Submission Deadline: 30 June 2022 (closed) View: 189
As the demand on the reliability of engineering systems such as aircraft engines, steam turbines, nuclear reactors and high-speed trains increases, computer-aided structural integrity and safety of these systems have been becoming extremely significant. With the help of advanced monitoring/testing techniques and mathematical approaches/tools, currently increasing interests are being paid on new techniques to discover and understand the integrity and safety of engineering systems, from materials to components. Current design of engineering systems aims to operate in extreme loading environments, which need to consider the unexpected ageing related degradations/damaging and integrity.
As the advances in the computational methods, structural integrity and safety assessment of engineering systems and their improvement have been feasible through the accurate failure mechanism modeling with the combination of either deterministic or probabilistic analyses by using computer methods, including artificial intelligence, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, knowledge discovery and engineering, machine learning, neural network computing etc. Using model-based and data-driven-based approaches, studies on integrity and performance degradation assessment should be conducted to maximize lifetime and optimize inspection and maintenance policy of engineering systems. Specifically, failure occurs under influences of multi-sources of uncertainty, including load variations in usage, material properties, geometry variations within tolerances, and other uncontrolled variations. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on structural integrity and safety assessment of engineering systems are desired and expected, which attempts to prevent over-design and unnecessary inspection and provide tools to enable a balance between safety and economy to be achieved.
The aim would be to establish a common understanding about the state of the field and draw a road map on where the research is heading, highlight the issues and discuss the possible solutions, and provide the data, models and tools necessary to performing statistically safety and integrity assessment by computer methods. It is also available to concerned review/regular articles that will support and stimulate the continuing efforts to understand the research and development of model-based and data-driven-based approaches for structural integrity, safety and field applications. Potential topics include, but are not limited to:
• Artificial intelligence
• Fuzzy logic and genetic algorithms
• Machine learning
• Neural network computing
• Structural integrity
• Structural reliability
• Structural health monitoring
• Computational mechanics
• Structural design methodology
• Prognostics and health management
• Probabilistic Physics of Failure
• Reliability-based design
• Durability and damage tolerance
• Uncertainty quantification and propagation
• Performance degradation modeling and analysis
• Non-destructive testing and evaluation for structural integrity
• Risk analysis and safety of materials and structural mechanics
• Analytical and numerical simulation of materials and structures
• Experimental methods applied to structural integrity
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