Computer vision-based safety risk computing and visualization on construction sites

被引：18

作者：

Hou, Xiaoyu ^{[1
]}

Li, Chengqian ^{[1
]}

Fang, Qi ^{[2
]}

机构：

[1] Hunan Univ, Sch Civil Engn, Changsha 410082, Peoples R China

[2] Cent South Univ, Sch Civil Engn, Changsha 410004, Peoples R China

来源：

AUTOMATION IN CONSTRUCTION | 2023年 / 156卷

基金：

中国国家自然科学基金;

关键词：

Computer-vision; Safety risk management; Risk visualization; Spatial interaction; Automated image processing; WORKERS SAFETY; FRAMEWORK; FALLS;

D O I：

10.1016/j.autcon.2023.105129

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Despite advancements in computer vision technology for construction site safety, the identification and evaluation of potential safety risks stemming from on-site hazardous objects and their spatial interactions is underresearched. Moreover, the subsequent visualization of risks, essential for effective safety management, remains insufficiently explored. This paper presents a model combining computer vision and the TOPSIS method to automate safety risk quantification and visualization during the construction process. Uniquely, it enhances risk predictions by dynamically monitoring the quantity and distance changes of hazard sources and their risk-related objects in real-time, beyond pre-construction risk identification and predictions. Experimental validation shows the model's effectiveness in risk quantification and visualization, with a high consistency ratio of 95% compared to expert manual assessments. This model lays the groundwork for more precise risk calculation and evaluation, fostering improved safety management decisions.

引用

页数：18

共 53 条

[1]

Ali A.K., 2020, 8 TH INT C CONSTRUCT

[2]

[Anonymous], 2007, China's Regulations on Reporting and Investigation and Handling of Production Safety Accidents

[3]

[Anonymous], 2012, China Architecture & Building Press, Technical Specification for Safety Operation of Construction Machinery

[4] Hazard identification and risk assessment using integrated exposure frequency and legislation requirements (HIRA-FL) in construction sites [J].

Arumugaprabu, V ;

Ajith, S. ;

Jerendran, J. ;

Naresh, K. ;

Sreekanth, P. S. Rama .

MATERIALS TODAY-PROCEEDINGS, 2022, 56 :1247-1250

[5] A state-of the-art survey of TOPSIS applications [J].

Behzadian, Majid ;

Otaghsara, S. Khanmohammadi ;

Yazdani, Morteza ;

Ignatius, Joshua .

EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (17) :13051-13069

[6]

Bertelsen S., 2003, P 11 ANN M INT GROUP, P11

[7] Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks [J].

Cha, Young-Jin ;

Choi, Wooram ;

Buyukozturk, Oral .

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2017, 32 (05) :361-378

[8]

Chansik P., 2020, Budapest Univ. Technol. Econ., P31, DOI [10.3311/ccc2020-052, DOI 10.3311/CCC2020-052]

[9] A proactive workers' safety risk evaluation framework based on position and posture data fusion [J].

Chen, Hainan ;

Luo, Xiaowei ;

Zheng, Zhuang ;

Ke, Jinjing .

AUTOMATION IN CONSTRUCTION, 2019, 98 :275-288

[10]

China Architecture & Building Press, 2018, Safety Standard for Prevention of Frequent Accidents in Construction

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