Integration of Digital Technologies into Underground Utility Asset Management

Installation, detection, maintenance, mapping, and management of underground utility assets present challenges to owners, engineers and contractors. Industry-wide practices include the use of geophysical and similar technologies to determine depth and location, and 2D as-built plans integrated with GIS databases for information management. The feasibility of incorporating 3D BIM models of the subsurface to replace the 2D plans to improve visualization and data management is examined in this paper. Obtaining an accurate image of the underground infrastructure would help minimize excavation accidents due to equipment-utility collisions and prevent property damage. Further, the inclusion of automated data collection and sharing features realized through BIM technology can enhance operations of smart cities. The research methodology consists of a state-of-the-art review of the current underground utility management systems, combined with statistical analysis of survey responses received from utility providers and one-call centers in the U.S. Three categories of utility practices are identified based on the level of digital technology integration. It is found that a vast majority of utility firms have adopted GIS databases with 2D plans, depth and other asset information, while a smaller percentage of providers have achieved full GIS-BIM integration, incorporating a wide range of asset data. Future progress on broader implementation appears to be constrained by the digital literacy of personnel and high costs of technology acquisition and application. A three-step framework for converting 2D plans to 3D BIM models is also presented and discussed. The process model proposed for this purpose allows the utilization of commercially available software with minimal need for additional coding.

Location-Based Data Dissemination for Spatial Queries in Wireless Broadcast Environments

Most current research on Location-Based Services （LBSs, for short） assumes point-to-point wireless commu- nication, where the server processes a query and returns the query result to the user via a point-to-point wireless channel. However, LBSs via point-to-point wireless channel suffer from a tremendous amount of tramc and service requests from the user and thereby result in poor performance. In this paper, we present broadcast-based spatial query processing algorithms designed to support k-NN （k-Nearest Neighbor） and range queries via a wireless network. The task of the query processor is to selectively monitor the wireless broadcast channel, when the data items are disseminated by the server, according to their locations. Experiments are conducted to evaluate the performance of the proposed algorithms. Comprehensive experiments illustrate that the presented algorithms are highly scalable and are more efficient than the previous techniques in terms of both access time and energy consumption.