Identification (biology)

Channel (broadcasting)

Polymer chemistry

Pedagogy

Structural health monitoring

Mathematics

Psychotherapist

Structural engineering

Biology

Accelerometer

Displacement (psychology)

Operating system

Image (mathematics)

Engineering

Artificial intelligence

Optical flow

Psychology

Tracking (education)

Chemistry

Geometry

Stadium

Synchronization (alternating current)

Computer science

Botany

Modal

Real-time computing

Point (geometry)

Computer vision

Simulation

Telecommunications

Structural Health Monitoring

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.

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