Ground-based radar monitoring building thermal expansion and contraction data applied to machine learning multi-feature-deformation regression prediction task

Under the background of continuous increase of surface temperature caused by global warming, high temperature events occur frequently, posing a potential threat to natural ecosystems and economic and social development. China 's coastal areas have dense infrastructure and are high-risk areas for this weather. Due to its high flexibility and low fundamental frequency characteristics, super high-rise buildings are highly sensitive to temperature action, and structural damage or even failure is prone to occur under high temperature loads. However, the traditional thermal expansion and contraction deformation measurement technology relies on sensors. This method has problems such as high cost, limited coverage, and easy damage to structures, and it is difficult to meet the needs of large-span dynamic monitoring. This experiment is based on the portable ground-based radar interferometer ( GPRI ) system developed by Swiss GAMMA company. This technology has the advantages of non-contact measurement, all-weather observation, high spatial and temporal resolution, sub-millimeter high precision and wide monitoring range. It provides an innovative solution for the monitoring of thermal expansion and contraction deformation of super high-rise buildings in high temperature weather. At the same time, the meteorological instrument is used to monitor the corresponding meteorological data in real time. Finally, the deformation data and monitoring data are used as the data set of multi-feature deformation prediction of machine learning model. The final conclusion is that the correlation coefficient between the predicted value and the true value is very high, which is greater than 0.96, and the mean square error is very low, which is less than 0.007, which verifies the reliability of the GPRI system.