Strain is one of the conventional parameters that evaluate structural performance in the field of bridge health monitoring. Among the various techniques that have been utilized to measure strain, fiber Bragg grating (FBG) sensing system has been broadly applied for long-term strain monitoring in civil engineering, whereas microimage strain sensing (MISS), with its corresponding sensors designed and fabricated, is a new technology for strain measurement. Furthermore, the MISS system is developed to monitor the strain of small and medium span bridges. To verify the performance of the MISS system in strain measurement, a comprehensive and comparative study of the MISS system and FBG strain sensor system is provided. First, the technical features of the two technologies were described. Moreover, more details were expounded in terms of service life, deployment, coverage, cost, and temperature dependence. Then, assisted data comparisons were carried out through component testing. Results show that the proposed MISS system is effective and robust. Therefore, the proposed MISS system will serve as a promising strain measuring alternative in the field of bridge structural health monitoring.
The number and scale of tunnels around the world are continuously increasing, but various disease problems during the operation period have also followed, and they have become one of the important problems facing tunnels at present. Many detection methods have been proposed in the field of tunnel detection, such as traditional manual detection method, ultrasonic detection method, ground-penetrating radar method, laser scanning method and inspection method based on image processing technology. However, due to the high cost of equipment, single test content, strict test environment and other reasons, most of the current tunnel routine inspection is still manual inspection. In order to solve the existing problems in tunnel detection, a method for rapid detection and treatment analysis of cracks in tunnel linings based on deep learning is proposed. Firstly, lining cracks were selected as the main research objects, and their causes and treatment measures in different parts were analyzed. Secondly, the AlexNet convolutional neural network based on the Caffe framework was used to identify the cracks. The crack images were collected to establish a data set, and the network parameters were modified and trained. Then use MATLAB to extract the crack length and width, and design a human-machine interactive tunnel lining crack detection program in MATLAB GUI. Finally, the content and results of this paper are discussed.
In order to ensure the safety of construction, all kinds of construction machinery are widely applied to the construction site. Tower crane, as a material handling equipment, has the characteristics of wide operating range and large potential energy, and has become the core machinery in the construction site. The tower crane driver’s field of vision is often blocked, which seriously affects the safety of hoisting. To increase the view of tower crane drivers, most of the current monitoring systems will install a camera on the boom above the hook. But this camera can only view the situation around the hook, and it cannot be quantified. Based on this, this paper proposes a hoisting security detection technology based on deep learning. Firstly, the camera in the monitoring system is used to collect data sets. Secondly, the hook and workers are marked in the image. Then, Faster R-CNN is used to train and evaluate the data sets. The results show that the method has high recognition accuracy. However, the worker and the hook are not on a horizontal plane, so a verification test of the relationship between the height and the ratio of pixel length to true length was completed. The results show that the method can convert the ratio of the hook to the ratio of the worker, and then the real distance between the worker and the hook can be calculated.
A certain level of horizontal displacement will occur during excavation or subsequent construction of deep foundation pit. If the support is improper and the horizontal displacement of the foundation pit is too large, it will cause collapse and even affect the buildings around the foundation pit, which will endanger people's life and property. Therefore, the horizontal displacement monitoring of deep foundation pit becomes more and more important. At present, the electronic total station is often used to monitor the horizontal displacement of the foundation pit, but this monitoring method is expensive, prone to accidental errors, and can not be used for real-time monitoring. Therefore, a method of monitoring the horizontal displacement of deep foundation pit by using laser projection sensing technique is proposed in this paper. The horizontal displacement of the foundation pit is replaced by the displacement of the laser spot emitted by the laser, and the horizontal displacement of the foundation pit can be obtained by identifying the displacement of the laser spot projected on the screen. A series of experiments show that the accuracy of this monitoring method meets the engineering requirements and greatly reduces the cost, which provides a new technology for the displacement monitoring of deep foundation pit.
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