Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods

Dear Colleagues,

Buildings are an important part of human society. Vital types of infrastructure, such as roads, buildings, high-speed railways, and bridges, are built all over the world. However, with the extension of running time and the increase in environmental load, these structures gradually lose stability, resulting in the degradation and slow failure of the structure. If this damage is not detected in time, it may threaten the normal operation of the structure, and even cause major harm. Therefore, as an important practical problem, structural health monitoring (SHM) has been paid increasing attention in various fields. Nondestructive technologies, especially remote sensing technologies (LiDAR, photogrammetry, infrared thermal imaging, etc.), provide technical support for the timely detection of safety hazards and ensure the safe operation of structures. In addition, these techniques form the basis of most 3D modeling methods that perform structural analysis functions based on numerical simulation or building information modeling (BIM) and heritage building information modeling (HBIM) processes.

In this context, the second edition of this Special Issue aims to include state-of-the-art research, discuss advanced remote sensing techniques and data processing methods that can be used for structural damage mapping and resilience assessment, present some of the most relevant research currently being conducted, highlight future challenges, and include several case studies.

Topics of interest will include, but are not limited to, the following:

• Damage identification and maintenance of modern and historic buildings;
• Structural deformation monitoring and analysis by time-series InSAR;
• Structural damage mapping by Lidar;
• Structural reconstruction by remote sensing;
• Remote sensing data processing;
• Multisource remote sensing fusion method and application;
• Structural damage identification based on deep learning;
• Structural resilience assessment based on damage mapping.

Prof. Dr. Joan Ramon Casas Rius
Prof. Dr. Necati Catbas
Dr. Rolando A. Chacón
Prof. Dr. Daniele Zonta
Guest Editors

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