Advanced Devices and Data Analysis in Vibration Control and Structural Health Monitoring

Dear Colleagues,

Introduction

Structural health monitoring (SHM) is a vital function of modern large-scale structures and infrastructure systems. This technology, along with data-driven simulation and analysis, can ensure the safety, reliability and durability of infrastructures. As sensor technology has matured, there has been a surging increase in the number of studies performed in the field of SHM to enrich more functionalities for health monitoring. Many efficient methods such as image recognition for cracks, pattern recognition for structural vibration, etc., can be computationally efficient. However, the problems of SHM are still challenging, and therefore still need to be addressed with more advanced devices and data analysis techniques. In line with the practical purpose of SHM, methods that can simultaneously investigate state-of-the-practice engineering models and data-driven analysis methods should be investigated and devised. This Special Issue aims to provide a platform for researchers, practitioners, and engineers to share their latest findings, innovations, and developments in advanced devices, new data analysis strategies, artificial intelligence, deep learning, uncertainty quantification and engineering optimization for SHM.

Scope

This Special Issue focuses on the latest advances in advanced devices, innovative data analysis strategies, optimization methods, uncertainty quantification, Bayesian methods and artificial intelligence techniques for SHM. It covers research works related to sensors, data acquisition systems, wireless sensor networks, data analysis techniques, signal processing algorithms and optimization strategies for SHM and applications. This issue also covers the application of advanced devices, innovative data analysis strategies, artificial intelligence and deep learning in practical scenarios.

Topics of Interest

The topics of interest for this Special Issue include, but are not limited to, the following:

1. Advanced sensors and data acquisition systems for SHM and engineering optimization;
2. Wireless sensor networks for SHM;
3. Innovative data analysis strategies for SHM and engineering optimization;
4. Signal processing and machine learning techniques for SHM and engineering optimization;
5. Non-destructive testing and evaluation for SHM;
6. Experimental and numerical studies on SHM and engineering optimization;
7. Applications of advanced devices, innovative data analysis strategies, artificial intelligence and deep learning in real-world scenarios;
8. Health monitoring of large-scale structures such as bridges, wind turbines and buildings;
9. Uncertainty quantification with advanced numerical methods and simulations;
10. Risk-informed decision-making for SHM of infrastructure systems.

Dr. Yinghao Zhao
Dr. Zeyu Wang
Dr. Xijun Ye
Guest Editors

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