaghani@uts.edu.au
Affiliation: School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, 2007, Australia
Research Interests: rock mechanics, concrete technology, tunnelling, artificial intelligence and optimization algorithms
Prof. Dr. Hadi Khabbaz
Email: hadi.khabbaz@uts.edu.au
Affiliation: School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, 2007, Australia
Research Interests: ground improvement techniques, development of smart tools using MATLAB
Dr. Yilin Gui
Email: yilin.gui@qut.edu.au
Affiliation: School of Civil and Environmental Engineering, Queensland University of Technology, Queensland, 4000, Australia
Research Interests: Geomechanics, computational and constitutive modelling of geomaterials, Geoenvironmental Engineering and other related areas in Geotechnical Engineering and Mining Engineering
In the last two decades, the topic of computational intelligence (CI) has undergone several definitions. Adaptation and self-organization algorithms and implementations that permit or facilitate appropriate behaviours (intelligent behaviour) in complex and dynamic settings are included in the notion of CI. One or more properties of reason, such as generalisation, discovery, association, and abstraction, are said to be present in this computer paradigm, which demonstrates a capacity to adapt to new conditions and learn from them. Many of the issues we face today in the area of engineering are becoming more complicated because of the prevalence of amorphous structures and behaviours, as well as large-scale, low dependability, and a scarcity of shared or comprehensive information. This intricacy necessitated that the scope of CI is widened to highlight adaptability.
In order to operate a system similar to human thinking, CI relies on three primary components: artificial neural networks, fuzzy logic, and evolutionary computation, both of which employ machine learning theories to cope with uncertainty. Hybrid CI models have shown a greater performance and application level in numerous fields of engineering than conventional CI models, which had serious limitations such time-consuming human participation and a lack of resilience. Metaheuristic algorithms may be utilised to improve base model hyper-parameters (CI models), adding extra value to frequently used base intelligence approaches.
This Special Issue focuses on the creation of unique hybrid intelligence strategies for handling regression, classification, and time series challenges. We invite scholars to submit original research papers that will help to promote ongoing research on the use of emerging CI and hybrid CI systems to assess and solve complex engineering challenges. In addition, state-of-the-art research reports, reviews, and critical evaluations of CI and hybrid CI systems are most welcome.
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