Integrating data mining with KJ method to classify bridge construction defects
Integrating data mining with KJ method to classify bridge construction defects
复制标题
DOI：
10.1016/j.eswa.2010.12.047
复制
发表时间：
2011-06-01
影响因子：
8.5
通讯作者：
Leu, Sou-Sen
中科院分区：
文献类型：
--
作者：
Cheng, Ying-Mei;Leu, Sou-Sen
本文试图对桥梁施工中常见的缺陷进行分析，对其进行分类，并将其重新定义为提高桥梁施工质量的预防措施和手段。为此，2007年1月以来的桥梁建设数据来自台湾公共建设委员会（PCC）的评估报告。根据桥梁施工缺陷的特点对其进行分类。提出并应用了基于约束的聚类方法和亲和图方法。该方法可以同时处理混合数据类型；此外，它还可以合并用户指定的约束。质量或安全问题、负责单位（政府部门/项目业主/承包商）、缺陷属性（施工/审计/文件/其他）为分类属性。约束是避免空集群或具有很少对象的集群。结果显示了五个主要缺陷类别：安全与环境、施工现场缺陷、监督/控制过程、施工质量文件和其他。(C) 2010 Elsevier Ltd.版权所有。
This paper tries to analyze common bridge construction defects, classify them into appropriate groups, and redefine them as a precautionary measure and means to improve quality in bridge construction. For this purpose, data on bridge construction since January 2007 were obtained from the evaluation report of the Public Construction Committee (PCC) of Taiwan. Bridge construction defects were classified according to their characteristics. A constraint-based clustering method and affinity diagram (KJ method) are proposed and used. This method can simultaneously treat mixed data types; moreover, it can incorporate user-specified constraints. The quality or safety issues, the unit-in-charge (Government authorities/project owners/contractor), and the properties of the defects (construction/audit/documents/others) are the sorting attributes. The constraint is avoiding empty clusters or clusters having very few objects. The results revealed five major defect classifications: safety and environment, construction site defects, supervision/control process, construction quality documents, and others. (C) 2010 Elsevier Ltd. All rights reserved.