Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

Young-Su Kim, U-Yeol Park, Seoung-Wook Whang, Dong-Joon Ahn and Sangyong Kim
Additional contact information
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

Sustainability, 2021, vol. 13, issue 3, 1-13

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%.

Keywords: artificial intelligence; decision-making; machine learning; retaining wall technique; self-organizing maps (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/3/1328/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/3/1328/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:3:p:1328-:d:488096

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().