Data-Enhanced Engineering Structural Integrity Assessment and Design

Dear Colleagues,

Engineering structures, such as construction, offshore platforms, machinery, and equipment, face significant challenges in maintaining structural integrity under complex and uncertain conditions. Traditional structural integrity assessment methods often rely on models that lack the capability to adapt to real-world complexities, including material degradation, environmental variability, and dynamic loading. This has also resulted in traditional modeling and design approaches struggling to address the intricacies posed by the above challenges, particularly when confronted with nonlinearity, uncertainty, and large-scale computations. This gap underscores the need for advanced computational methods, which are emerging as pivotal tools to solve these challenges with higher precision and speed.

Advances in data collection technologies and computational modeling now offer unprecedented opportunities to enhance the precision, reliability, and efficiency of structural integrity assessments. Data-enhanced methodologies leverage real-time monitoring, historical records, and advanced machine-learning techniques to understand structural behavior comprehensively. By integrating physics-based models with data-driven approaches, these methods enable engineers to address uncertainties, predict potential failures, and optimize maintenance strategies. Furthermore, advanced computational design methods are important because of their ability to enhance the performance, reliability, and safety of complex engineering structures. These methods, which include high-performance computing, machine learning, and artificial intelligence, enable engineers to simulate multifaceted phenomena more accurately and optimize multi-objective functions in real time. In addition, the integration of computational intelligence with conventional engineering approaches facilitates more adaptive and robust solutions, even in the presence of uncertainty. Such advanced methods have revolutionized fields like structural integrity analysis, structural optimization, and failure prediction, driving innovation and improving decision-making capabilities across diverse engineering domains.

This Special Issue seeks to develop advanced structural integrity assessment and design strategies for complex engineering structures by integrating advanced computational techniques and real-world data. The specific objectives include, but are not limited to:

• Physics-informed machine learning;
• Multifidelity modeling and optimization;
• Uncertainty quantification and propagation;
• Generative design and data augmentation;
• Digital twin technology;
• AI-augmented multidisciplinary design optimization (MDO);
• Probabilistic design optimization;
• Topology optimization with machine learning;
• Real-time optimization using edge computing;
• Energy-efficient computational methods;
• Resilient infrastructure design;
• Surrogate-assisted optimization;
• Multi-objective optimization with explainable AI;
• Robust and adaptive algorithms for dynamic systems;
• Hybrid optimization frameworks for renewable energy systems.

Dr. Debiao Meng
Dr. Wei Li
Guest Editors

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• advanced computational methods
• uncertainty quantification
• physics-informed machine learning
• reliability-based design optimization (RBDO)
• data augmentation
• structural integrity modeling