The delayed brittle fracture of high-strength bolts in long-span steel bridges threatens the safety of the bridges and even lead to serious accidents. Currently, human periodic inspection, the most commonly applied detection method for this kind of high-strength bolts damage, is a dangerous process and consumes plenty of manpower and time. To detect the damage fast and automatically, a visual inspection approach based on deep learning is proposed. YOLOv3, an object detection algorithm based on convolution neural network (CNN), is introduced due to its good performance for the detection of small objects. First, a dataset including 500 images labeled for damage is developed. Then, the YOLOv3 neural network model is trained by using the dataset, and the capability of the trained model is verified by using 2 new damage images. The feasibility of the proposed detection method has been demonstrated by the experimental results.
Fiber Bragg grating (FBG) sensors show superior potential for structural health monitoring of civil structures to ensure their structural integrity, durability, and reliability. In this work, FBG sensors, including strain and temperature sensors, are applied for health monitoring of the oil production offshore platform number CB271, which is located in the Bohai Sea, East China. The procedure of FBG sensor installation during platform construction, as well as model validation in a laboratory under a variety of loading conditions on a seismic simulation shaking table, is also presented. In the tests, FBG strain sensors are placed as a strain rosette on the surface of the platform central pillar, and an FBG temperature sensor is installed close to those strain sensors for temperature compensation. The FBG sensors have been in operation for one year without any significant reduction of working performance. Strain responses induced by the impacts of ocean waves and the ship's hundred tons of weight are monitored on site successfully. The fundamental frequency of the platform identified by the results of the FBG sensors agrees well with that obtained by theoretical analysis. In the monitoring, FBG sensors exhibit excellent performance and higher tolerance to harsh environments in the long-term real-time health monitoring of ocean offshore platforms.
Optical fiber sensors show superior potential for structural health monitoring of civil structures to ensure their structural integrity, durability and reliability. Apparent advantages of applying fiber optic sensors to a marine structure include fiber optic sensors’ immunity of electromagnetic interference and electrical hazard when used near metallic elements over a long distance. The strains and accelerations of the newly proposed model of a single post jacket offshore platform were monitored by fiber Bragg grating (FBG) sensors. These FBG sensors were attached to the legs and the top of the platform model in parallel with electric strain gauges or traditional piezoelectric accelerometers, respectively. Experiments were conducted under a variety of loading conditions, including underwater base earthquake simulation dynamic tests and static loading tests. Underwater seismic shaking table was utilized to provide the appropriate excitations. The natural frequencies measured by the FBG accelerometer agree well with those measured by piezo-electrical accelerometers. The monitoring network shows the availability of applying different fiber optic sensors in long-distance structural health monitoring with frequency multiplexing technology. Finally, the existing problems of packaging, strain transferring ratio between the bare fiber and the host structure on which the fiber embedded, and installation and protection of fiber optic sensors are emphasized.