스마트폰 GPS 센서 기반의 토공 공정 모니터링 및 시뮬레이션 활용 사례연구Case Study of Smart Phone GPS Sensor-based Earthwork Monitoring and Simulation
- Other Titles
- Case Study of Smart Phone GPS Sensor-based Earthwork Monitoring and Simulation
- Authors
- 조현석; 윤충배; 박지현; 한상욱
- Issue Date
- Dec-2022
- Publisher
- 한국BIM학회
- Keywords
- Earthwork Monitoring; Optimization; Dump Truck; GPS Sensor; Simulation; 토공 모니터링; 공정 최적화; 덤프 트럭; GPS 센서; 시뮬레이션
- Citation
- KIBIM Magazine, v.12, no.4, pp 61 - 69
- Pages
- 9
- Indexed
- KCI
- Journal Title
- KIBIM Magazine
- Volume
- 12
- Number
- 4
- Start Page
- 61
- End Page
- 69
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185578
- DOI
- 10.13161/kibim.2022.12.4.061
- ISSN
- 2288-1697
- Abstract
- Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.
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