This study aims to develop an intelligent underground utility management system that integrates Mixed Reality (MR) technology to accurately and efficiently manage the location and attribute information of major underground facilities, including...

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https://www.riss.kr/link?id=T17393360
대구 : 경북대학교 대학원, 2025
학위논문(박사) -- 경북대학교 대학원 , 건설환경에너지공학부 , 2026. 2
2025
한국어
대구
x,137p ; 26 cm
지도교수: 서건원
I804:22001-000000111311
0
상세조회0
다운로드다국어 초록 (Multilingual Abstract)
This study aims to develop an intelligent underground utility management system that integrates Mixed Reality (MR) technology to accurately and efficiently manage the location and attribute information of major underground facilities, including...
This study aims to develop an intelligent underground utility management system that integrates Mixed Reality (MR) technology to accurately and efficiently manage the location and attribute information of major underground facilities, including pipelines, power lines, and communication networks. Recent advancements in fifth-generation (5G) mobile communication, cloud and edge computing, and deep learning-based object recognition technologies have significantly enhanced the realism, responsiveness, and mobility of MR devices. As a result, MR has emerged as a next-generation on-site visualization platform with strong industrial applicability, poised to replace traditional smartphone-centered field management systems.
Conventional underground utility management relies on two-dimensional drawings or Geographic Information System (GIS)-based databases, which inherently limit on-site accessibility, data portability, accurate location identification, real-time data updates, and collaborative functions. Furthermore, aging underground infrastructure, increased urban complexity, and heightened safety concerns have intensified the need for a precise and intuitive three-dimensional management framework. To address these issues, this study designs and implements an MR-based intelligent underground utility management system, referred to as SCUP (Smart City Underground Pipeline). The developed system comprises the following core modules.
First, the MR-based 3D visualization module employs OpenCV and the Unity3D engine to register and display 3D objects—such as pipelines, cables, and valves—onto real-world imagery in real time. A stratification recognition and depth estimation algorithm is applied to accurately represent the actual burial depth of underground utilities beneath the ground surface. Second, the underground utility recognition and data automation module automatically identifies infrastructure components, including markers and valves, and collects and refines attribute information such as GPS-based location, depth, and path in real time. Third, the WebRTC-based remote collaboration and data transmission module enables bidirectional video and data streaming between control centers and field personnel, supporting real-time collaboration, task instruction, and issue response.
Fourth, a MongoDB–Hadoop-based data management architecture integrates unstructured data (e.g., XML, JSON, CSV) with structured pipeline attributes to support high-speed processing and distributed storage of large-scale 3D spatial datasets. Fifth, the field interface for HMD and mobile devices provides Bluetooth-based data communication, enabling field workers to access and manipulate underground utility information directly within the MR environment.
A field demonstration was conducted using seven major categories of underground utilities—including water, sewer, power, telecommunications, and gas—within a designated smart city testbed. The results show that the proposed system improves location accuracy, operational efficiency, and data update speed compared to traditional 2D-based management methods. In addition, the WebRTC-based collaboration function maintained a data synchronization latency of less than 200 ms between field units and the control center, validating the system’s capability for real-time cooperative operations.
The MR-based intelligent underground utility management technology presented in this study not only enhances the safety, efficiency, and sustainability of urban underground infrastructure management but also demonstrates strong potential as a core enabling technology for digital twin–based smart city development. Future extensions of this research will focus on integrating MR with IoT, cloud computing, and AI analytics to establish real-time citywide infrastructure monitoring and predictive maintenance systems.
목차 (Table of Contents)