TY - JOUR
T1 - Potential of big visual data and building information modeling for construction performance analytics
T2 - An exploratory study
AU - Han, Kevin K.
AU - Golparvar-Fard, Mani
N1 - The authors would like to thank Turner Construction for providing access to their job sites, in addition to all the individuals who participated in the survey for this report who provided numerous constructive comments to improve it. This work is funded in part by the National Science Foundation (NSF)’s grant CMMI-1360562 and CPS-1446765 and the National Center for Supercomputing Applications (NCSA)’s Institute for Advanced Computing Applications and Technologies Fellows program. Any opinions, findings, conclusions or recommendations presented in this paper are those of the authors and do not reflect the views of NSF, NCSA, Turner Construction or the individual acknowledged above.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The ever increasing volume of visual data due to recent advances in smart devices and camera-equipped platforms provides an unprecedented opportunity to visually capture actual status of construction sites at a fraction of cost compared to other alternatives methods. Most efforts on documenting as-built status, however, stay at collecting visual data and updating BIM. Hundreds of images and videos are captured but most of them soon become useless without properly being localized with plan document and time. To take full advantage of visual data for construction performance analytics, three aspects (reliability, relevance, and speed) of capturing, analyzing, and reporting visual data are critical. This paper 1) investigates current strategies for leveraging emerging big visual data and BIM in construction performance monitoring from these three aspects, 2) characterizes gaps in knowledge via case studies and structures a road map for research in visual sensing and analytics.
AB - The ever increasing volume of visual data due to recent advances in smart devices and camera-equipped platforms provides an unprecedented opportunity to visually capture actual status of construction sites at a fraction of cost compared to other alternatives methods. Most efforts on documenting as-built status, however, stay at collecting visual data and updating BIM. Hundreds of images and videos are captured but most of them soon become useless without properly being localized with plan document and time. To take full advantage of visual data for construction performance analytics, three aspects (reliability, relevance, and speed) of capturing, analyzing, and reporting visual data are critical. This paper 1) investigates current strategies for leveraging emerging big visual data and BIM in construction performance monitoring from these three aspects, 2) characterizes gaps in knowledge via case studies and structures a road map for research in visual sensing and analytics.
KW - Big visual data
KW - Construction progress monitoring
KW - Images
KW - Point cloud
KW - Quality control
KW - Videos
UR - https://www.scopus.com/pages/publications/84998879912
UR - https://www.scopus.com/pages/publications/84998879912#tab=citedBy
U2 - 10.1016/j.autcon.2016.11.004
DO - 10.1016/j.autcon.2016.11.004
M3 - Article
AN - SCOPUS:84998879912
SN - 0926-5805
VL - 73
SP - 184
EP - 198
JO - Automation in Construction
JF - Automation in Construction
ER -