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    The American Society of Mechanical Engineers

    Special Issue on AI-Powered Engineering in Assessment and Design of Pressure Vessels and Piping Systems

    Journal of Pressure Vessel Technology
    Journal of Pressure Vessel icon

    Artificial Intelligence (AI) and Machine Learning (ML) are increasingly recognized as key enablers of innovation in structural and mechanical engineering. In particular, their integration into the assessment, monitoring, and design of industrial systems and critical infrastructure is opening new possibilities for predictive maintenance, reliability analysis, and real-time decision-making.

    This Special Issue aims to showcase state-of-the-art research on AI applications in industrial field, with a special focus on structural integrity, seismic performance, and safety assessment. The issue welcomes both methodological advances and application-oriented studies that leverage AI/ML techniques to address challenges in design, inspection, degradation modeling, and hazard evaluation — including seismic and other extreme loading scenarios.

    The Special Issue is open to original research articles, review papers, and case studies from industry and academia. By highlighting the synergy between artificial intelligence and structural/mechanical engineering, this issue seeks to foster the development of smart, resilient, and efficient technologies for the safety and sustainability of industrial systems.

    Topic Areas

    THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:

    • Machine learning for prediction of mechanical properties, flaw interaction, and material degradation
    • Surrogate models and operator learning for nonlinear and history-dependent material behavior
    • Deep learning for damage detection, corrosion classification, and defect characterization
    • AI-supported non-destructive evaluation, including eddy current and remote-field testing
    • AI-enhanced seismic risk assessment and post-earthquake performance evaluation
    • Smart structural health monitoring using sensor fusion and unsupervised learning
    • Computer vision and robotics for inspection of pipelines, bolted joints, and welded components
    • Data fusion and digital twins for reliability-based design and prognostic maintenance