Pioneering Remote Sensing in Structural Health Monitoring

Recently, long-term structural health monitoring (SHM) of civil structures by using the technology of remote sensing has received increasing attention by civil engineers. This is because such technology can facilitate SHM by introducing useful products such as synthetic aperture radar (SAR) images acquired from some satellites suitable for monitoring of large-scale civil structures in wide areas. In contrast to conventional contact-based and next-generation vision-based sensors, a long-term monitoring program via space borne remote sensing cannot provide high-dimensional structural responses. On this basis, it is feasible to conduct the program with a few SAR images. Using such products, one can extract structural responses in terms of displacements at different areas of a civil structure and monitor the responses for detecting any abnormal change. Because the long-term monitoring process is based on analyzing structural displacement responses, the main focus is on machine learning. For this process, environmental and operational changes seriously affect the performance of data-driven machine learning-aided techniques. Due to the importance of SHM in every society, this chapter intends to explain the main parts of remote sensing-based health monitoring of civil structures through SAR images and machine learning.