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Sensor-based safety management

delete2020-05-01
delete86
PRE
AI
A
Amin Asadzadeh
M
Mehrdad Arashpour *
李恒 cover
李恒 (Heng Li)
T
Tuan Ngo
A
Alireza Bab‐Hadiashar
A
Ali Rashidi
DOI:10.1016/j.autcon.2020.103128delete
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Abstract

Abstract

En 中文
The construction industry has one of the most hazardous working environments worldwide, which accounts for about 1 in every 5 occupational fatalities. The high rates of workplace injuries, illnesses and fatalities cause irreversible harm to workers and are often the source of delays and additional project costs. Improvements in sensor technologies, wireless communication, the processing power of computers, and advancements in machine learning and computer vision are now enabling the development of sensor-based safety management systems. The rapid growth of Building Information Modelling (BIM) has also created opportunities for improving safety management. While considerable progress has been made to improve construction safety, few studies have focused on the integration of sensor-based systems and BIM. This research, which is motivated by the development of such integrated methods, carries out a systematic review of the relevant literature, summarising recent developments of sensor-based safety management systems and advancements in safety management through BIM. The research gaps are identified and an outline for potential future research is provided. The results of the review reveal the potential of combining sensor-driven systems with BIM for improving safety management in construction.
Keywords:
INTELLIGENT VIDEO SURVEILLANCE
INFORMATION MODELING BIM
CONSTRUCTION SAFETY
KNOWLEDGE MANAGEMENT
VISUALIZATION TECHNOLOGY
SPATIOTEMPORAL ANALYSIS
PROCESS INTEGRATION
AUTOMATED DETECTION
NEURAL-NETWORKS
WORKER SAFETY
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Automation in Construction cover
Automation in Construction
IF:
11.5
Papers:
6.3K
Citations:
4.2W

Organization

M
Monash University
Scholars:
5.4W
Papers: 5.4W
Citations: 79
H
hong kong polytechnic university
Scholars:
3.0W
Papers: 4.1W
Citations: 921
U
university of melbourne
Scholars:
5.7W
Papers: 5.4W
Citations: 69
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Cited Papers

Cited Papers

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Combining motion and appearance cues for anomaly detection
err2016-03-01
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errZhang, Ying; Lu, Huchuan; Zhang, Lihe; Ruan, Xiang
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A deep learning-based method for detecting non-certified work on construction sites
err2018-01-01
err126
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errFang, Qi; Li, Heng; Luo, Xiaochun; Ding, Lieyun; Rose, Timothy M.; An, Wangpeng; Yu, Yantao
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BIM implementation throughout the UK construction project lifecycle: An analysis
err2013-12-01
err471
PREAI
errEadie, Robert; Browne, Mike; Odeyinka, Henry; McKeown, Clare; McNiff, Sean
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Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit
err2016-08-01
err148
errOAAI
errYang, Kanghyeok; Ahn, Changbum R.; Vuran, Mehmet C.; Aria, Sepideh S.
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Big Data in the construction industry: A review of present status, opportunities, and future trends
err2016-08-01
err453
PREAI
errBilal, Muhammad; Oyedele, Lukumon O.; Qadir, Junaid; Munir, Kamran; Ajayi, Saheed O.; Akinade, Olugbenga O.; Owolabi, Hakeem A.; Alaka, Hafiz A.; Pasha, Maruf
errShare
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