Intelligent Damage Assessment for Engineering Materials and Structures

Dear Colleagues,

Many complex and high-performance pieces of equipment, such as launch vehicles, in-orbit satellites, alien detectors and nuclear power systems, need to operate in extreme environments such as vacuum and irradiation. With the increasing complexity of the service environment, the requirements for equipment performance, health monitoring and maintenance systems are constantly improving. Strict service conditions put forward intelligence requirements for the assessment of material and structure damage status. It is necessary to build an intelligent damage assessment system to meet the unmanned and intelligent requirements of health monitoring. Research on intelligent damage detection and digital twinning theory and methodology for materials and structures, including intelligent damage perception, physical information fusion damage models, real-time damage assessment and load optimization methods, is of great significance to the damage analysis of complex equipment in special operating environments such as launch vehicles, on-orbit satellites, nuclear power systems, etc.

This Special Issue aims to facilitate the communication about intelligent structural damage monitoring and assessment theories and methods. New damage detection techniques and schemes are of interest, while methods that make full use of the available information from different sensors are worth exploring. Introducing mechanics and semi-empirical theory into existing data-driven models to address issues such as insufficient data and data imbalance is a scientific and effective approach. In addition, the real-time assessment of damage state and determining how to make quick decisions based on these assessment results are also particularly important. Recently, with the development of artificial intelligence technology, deep learning methods have been successfully applied in speech recognition, computer vision and other fields. Due to their advantages of automatically extracting features and their highly integrated convenience in mobile devices, deep leaning methods have also been applied in the areas of fracture and fatigue. Deep learning methods have become a powerful tool for intelligent damage assessment technology. Thus, it is encouraging to provide new research ideas in deep-learning-based damage detection and the intelligent health monitoring of damage to materials and structures of complex equipment. This Special Issue is dedicated to opening a scientific discussion on intelligent methods/techniques applied to damage assessment for engineering materials and structures. This collection will shed light on recent developments in this new research area with the aim of providing the updated state of the art and discussing advantages, drawbacks, and challenges in detail.

Dr. Xiangyun Long
Prof. Dr. Shun-Peng Zhu
Dr. José A.F.O. Correia
Guest Editors

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• deep-learning-based damage detection
• multi-source information fusion
• machine learning for damage assessment
• physical information fusion model
• deep-learning-based fatigue life prediction
• physics-informed neural networks for probabilistic fatigue life
• real-time load optimization
• fatigue digital twins
• structural health monitoring
• structural integrity
• fatigue under uncertainty