Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects
| Autor: | Sangwon Han, Sangdeok Lee, Chang-Taek Hyun, Yongwoon Cha |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Schedule
causality Association rule learning social network analysis Computer science media_common.quotation_subject lcsh:TJ807-830 Geography Planning and Development lcsh:Renewable energy sources 0211 other engineering and technologies finishing work 02 engineering and technology Management Monitoring Policy and Law Causality (physics) Cost overrun 021105 building & construction 0202 electrical engineering electronic engineering information engineering Quality (business) Social network analysis lcsh:Environmental sciences media_common lcsh:GE1-350 Renewable Energy Sustainability and the Environment lcsh:Environmental effects of industries and plants defect causes lcsh:TD194-195 Risk analysis (engineering) association rule mining 020201 artificial intelligence & image processing |
| Zdroj: | Sustainability Volume 11 Issue 3 Sustainability, Vol 11, Iss 3, p 618 (2019) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su11030618 |
| Popis: | A construction defect can cause schedule delay, cost overrun and quality deterioration. In order to minimize these negative impacts of construction defects, this paper aims to analyze the causality of construction defects. Specifically, association rule mining (ARM) is used to quantify the interrelationships between defect causes, and social network analysis (SNA) is utilized to find out the most influential causes triggering generation of construction defects. The suggested approach was applied to 2949 defect instances in finishing work. Through this application, it was confirmed that the proposed approach can systematically identify and quantify causality among defect causes. |
| Databáze: | OpenAIRE |
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