In many structures, most of the destruction and damage are caused by torsion. At the same time, in many cases, the torsion effect plays a controlling role in architectural design. Therefore, the monitoring of structural torsion has become more and more important, but there is less research at present. Based on machine vision technology, this paper proposes a monitoring method for the torsional deformation of building structure. Firstly, the theoretical basis of the scheme and the composition of the monitoring system are introduced. Then the feasibility of the scheme is proved through free rotation and elastic torsion experiments. It is verified that the scheme has the advantages of high precision and low cost, and can realize the real-time monitoring of structural torsional deformation, It can avoid the large deformation of the structure due to torsion, affect the use and even damage, and make a certain contribution to the safety evaluation and health evaluation of the structure.
Strain is an important parameter reflecting structural state, which is particularly important in the field of structural health monitoring (SHM). It can be used to evaluate the mechanical properties, failure behavior, crack development and residual stress of structural members and materials. It is particularly important in the field of structural health monitoring (SHM). In this paper, a new type of large gauge strain sensor is proposed. The proposed sensor is based on micro vision and uses a camera to capture small displacement. The packaging structure of strain sensor with sensing gauge length of about 50 cm is designed. In order to realize the monitoring in the field environment, the microscopic images in the experiment are obtained by webcam, which has broad application prospects. In the field of view of the camera, the maximum distance that the probe in the sensor can move is certain, but when the sensing gauge distance increases, the accuracy of strain measurement will be improved. The strain data obtained by the sensor is compared with the data obtained by FBG sensors to verify the measurement accuracy. It is found that the measurement accuracy is suitable for SHM of infrastructure.
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