Recent Advances in Sensing and Data Centric Methods for Structural Health Monitoring and Resilience: 2nd Edition

Dear Colleagues,

Over the last decade, due to major advancements in sensing technology, diagnostics, data analytics (including various AI-based methodologies), optimization, and system identification, significant developments have taken place in structural health monitoring, intelligent systems, and enhancing the resilience of engineering systems. This Special Issue aims to underscore the importance of the latest developments in those areas and their collective impact on further progress in the fields of structural health monitoring and the resilience of infrastructure, mechanical and aerospace systems, as well as other engineering systems. Potential research topics include, but are not limited to, the following:

• New and novel innovations in structural health monitoring;
• IoT and smart infrastructure;
• The SHM of historic and ageing structures;
• AI-based methodologies, such as deep learning neural networks, big data, and digital twins;
• System identification;
• Surrogate models;
• Optimization techniques;
• Probabilistic methods, such as uncertainty quantification and variability assessment, especially combined with AI methods;
• Various machine learning tools;
• The dynamic response prediction of highly nonlinear systems;
• Feature extraction schemes;
• The resilience of civil infrastructure in a life cycle;
• The resiliency and recoverability of structures and isolation–structure systems;
• The impact of SHM on urban infrastructure resilience;
• The utilization of data analytics schemes in structural control and seismic isolation systems;
• Data-driven methods used for structural damage identification;
• The reliability and safety of engineering structures;
• Distributed sensors and big data in SHM application;
• Damage identification (detection, localization, quantification, and remaining life) to improve structural resilience;
• Image processing methods used for SHM;
• Output-only methods used for SHM.

Prof. Dr. Mohammad N Noori
Dr. Marco Domaneschi
Dr. Naiwei Lu
Dr. Tianyu Wang
Guest Editors

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