Vision-based real-time marine and offshore structural health monitoring system using underwater robots

Structural health monitoring (SHM) plays an increasingly vital role in guaranteeing the operation success of marine and offshore (MO) structures while reducing their risks of structural failure. In this study, a real-time MO-SHM system based on the highly controllable underwater robots is developed, which is functionalized with three modules, that is, underwater monitoring robots, vision-based image processing and analyzing, and time-dependent damage assessing and early warning. The robotic module is actuated by the hybrid driving method that combines the combustion-based actuators (ejection) and propeller thrusters (propulsion) for transient actuation ability and well reliability in complex underwater environments. The image processing and analyzing module is conducted based on the You Only Look Once (YOLO)-Underwater model that is expanded by the transfer learning and attention mechanisms and the underwater preconditioning and warning correction added for the specific underwater concrete identification. The damage assessing and early warning module is achieved by the time-dependent hybrid analytic hierarchy process method that is integrated with the ordered weighted averaging and entropy weight method for more objective assessment and feedback. The reported MO-SHM system provides design guidance for the next-generation multifunctional underwater devices that combine analyzing and early warning for underwater concrete structures.