Journal
ADVANCED ENGINEERING INFORMATICS
Volume 29, Issue 2, Pages 211-224Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.aei.2015.01.011
Keywords
Construction; Performance monitoring; Computer vision; Machine learning
Funding
- National Natural Science Foundation of China [51208425]
- Research Foundation of Northwestern Polytechnical University [JCY20130127]
- Infrastructure and Transportation Technology Promotion Research Program - Ministry of Land, Infrastructure and Transport of Korean government [14CTAP-C078828-01]
- United State National Center for Supercomputing Applications
- Institute of Applied Science and Technology (ICTAS)
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1030472] Funding Source: National Science Foundation
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Timely and accurate monitoring of onsite construction operations can bring an immediate awareness on project specific issues. It provides practitioners with the information they need to easily and quickly make project control decisions. Despite their importance, the current practices are still time-consuming, costly, and prone to errors. To facilitate the process of collecting and analyzing performance data, researchers have focused on devising methods that can semi-automatically or automatically assess ongoing operations both at project level and operation level. A major line of work has particularly focused on developing computer vision techniques that can leverage still images, time-lapse photos and video streams for documenting the work in progress. To this end, this paper extensively reviews these state-of-the-art vision-based construction performance monitoring methods. Based on the level of information perceived and the types of output, these methods are mainly divided into two categories (namely project level: visual monitoring of civil infrastructure or building elements vs. operation level: visual monitoring of construction equipment and workers). The underlying formulations and assumptions used in these methods are discussed in detail. Finally the gaps in knowledge that need to be addressed in future research are identified. (C) 2015 Elsevier Ltd. All rights reserved.
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