Submission Deadline: 01 July 2025 View: 560 Submit to Special Issue
Prof. Abilio M. P. de Jesus, University of Porto, Portugal
Prof. Shun-Peng Zhu, University of Electronic Science and Technology of China (UESTC), China
Prof. Debiao Meng, University of Electronic Science and Technology of China (UESTC), China
As technology continues to advance, the deployment of complex engineering systems in extreme environments has become increasingly common. Throughout their service life, these systems face numerous uncertainties that significantly impact their reliability and security. Addressing these challenges has become a research hotspot in engineering design, and the integration of Artificial Intelligence (AI) in complex system modeling and reliability assessment has proven to be a potent tool. This special issue aims to explore and promote the latest developments in the intelligent methods for structural integrity assessment and design optimization, focusing on the utilization of machine learning techniques.
The robustness of AI, particularly machine learning, has been demonstrated in various applications such as condition monitoring, safety assessment, reliability modeling, and analysis of mechanical structures. Existing research has played crucial roles in industries like manufacturing, transportation, installation, monitoring, and maintenance. Despite significant progress, there is a pressing need for more research in this domain, given the increasing complexity of mechanical systems. The challenge lies in developing more efficient and accurate modeling methods, as well as reliability and security analysis methods. Different AI techniques have varying strengths, making it essential to explore their applicability to different scenarios.
The proposed special issue seeks to create a platform for academic exchange, fostering a common understanding of the current state of intelligent methods in complex system modeling and reliability assessment. Furthermore, the special issue aims to advance the understanding and breakthroughs in the development of intelligent methods for structural integrity assessment and design optimization under uncertainty. By leveraging the potential of artificial intelligence, the focus will be on discussing and researching the data, models, and methods necessary for analyzing complex engineering structures. Potential topics include, but are not limited to:
· Structural integrity
· Structural reliability
· Failure mechanisms
· Prognostics and health management
· Probabilistic Physics of Failure
· Reliability-based design
· Durability and damage tolerance
· Uncertainty quantification and propagation
· Performance degradation modeling and analysis
· Deep learning models
· Regression models
· Artificial intelligence
· Fatigue life prediction
· Hydrogen embrittlement
· Remaining useful life prediction and fault diagnosis
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