Proactive 2D model-based scan planning for existing buildings (bibtex)
by Meida Chen, Eyuphan Koc, Zhuoya Shi, Lucio Soibelman
Abstract:
Creating a building information model (BIM) is known to be valuable during the life-cycle of a building. In most cases, a BIM of an existing building either does not exist or is out of date. For existing buildings, an as-is BIM is needed to leverage the technology towards building life-cycle objectives. To create an as-is BIM, field surveying is a necessary task in collecting current building related information. Terrestrial laser scanners have been widely accepted as field surveying instruments due to their high level of accuracy. However, laser scanning is a timeconsuming and labor-intensive process. Site revisiting and reworking of the scanning process is generally unavoidable because ofinappropriate datacollection processes. In thiscontext, creatinga scanplan beforegoing to a job-site can improve the data collection process. In this study, the authors have proposed a 2D proactive scanplanning frameworkthatincludesthreemodules: aninformation-gathering module,apreparation module,anda searching module. In addition, three search algorithms — a greedy best-first search algorithm, a greedy search algorithm with a backtracking process, and a simulated annealing algorithm — were compared based on 64 actual building site drawings to identify strength and limitations. The experimental results demonstrate that the greedy search algorithm with a backtracking process could be used to compute an initial scan plan and the simulated annealing algorithm couldbe used tofurther refinethe initial scanplan. This paperwill alsointroduce the results of a case study that deployed the proposed scan-planning framework. In the case study, the resulting 3D-point cloud that was generated based on the proposed framework was compared with the 3D point cloud created with data collected through a planned scanning process performed by a scan technician.
Reference:
Proactive 2D model-based scan planning for existing buildings (Meida Chen, Eyuphan Koc, Zhuoya Shi, Lucio Soibelman), In Automation in Construction, volume 93, 2018.
Bibtex Entry:
@article{chen_proactive_2018,
	title = {Proactive 2D model-based scan planning for existing buildings},
	volume = {93},
	issn = {09265805},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0926580517310385},
	doi = {10.1016/j.autcon.2018.05.010},
	abstract = {Creating a building information model (BIM) is known to be valuable during the life-cycle of a building. In most cases, a BIM of an existing building either does not exist or is out of date. For existing buildings, an as-is BIM is needed to leverage the technology towards building life-cycle objectives. To create an as-is BIM, field surveying is a necessary task in collecting current building related information. Terrestrial laser scanners have been widely accepted as field surveying instruments due to their high level of accuracy. However, laser scanning is a timeconsuming and labor-intensive process. Site revisiting and reworking of the scanning process is generally unavoidable because ofinappropriate datacollection processes. In thiscontext, creatinga scanplan beforegoing to a job-site can improve the data collection process. In this study, the authors have proposed a 2D proactive scanplanning frameworkthatincludesthreemodules: aninformation-gathering module,apreparation module,anda searching module. In addition, three search algorithms — a greedy best-first search algorithm, a greedy search algorithm with a backtracking process, and a simulated annealing algorithm — were compared based on 64 actual building site drawings to identify strength and limitations. The experimental results demonstrate that the greedy search algorithm with a backtracking process could be used to compute an initial scan plan and the simulated annealing algorithm couldbe used tofurther refinethe initial scanplan. This paperwill alsointroduce the results of a case study that deployed the proposed scan-planning framework. In the case study, the resulting 3D-point cloud that was generated based on the proposed framework was compared with the 3D point cloud created with data collected through a planned scanning process performed by a scan technician.},
	journal = {Automation in Construction},
	author = {Chen, Meida and Koc, Eyuphan and Shi, Zhuoya and Soibelman, Lucio},
	month = sep,
	year = {2018},
	keywords = {UARC, STG},
	pages = {165--177}
}
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