Identification of micro-deformations in historic buildings through time-series data measurement and calibration

Historical building micro-deformation recognition methods measure the propagation time of laser light between a recognition point and a rangefinder, and calculate the distance between the two points using the speed of light. However, laser light can be affected by factors such as uneven air refractive index and air turbulence, resulting in deviations in the measured distance. Therefore, a time-series-based millimeter-wave radar recognition method for historical building micro-deformation is proposed. Micro-deformation data collected by millimeter-wave radar are calibrated to form a time-series dataset. The calibrated data reflect the building's micro-deformation state and are then input into a time-series analysis model to uncover deformation patterns. Using the calibrated data and an accurate model, future microdeformations are predicted, safety thresholds are set, and potential hazards are detected and addressed in advance. Experimental results show that the measured micro-deformation displacements at target measuring points A and B are very close to the actual displacements, with minimal data deviation. For recognition points 1 to 80, the absolute deviations between the measured and true deformations at A and B are within ±0.05 mm, demonstrating good curve synchronization. Error analysis shows that the error of this method is stable within a range of -0.03mm to +0.03mm. This method has high recognition accuracy and can provide more reliable data for historical building protection.