Integrating data mining with KJ method to classify bridge construction defects

Authors:
Ying-Mei Cheng;Sou-Sen Leu
Affiliations:
Department of Civil Engineering, China University of Technology, 56 Hsing-Lung Road, Section 3, Taipei 116, Taiwan, ROC;Department of Construction Engineering, National Taiwan University of Science and Technology, 43 Kee-Lung Road, Section 4, Taipei 10672, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

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.