Numerical studies on CFRP strengthened steel circular members under marine environment

The durability of carbon fibre reinforced polymer (CFRP) strengthened steel circular hollow section (CHS) members has now become a real challenge to researchers. In addition, various parameters that may affect the durability of such members have not been revealed yet. This paper presents brief experimental results and the first finite element (FE) approach of CFRP strengthened steel CHS beams conditioned in simulated sea water, along with an accelerated corrosion environment at ambient (24 OC ± 4 OC) and 50 OC temperatures. The beams were loaded to failure under four-point bending. It was found that the strength and stiffness reduced significantly after conditioning in an accelerated corrosion environment. Numerical simulation is implemented using the ABAQUS static general approach. A cohesive element was utilised to model the interface element and an 8-node quadrilateral in-plane general-purpose continuum shell was used to model CFRP elements. A mixed mode cohesive law was deployed for all the three components of stresses in the proposed FE approach, which were one normal component and two shear components. The validity of the FE models was ascertained by comparing the ultimate load and load vs deflection response from experimental results. A range of parametric studies were conducted to investigate the effects of bond length, adhesive types, thickness and diameter of tubes. The results of parametric studies indicated that the adhesive with high tensile modulus performed better and durability design factors varied from section to section.

A Distributed Robotic Vision Service

Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.