Drone-Based Computer Vision-Enabled Vehicle Dynamic Mobility and Safety Performance Monitoring

This report documents the research activities to develop a drone-based computer vision-enabled vehicle dynamic safety performance monitoring in Rural, Isolated, Tribal, or Indigenous (RITI) communities. The acquisition of traffic system information, especially the vehicle speed and trajectory information, is of great significance to the study of the characteristics and management of the traffic system in RITI communities. The traditional method of relying on video analysis to obtain vehicle number and trajectory information has its application scenarios, but the common video source is often a camera fixed on a roadside device. In the videos obtained in this way, vehicles are likely to occlude each other, which seriously affects the accuracy of vehicle detection and the estimation of speed. Although there are methods to obtain high-view road video by means of aircraft and satellites, the corresponding cost will be high. Therefore, considering that drones can obtain high-definition video at a higher viewing angle, and the cost is relatively low, we decided to use drones to obtain road videos to complete vehicle detection. In order to overcome the shortcomings of traditional object detection methods when facing a large number of targets and complex scenes of RITI communities, our proposed method uses convolutional neural network (CNN) technology. We modified the YOLO v3 network structure and used a vehicle data set captured by drones for transfer learning, and finally trained a network that can detect and classify vehicles in videos captured by drones. A self-calibrated road boundary extraction method based on image sequences was used to extract road boundaries and filter vehicles to improve the detection accuracy of cars on the road. Using the results of neural network detection as input, we use video-based object tracking to complete the extraction of vehicle trajectory information for traffic safety improvements. Finally, the number of vehicles, speed and trajectory information of vehicles were calculated, and the average speed and density of the traffic flow were estimated on this basis. By analyzing the acquiesced data, we can estimate the traffic condition of the monitored area to predict possible crashes on the highways

Hawaii Deep Water Cable Program : bottom roughness survey of the Alenuihaha Channel

The Hawaii Deep Water Cable Program is responsible for determining the feasibility of laying multiple power cables between the islands of Hawaii and Oahu in the Hawaiian Islands. One major obstacle identified early in the program is the Alenuihaha Channel, 1920m deep between Maui and Hawaii, Figure 1 shows the cable route between the islands and the area selected for this survey. The Alenuihaha channel has been identified as a major obstacle based on its extreme depth, very steep slopes and relatively recent geology. Studies done on both the Alenuihaha Channel and other comparable areas in the Hawaiian Islands led to the conclusion that this channel would be the major bottom roughness obstacle to the cable laying operation. Faults, lava flows, old shorelines, reefs and large vertical escarpments are typical underwater features on the steep elopes of the islands of Maui and Hawaii

Second-Order Inelastic Analysis of Composite Framed Structures Based on the Refined Plastic Hinge Method

Composite steel-concrete structures experience non-linear effects which arise from both instability-related geometric non-linearity and from material non-linearity in all of their component members. This paper therefore presents a numerical procedure capable of addressing geometric and material non-linearities at the strength limit state based on the refined plastic hinge method. The refined plastic hinge approach models the elasto-gradual-plastic material non-linearity with strain-hardening under the interaction of bending and axial actions. This produces a benign method for a beam–column composite element under general loading cases. Another main feature of this paper is that, for members containing a point of contraflexure, its location is determined and a node is then located at this position to reproduce the real flexural behaviour and associated material non-linearity of the member. The formulation with the refined plastic hinge approach is efficacious and robust, and so a full frame analysis incorporating geometric and material non-linearity is tractable. Following development of the theory, its application is illustrated with a number of varied examples

Conceptual design appendices A-N (extracted from Hawaii Deep Water Cable Program bottom roughness survey of the Alenuihaha Channel May 1986.)

Conceptual design appendices A-N extracted from "Hawaii Deep Water Cable Program Bottom Roughness Survey of the Alenuihaha Channel May 1986.