Identification of Defect Generation Rules among Defects in Construction Projects Using Association Rule Mining
| Autor: | Yongwoon Cha, Chang-Taek Hyun, Jung-Eun Park, Sangwon Han, Hamad Al Jassmi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:GE1-350
Association rule learning Renewable Energy Sustainability and the Environment Computer science Process (engineering) defect prevention lcsh:Environmental effects of industries and plants Geography Planning and Development lcsh:TJ807-830 0211 other engineering and technologies lcsh:Renewable energy sources 02 engineering and technology defect generation rule 010501 environmental sciences Management Monitoring Policy and Law 01 natural sciences multi-family housing Identification (information) lcsh:TD194-195 Risk analysis (engineering) association rule mining 021105 building & construction construction quality management lcsh:Environmental sciences 0105 earth and related environmental sciences |
| Zdroj: | Sustainability, Vol 12, Iss 3875, p 3875 (2020) Sustainability Volume 12 Issue 9 |
| ISSN: | 2071-1050 |
| Popis: | This study aims to identify the defect generation rules between defects, to support effective defect prevention at construction sites. Numerous studies have been performed to identify the relations between defect causes, to prevent defects in construction projects. However, identifying the inter-causal pattern does not yet guarantee an ultimate grasp of what constitutes proper defect mitigation strategies, unless the underlying defect-to-defect generation rules are thoroughly understood too. Specifically, if a defect generated in a work process is ignored without taking necessary corrective action, then additional defects could be generated in its following works as well. Thus, to minimize defect generation, this study analyzes the defects in the sequence of a construction work. To achieve this, the authors collected 9054 defect data, and association rule mining is used to analyze the rules between the defects. Consequently, 216 rules are identified, and 152 rules are classified into 3 categories along with 4 experts (71 expected rules, 22 unexpected but explainable rules, and 59 unexpected and unexplainable rules). The generation rules between the defects identified in this study are expected to be used to regularize various defect types to determine those that require priority management. |
| Databáze: | OpenAIRE |
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