Earticle

초록

영어

It has been recognized that the value of buildings gradually decreases over time due to aesthetic, functional, and structural deteriorations caused by various defects. In response to these deteriorations, the value of buildings can be restored by various types of maintenance operations. However, minimizing depreciation is better than maintenance. In this regard, the defects that cause depreciation should be reduced through data analysis on existing buildings. This study suggests the “Pixelization Method (PM)” for data mining on building façade elements to collect data on façade defects. PM is a technique that supports the creation of a database by simplifying the analysis of building façades as well as the input data. The database, developed with PM, is expected to be used in various areas such as analysis of the relationship between the defects and the facade elements or the prediction of defects.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1. Defects in Building Exterior Wall
  2.2. Influencing Factors of Defect
  2.2. Defect Analysis Method
  2.3. Summary
 3. “Pixelization Method” for Mining Exterior Wall Information
  3.1. Definition
  3.2. Data Collection Process for “Pixelization Method”
  3.2. Props and Cons of “Pixelization Method”
 4. Utilization of “Pixelization Method”
  4.1. Relation Analysis of Façade Elements and Defects
  4.2. Predicting the Degree of Defects based on Case-based Reasoning
  4.3. Summary
 5. Conclusions
 References

키워드

저자

• Jaeseob Lee [ Department of Architectural Engineering, Dongguk University, Korea ]
• Yongdeok Jeon [ Department of Architectural Engineering, Dongguk University, Korea ]
• Kichang Jeong [ Construction Cost Management Research Institute, Korea ]
• Yeonwoo Shin [ Department of Architectural Engineering, Dongguk University, Korea ]
• Wooram Kim [ Department of Architectural Engineering, Dongguk University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605