Machine Learning in Infrastructure Monitoring and Disaster Management

Dear Colleagues,

The rapid advancements in information and sensing technology have led to an exponential increase in data related to infrastructure monitoring and disaster management. This includes real-time monitoring time series, inspection images and videos, and hazard reconnaissance data. In today's era of big data, these valuable data sources serve as the new fuel driving society toward more resilient, reliable, and safer infrastructure facilities.

The vast amounts of data generated also necessitate more powerful analytical tools. Over the past few decades, advancements in machine learning (ML) techniques and hardware development have enabled significant progress in data cleansing, model training, and deployment for structural health monitoring and disaster management. ML techniques allow researchers to leverage diverse data types to assess structural conditions in real or near-real time. However, the application of ML faces several challenges in infrastructure monitoring and disaster management, such as big data collection in harsh environments, robust optimization in model training, the stability of model performance, and sensing techniques and hardware limitations.

This Special Issue delves into ML in infrastructure monitoring and disaster management, covering topics such as data collection and storage, ML applications, algorithm development, and sensing advancements. Progress in this field will lead us toward a safer and more resilient community.

Dr. Haibin Zhang
Dr. Xinzhe Yuan
Dr. Xingxing Zou
Dr. Tarutal Ghosh Mondal
Guest Editors

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