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2018 Vol.17, Issue 4 Preview Page

Research Article

Artificial Intelligence (AI)-based Deep Excavation Designed Program
Chungsik Yoo1
,
Haider Syed Aizaz2*
,
Qaisar Abbas3
,
Jaewon Yang4
1Member, Professor, School of Civil and Environmental Engineering, Sungkyunkwan Univ. Natural Sciences Campus, Cheoncheon-dong, Jangan-gu, Suwan-si, Gyeonggi-do 16419, Korea
2Member, Graduate Student, School of Civil and Environmental Engineering, Sungkyunkwan Univ. Natural Sciences Campus, Cheoncheon-dong, Jangan-gu, Suwan-si, Gyeonggi-do 16419, Korea
3Student Member, Graduate Student, School of Civil and Environmental Engineering, Sungkyunkwan Univ. Natural Sciences Campus, Cheoncheon-dong, Jangan-gu, Suwan-si, Gyeonggi-do 16419, Korea
4Member, Graduate Student, School of Civil and Environmental Engineering, Sungkyunkwan Univ. Natural Sciences Campus, Cheoncheon-dong, Jangan-gu, Suwan-si, Gyeonggi-do 16419, Korea
* Corresponding Author
30 December 2018. pp. 277-292
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Abstract

This paper presents the development and implementation of an artificial intelligence (AI)-based deep excavation induced wall and ground displacements and wall support member forces prediction program (ANN-EXCAV). The program has been developed in a C# environment by using the well-known AI technique artificial neural network (ANN). Program used ANN to predict the induced displacement, groundwater drawdown and wall and support member forces parameters for deep excavation project and run the stability check by comparing predict values to the calculated allowable values. Generalised ANNs were trained to predict the said parameters through databases generated by numerical analysis for cases that represented real field conditions. A practical example to run the ANN-EXCAV is illustrated in this paper. Results indicate that the program efficiently performed the calculations with a considerable accuracy, so it can be handy and robust tool for preliminary design of wall and support members for deep excavation project.

Keywords
Artificial intelligence
Deep excavation
Artificial neural network
Numerical analysis

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Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 17
  • No :4
  • Pages :277-292
  • Received Date : 2018-11-21
  • Revised Date : 2018-12-14
  • Accepted Date : 2018-12-20
  • DOI :https://doi.org/10.12814/jkgss.2018.17.4.277
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