Out-of-plane modal property extraction based on multi-level image pyramid reconstruction using stereophotogrammetry

Abstract

Understanding the dynamic behavior of as-built structures is important, because structural condition can often be inferred from changes in its dynamic response. To this end, modal properties such as natural frequencies, damping ratios, and mode shapes can be extracted from acceleration measurements. Associated installations require sensors, cabling, and data acquisition systems that can be expensive and time consuming. Alternatively, computer vision-based approaches have been proposed that offer non-contact measurements, as well as substantial cost savings. However, such approaches usually focus only on the structural component responses that are in the plane of the image; researchers seldom consider the structural response that is out-of-plane (i.e., perpendicular to the image plane). For complex structural component that possess asymmetries, the out-of-plane behavior is critical to understanding their dynamic response. Determination of dynamic response of the structure requires that multiple cameras are used and can be computationally demanding. In this study, imagery from a commercially available stereo camera is combined with a multi-level image pyramid approach and operational modal analysis (OMA) to extract out-of-plane modal properties. The measurements are first padded to reduce the distortion effect caused by the image pyramid. The responses are then compressed and decomposed into sub-bands using an image pyramid decomposition; followed by extraction of the modal properties using the OMA approach known as frequency-domain stochastic subspace identification. The advantages and limitations of the proposed approach are illustrated numerically using a physics-based graphics model (PBGM) of a continuous beam. Subsequently, experimental validation is conducted for a 3-story model building using the Intel® RealSenseTM D415 depth camera. Modal properties are shown to be determined quickly, with high accuracy and noise robustness. Moreover, detailed near-continuous mode shapes are obtained using multi-level image pyramid reconstruction. These results demonstrate that the proposed approach can give an accurate picture of the dynamic characteristics of a structure, offering the potential for effective long-term structural health monitoring for important structural component.