Cause analysis of construction collapse accidents using association rule mining
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 4120-4142 |
Journal / Publication | Engineering, Construction and Architectural Management |
Volume | 30 |
Issue number | 9 |
Online published | 1 Jun 2022 |
Publication status | Published - 27 Nov 2023 |
Link(s)
Abstract
Purpose - The construction collapse is one of the most serious accidents since it has several attributes (e.g. accident type and consequence) and its occurrence involves various kinds of causal factors (e.g. human factors). The impact of causal factors on construction collapse accidents and the interrelationships among causal factors remain poorly explored. Thus, the purpose of this paper is to use association rule mining (ARM) for cause analysis of construction collapse accidents.
Design/methodology/approach - An accident analytic framework is developed to determine the accident attributes and causal factors, and then ARM is introduced as the method for data mining. The data are from 620 historical accident records on government websites of China from 2010 to 2020. Through the generated association rules, the impact of causal factors and the interrelationships among causal factors are explored.
Findings - Collapse accident is easily caused by human factors, material and machine condition and management factors. Furthermore, the results show a close interrelationship between many causal factors and construction scheme and organization. The earthwork collapse is greatly related to environmental condition and the scaffolding collapse is greatly related to material and machine condition.
Practical implications - This study found relevant knowledge about the key causes for different types of construction collapses. Besides, several suggestions are further provided for construction units to prevent construction collapse accidents.
Originality/value - This study uses data mining methods to extract knowledge about the causes of collapse accidents. The impact of causal factors on various types of construction collapse accidents and the interrelationships among causal factors are explained from historical accident data.
© Emerald Publishing Limited
Design/methodology/approach - An accident analytic framework is developed to determine the accident attributes and causal factors, and then ARM is introduced as the method for data mining. The data are from 620 historical accident records on government websites of China from 2010 to 2020. Through the generated association rules, the impact of causal factors and the interrelationships among causal factors are explored.
Findings - Collapse accident is easily caused by human factors, material and machine condition and management factors. Furthermore, the results show a close interrelationship between many causal factors and construction scheme and organization. The earthwork collapse is greatly related to environmental condition and the scaffolding collapse is greatly related to material and machine condition.
Practical implications - This study found relevant knowledge about the key causes for different types of construction collapses. Besides, several suggestions are further provided for construction units to prevent construction collapse accidents.
Originality/value - This study uses data mining methods to extract knowledge about the causes of collapse accidents. The impact of causal factors on various types of construction collapse accidents and the interrelationships among causal factors are explained from historical accident data.
© Emerald Publishing Limited
Research Area(s)
- Accident prevention, Association rule, Cause analysis, Construction collapse, Knowledge discovery
Citation Format(s)
Cause analysis of construction collapse accidents using association rule mining. / Shao, Lijia; Guo, Shengyu; Dong, Yimeng et al.
In: Engineering, Construction and Architectural Management, Vol. 30, No. 9, 27.11.2023, p. 4120-4142.
In: Engineering, Construction and Architectural Management, Vol. 30, No. 9, 27.11.2023, p. 4120-4142.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review