TY - JOUR
T1 - An integrated ergonomics framework for evaluation and design of construction operations
AU - Golabchi, Alireza
AU - Guo, Xingzhou
AU - Liu, Meiyin
AU - Han, Sang Uk
AU - Lee, Sang Hyun
AU - AbouRizk, Simaan
N1 - Funding Information:
This research was supported by the Natural Sciences and Engineering Research Council of Canada ( NSERC CRDPJ 470598-14 ), the National Science Foundation Award ( IIP-1640633 ), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) ( NRF-2018R1A5A1025137 ). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the Natural Sciences and Engineering Research Council of Canada, the National Science Foundation, or the National Research Foundation of Korea.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/11
Y1 - 2018/11
N2 - Labor is one of the most critical resources in the construction industry due to its impact on the productivity, safety, quality, and cost of a construction project. Ergonomic assessment, as a tool and method for analyzing human activities and their interactions with the surrounding environment, is thus crucial for designing operations and workplaces that achieve both high productivity and safety. In construction, however, the constantly changing work environments and laborious tasks cause traditional approaches to ergonomic analysis, such as manual observations and measurements, to require substantial time and effort to yield reliable results. Therefore, to simplify and automate the assessment processes, this study explores the adaptation and integration of various existing methods for data collection, analysis, and output representation potentially available for comprehensive ergonomic analysis. The proposed framework integrates sensing for data collection, action recognition and simulation modeling for productivity and ergonomic analysis, and point cloud model generation and human motion animation for output visualization. The proposed framework is demonstrated through a case study using data from an off-site construction job site. The results indicate that integrating the various techniques can facilitate the assessment of manual operations and thereby enhance the implementation of ergonomic practices during a construction project by reducing the time, effort, and complexity required to apply the techniques.
AB - Labor is one of the most critical resources in the construction industry due to its impact on the productivity, safety, quality, and cost of a construction project. Ergonomic assessment, as a tool and method for analyzing human activities and their interactions with the surrounding environment, is thus crucial for designing operations and workplaces that achieve both high productivity and safety. In construction, however, the constantly changing work environments and laborious tasks cause traditional approaches to ergonomic analysis, such as manual observations and measurements, to require substantial time and effort to yield reliable results. Therefore, to simplify and automate the assessment processes, this study explores the adaptation and integration of various existing methods for data collection, analysis, and output representation potentially available for comprehensive ergonomic analysis. The proposed framework integrates sensing for data collection, action recognition and simulation modeling for productivity and ergonomic analysis, and point cloud model generation and human motion animation for output visualization. The proposed framework is demonstrated through a case study using data from an off-site construction job site. The results indicate that integrating the various techniques can facilitate the assessment of manual operations and thereby enhance the implementation of ergonomic practices during a construction project by reducing the time, effort, and complexity required to apply the techniques.
KW - Action recognition
KW - Ergonomics
KW - Point cloud generation
KW - Sensing
KW - Simulation
KW - Visualization
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U2 - 10.1016/j.autcon.2018.08.003
DO - 10.1016/j.autcon.2018.08.003
M3 - Article
AN - SCOPUS:85051637370
VL - 95
SP - 72
EP - 85
JO - Automation in Construction
JF - Automation in Construction
SN - 0926-5805
ER -