Development of BIM-Based Bridge Maintenance System Considering Maintenance Data Schema and Information System

Due to the significant increase in the age of infrastructure globally, maintenance of existing structures has been prioritized over the construction of new structures, which are very costly. However, many infrastructure facilities have not been managed efficiently due to a lack of well-trained staff and budget limitations. Bridge management systems (BMSs) have been constructed and operated globally to maintain the originally designed structural performance and to overcome the inefficiency of maintenance practices for existing bridges. Unfortunately, because most of the current BMSs are based on 2D information systems, bridge maintenance data and information are not utilized effectively for bridge management. To overcome these problems, studies of BMSs based on building information modeling (BIM) have significantly increased in number. Most previous studies have proposed comprehensive frameworks containing approximate and limited information for maintenance to utilize BIM technology. Moreover, the utilization level of the maintenance information is less efficient because detailed information regarding safety diagnosis and maintenance are not included in data formats that are interpretable by computer algorithms. Therefore, in this study, a BIM-based BMS, including detailed information relating to safety diagnosis and maintenance, was constructed for the sustainability of bridge maintenance. To consider detailed information in the BMS, a maintenance data schema and its information system were established via the compilation of detailed information for safety diagnosis, repair and strengthening, remaining life, and valuation. In addition, a web data management program (WDMP) was developed using the maintenance data schema and information system, and was connected with the Midas CIM, which is a 3D modeling program. Finally, a prototype of the proposed BMS was established for an actual bridge in Korea. The proposed BMS in this study may be expected to improve the existing management practices for maintenance, and to reduce maintenance cost and information loss.

Current scenario of COVID-19

The COVID-19 is declared a pandemic by the WHO, which was originated from Wuhan China. Despite its zoonotic source, it is capable of transmission from human to human and causes of so many lives all around the globe approx 1M with the presence in 235 countries as per the WHO report on 15th Oct. In spite of lethal present in countries like the USA, India, and Brazil, the FDA could not approve any drug/vaccine except for emergency care, while many existing drugs like chloroquine, hydroxychloroquine, azithromycin, remdesivir, and favipiravir came up with the new hope. As of now the physical distancing and mask are mostly adopted by the various countries. Here in the important points released by WHO, has been considered in the present review. The aim of review is to give a complete picture of the research, treatment, and affect of COVID-19 to all over the world. Here the important and relevant information mostly the current is gathered from the various sources. Herein some of the serious concern about the safety, diagnosis, and treatment is highlighted with appropriate references, which will be beneficial for the reader.

Automated Safety Diagnosis Based on Unmanned Aerial Vehicle Video and Deep Learning Algorithm

This paper presents an automated traffic safety diagnostics solution named “Automated Roadway Conflict Identification System” (ARCIS) that uses deep learning techniques to process traffic videos collected by unmanned aerial vehicle (UAV). Mask region convolutional neural network (R-CNN) is employed to improve detection of vehicles in UAV videos. The detected vehicles are tracked by a channel and spatial reliability tracking algorithm, and vehicle trajectories are generated based on the tracking algorithm. Missing vehicles can be identified and tracked by identifying stationary vehicles and comparing intersect of union (IOU) between the detection results and the tracking results. Rotated bounding rectangles based on the pixel-to-pixel manner masks that are generated by mask R-CNN detection are introduced to obtain precise vehicle size and location data. Based on the vehicle trajectories, post-encroachment time (PET) is calculated for each conflict event at the pixel level. By comparing the PET values and the threshold, conflicts with the corresponding pixels in which the conflicts happened can be reported. Various conflict types: rear-end, head on, sideswipe, and angle, can also be determined. A case study at a typical signalized intersection is presented; the results indicate that the proposed framework could significantly improve the accuracy of the output data. Moreover, safety diagnostics for the studied intersection are conducted by calculating the PET values for each conflict event. It is expected that the proposed detection and tracking method with UAVs could help diagnose road safety problems efficiently and appropriate countermeasures could then be proposed.