Marker-free monitoring of the grandstand structures and modal identification using computer vision methods

Abstract

In this study, a vision-based multi-point structural dynamic monitoring framework is proposed. This framework aims to solve issues in current vision-based structural health monitoring. Limitations are due to manual markers, single-point monitoring, and synchronization between a multiple-camera setup and a sensor network. The proposed method addresses the first issue using virtual markers—features extracted from an image—instead of physical manual markers. The virtual markers can be selected according to each scenario, which makes them versatile. The framework also overcomes the issue of single-point monitoring by utilizing an advanced visual tracking algorithm based on optical flow, allowing multi-point displacement measurements. Besides, a synchronization mechanism between a multiple-camera setup and a sensor network is built. The proposed method is first tested on a grandstand simulator located in the laboratory. The experiment is to verify the performance of displacement measurement of the proposed method and conduct structural identification of the grandstand through multi-point displacement records. The results from the proposed method are then compared to the data gathered by traditional displacement sensors and accelerometers. A second experiment is conducted at a stadium during a football game to validate the feasibility of field application and the operational modal identification of the stadium under human crowd jumping through the measured displacement records. From these experiments, it is concluded that the proposed method can be employed to identify modal parameters for structural health monitoring.

An Improved Vision Method for Robust Monitoring of Multi-Point Dynamic Displacements with Smartphones in an Interference Environment

“…Therein, MSTC-1 and MSTC-2 denote the results of the first target point and the second target point identified by the MSTC method, respectively; STC-1 and STC-2 denote the ones by the STC method; LK denotes the ones by the characteristic OF method; sensor denotes the LVDT data. It is noted that the sampling rate for the LVDT is 100 Hz and it is down-sampled to be comparable to the vision measurements with the theoretical or actual frame rate [ 27 ].…”

A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation

“…The result of using image upsampling was a much smoother curve and more subpixel-level displacement records. However, it still cannot provide more details about small motions [2], especially at the very beginning and at the end. When there are no apparent loads on the structure, there is still very small structural motion induced by random environmental loads, such as wind, nearby machine operations, ambient ground vibration, etc.…”

“…Computer vision-based displacement measurement using cameras has attracted increasing attention in the community of structural health monitoring (SHM) because of its characteristics as a non-contact, long-distance, multi-point, high-precision, time-saving, and cost-effective sensing technique [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. Structural displacement is a critical indicator for evaluating performance and identifying and determining the effects of damage/change under external loads [18,19].…”

A Novel Dense Full-Field Displacement Monitoring Method Based on Image Sequences and Optical Flow Algorithm

“…Displacement is an important index of structural state and performance evaluations [7]. The static and dynamic characteristics of a structure, such as bearing capacity [12], deflection [13], deformation [14], load distribution [15], load input [16], influence line [17], influence surface [18], and modal parameters [19,20], can be calculated by structural displacement, to convert them further into physical indicators of response for structural safety assessment.…”