A. Gurmu, M. R. Hosseini, Mehrdad Arashpour, Wellia Lioeng
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Data mining methods were applied using a wide range of Python libraries including NumPy, Pandas, Natural Language Toolkit, SpaCy and Regular Expression, alongside association rule mining (ARM) and simulations. Findings Findings reveal that defects in multi-storey buildings often occur on lower levels, rather than on higher levels. Joinery defects were found to be the most recurrent problem on ground floors. The ARM outcomes show that the occurrence of one type of defect can be taken as an indication for the existence of other types of defects. For instance, in laundry, the chance of occurrence of plumbing and joinery defects, where paint defects are observed, is 88%. The stochastic model built for door defects showed that there is a 60% chance that defects on doors can be rectified within 60 days. Originality/value The dashboards provide original insight and novel ideas regarding the frequency of defects in various positions in multi-storey buildings. 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引用次数: 0
摘要
目的建筑缺陷正在成为大多数高层建筑中反复出现的现象。然而,很少有研究基于真实项目的数据来分析高层建筑的缺陷。本研究旨在开发仪表盘和模型,以揭示最常见的缺陷位置,了解缺陷之间的关联,并预测整改周期。设计/方法/方法总共从一家为澳大利亚维多利亚州建筑业提供咨询服务的公司获得了15484份缺陷报告,包括定性和定量数据。数据挖掘方法使用了广泛的Python库,包括NumPy、Pandas、Natural Language Toolkit、SpaCy和Regular Expression,以及关联规则挖掘(ARM)和模拟。调查结果显示,多层建筑的缺陷往往发生在较低的楼层,而不是较高的楼层。细木工缺陷被发现是底层最常见的问题。ARM结果表明,一种类型缺陷的发生可以作为其他类型缺陷存在的指示。例如,在洗衣房中,发现油漆缺陷的管道和细木工缺陷发生的几率为88%。针对车门缺陷建立的随机模型表明,车门上的缺陷在60秒内得到纠正的可能性为60% 天。独创性/价值仪表盘提供了关于多层建筑中不同位置缺陷频率的独创见解和新颖想法。随机模型可以为物业经理、住户和分包商提供可靠的参考点,以采取措施避免缺陷再次发生;研究结果也为各种类型的缺陷提供了可能的整改周期的估计。
Development of building defects dashboards and stochastic models for multi-storey buildings in Victoria, Australia
Purpose Building defects are becoming recurrent phenomena in most high-rise buildings. However, little research exists on the analysis of defects in high-rise buildings based on data from real-life projects. This study aims to develop dashboards and models for revealing the most common locations of defects, understanding associations among defects and predicting the rectification periods. Design/methodology/approach In total, 15,484 defect reports comprising qualitative and quantitative data were obtained from a company that provides consulting services for the construction industry in Victoria, Australia. Data mining methods were applied using a wide range of Python libraries including NumPy, Pandas, Natural Language Toolkit, SpaCy and Regular Expression, alongside association rule mining (ARM) and simulations. Findings Findings reveal that defects in multi-storey buildings often occur on lower levels, rather than on higher levels. Joinery defects were found to be the most recurrent problem on ground floors. The ARM outcomes show that the occurrence of one type of defect can be taken as an indication for the existence of other types of defects. For instance, in laundry, the chance of occurrence of plumbing and joinery defects, where paint defects are observed, is 88%. The stochastic model built for door defects showed that there is a 60% chance that defects on doors can be rectified within 60 days. Originality/value The dashboards provide original insight and novel ideas regarding the frequency of defects in various positions in multi-storey buildings. The stochastic models can provide a reliable point of reference for property managers, occupants and sub-contractors for taking measures to avoid reoccurring defects; so too, findings provide estimations of possible rectification periods for various types of defects.