Journal of the Korean Geosynthetics Society (한국지반신소재학회논문집)

Korean Geosynthetics Society (한국지반신소재학회)
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Artificial Intelligence (AI)-based Deep Excavation Designed Program

  • Yoo, Chungsik (School of Civil and Environmental Engineering, Sungkyunkwan Univ. Natural Sciences Campus) ;
  • Aizaz, Haider Syed (School of Civil and Environmental Engineering, Sungkyunkwan Univ. Natural Sciences Campus) ;
  • Abbas, Qaisar (School of Civil and Environmental Engineering, Sungkyunkwan Univ. Natural Sciences Campus) ;
  • Yang, Jaewon (School of Civil and Environmental Engineering, Sungkyunkwan Univ. Natural Sciences Campus)
  • Received : 2018.11.21
  • Accepted : 2018.12.20
  • Published : 2018.12.30
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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

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Fig. 1. ANN implementation steps

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Fig. 2. Program general overview

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Fig. 3. Input module layout

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Fig. 4. General structure of ANN

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Fig. 5. Displacement sub module

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Fig. 6. Displacement sub module

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Fig. 7. Wall and support sub module

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Fig. 8. Excavation case description

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Fig. 9. CIP wall parameter description

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Fig. 10. SCW parameter description

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Fig. 11. Groundwater submodule results comparison with SeepW

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Fig. 12. Displacement submodule results comparison with Plaxis

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Fig. 13. Wall and support submodule results comparison with GeoXD

Table 1. General input used in ANNs training

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Table 2. General ground material properties

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Table 3. Data ranges wall and ground displacement and wall and support member forces DBs

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Table 4. Data ranges ground water drawdown DB

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Table 5. Data ranges ground water drawdown DB

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Table 6. Ground and support input parameters

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Table 7. Strut parameters

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Table 8. H-Pile wall parameters

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Table 9. CIP wall parameters

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Table 10. SCW parameters

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Table 11. Validation results for groundwater submodule

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Table 12. Validation results for displacement submodule

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Table 13. Validation results for wall and support submodule

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Table 14. Stability check results for CIP and H-Pile Walls

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Table 15. Stability check results for SCW

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