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Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

Author

Listed:
  • Young-Su Kim

    (Department of Architectural Engineering, Pusan National University, 2 Busandaehak-ro, Busan 46241, Korea)

  • U-Yeol Park

    (Department of Architectural Engineering, Andong National University, 1375 Gyeongdong-ro, Andong 36729, Korea)

  • Seoung-Wook Whang

    (School of Architecture Computing and Engineering, University of East London, London E16 2RD, UK)

  • Dong-Joon Ahn

    (School of Architecture, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi 39177, Korea)

  • Sangyong Kim

    (School of Architecture, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Korea)

Abstract

Construction projects in urban areas tend to be associated with high-rise buildings and are of very large-scales; hence, the importance of a project’s underground construction work is significant. In this study, a rational model based on machine learning (ML) was developed. ML algorithms are programs that can learn from data and improve from experience without human intervention. In this study, self-organizing maps (SOMs) were utilized. An SOM is an alternative to existing ML methods and involves a subjective decision-making process because a developed model is used for data training to classify and effectively recognize patterns embedded in the input data space. In addition, unlike existing methods, the SOM can easily create a feature map by mapping multidimensional data to simple two-dimensional data. The objective of this study is to develop an SOM model as a decision-making approach for selecting a retaining wall technique. N-fold cross-validation was adopted to validate the accuracy of the SOM model and evaluate its reliability. The findings are useful for decision-making in selecting a retaining wall method, as demonstrated in this study. The maximum accuracy of the SOM was 81.5%, and the average accuracy was 79.8%.

Suggested Citation

  • Young-Su Kim & U-Yeol Park & Seoung-Wook Whang & Dong-Joon Ahn & Sangyong Kim, 2021. "Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps," Sustainability, MDPI, vol. 13(3), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1328-:d:488096
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    References listed on IDEAS

    as
    1. Nour, Mohamed A. & Madey, Gregory R., 1996. "Heuristic and optimization approaches to extending the Kohonen self organizing algorithm," European Journal of Operational Research, Elsevier, vol. 93(2), pages 428-448, September.
    2. Karolina Taczanowska & Luis-Millán González & Xavier García-Massó & Antoni Zięba & Christiane Brandenburg & Andreas Muhar & Maite Pellicer-Chenoll & José-Luis Toca-Herrera, 2019. "Nature-based Tourism or Mass Tourism in Nature? Segmentation of Mountain Protected Area Visitors Using Self-Organizing Maps (SOM)," Sustainability, MDPI, vol. 11(5), pages 1-13, March.
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