A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An association rule mining model for the assessment of the correlations between the attributes of severe accidents
Identifying the correlations between the attributes of severe accidents could be vital to preventing them. If such relationships were known dynamically, it would be possible to take preventative actions against accidents. The paper aims to develop an analytical model that is adaptable for each type of data to create preventative measures that will be suitable for any computational systems. The present model collectively shows the relationships between the attributes in a coherent manner to avoid severe accidents. In this respect, Association Rule Mining (ARM) is used as the technique to identify the correlations between the attributes. The research adopts a positivist approach to adhere to the factual knowledge concerning nine different accident types through case studies and quantitative measurements in an objective nature. ARM was exemplified with nine different types of construction accidents to validate the adaptability of the proposed model. The results show that each accident type has different characteristics with varying combinations of the attribute, and analytical model accomplished to accommodate variation through the dataset. Ultimately, professionals can identify the cause-effect relationships effectively and set up preventative measures to break the link between the accident causing factors.
An association rule mining model for the assessment of the correlations between the attributes of severe accidents
Identifying the correlations between the attributes of severe accidents could be vital to preventing them. If such relationships were known dynamically, it would be possible to take preventative actions against accidents. The paper aims to develop an analytical model that is adaptable for each type of data to create preventative measures that will be suitable for any computational systems. The present model collectively shows the relationships between the attributes in a coherent manner to avoid severe accidents. In this respect, Association Rule Mining (ARM) is used as the technique to identify the correlations between the attributes. The research adopts a positivist approach to adhere to the factual knowledge concerning nine different accident types through case studies and quantitative measurements in an objective nature. ARM was exemplified with nine different types of construction accidents to validate the adaptability of the proposed model. The results show that each accident type has different characteristics with varying combinations of the attribute, and analytical model accomplished to accommodate variation through the dataset. Ultimately, professionals can identify the cause-effect relationships effectively and set up preventative measures to break the link between the accident causing factors.
An association rule mining model for the assessment of the correlations between the attributes of severe accidents
Bilal Umut Ayhan (author) / Neşet Berkay Doğan (author) / Onur Behzat Tokdemir (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Cause analysis of construction collapse accidents using association rule mining
| Emerald Group Publishing | 2023
SARM - Succinct Association Rule Mining: An Approach to Enhance Association Mining
| British Library Conference Proceedings | 2005
Analysis of Association Rule in Fire Accidents - Evidence from Taipei City
| Trans Tech Publications | 2014