Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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A Study on Stochastic Gradient Tree Boosting Model for the Retaining Wall Selection in Domestic Building Construction

국내 건축공사 흙막이공법 선정을 위한 Stochastic Gradient Tree Boosting 모델에 관한 연구

  • Shin, Yoonseok (Department of Architectural Engineering, Kyonggi University)
  • Received : 2021.04.26
  • Accepted : 2021.06.30
  • Published : 2021.07.30
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Abstract

The rise of land prices and population density in urban areas has led to a need for deeper excavations, both for the building ground and the underground. It is difficult to select a retaining wall method that is appropriate for a construction site, not only because the retaining wall method should be chosen at an early stage of a construction project, at which time there is a lack of information on surrounding characteristics of the site, but also because there are uncertain factors such as underground water and the underlying rock formation. An inappropriate retaining wall method may cause changes in the original design or method of retaining wall, resulting in an inevitable increase in construction costs. Despite this fact, construction practitioners generally select a retaining wall method depending on their own limited, subjective experience and intuition. For this reason, in this study, I applied the stochastic gradient tree boosting (SGTB) technique to selecting a retaining wall method to assess the applicability of the technique to a work method selection. To evaluate the SGTB technique's performance, I built the models using NN as well as SGTB and then compared the results between the models. As a result, it was found out that the SGTB is relatively more excellent and stable compared to NN model when it comes to selecting a retaining wall. Consequently SGTB is helpful to practitioners who need to determine the excavation work at building construction project.

Keywords

Acknowledgement

본 연구는 2018학년도 경기대학교 학술연구비(일반과제) 지원에 의하여 수행되었음.

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