Geomechanics and Engineering

  • 제34권3호
  • /
  • Pages.329-339
  • /
  • 2023
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Development of stability evaluation system for retaining walls: Differential evolution algorithm-artificial neural network

  • Dong-Gun Lee (Department of Civil Engineering, Inha University) ;
  • Sang-Yun Lee (Department of Civil Engineering, Inha University) ;
  • Ki-Il Song (Department of Civil Engineering, Inha University)
  • 투고 : 2023.03.23
  • 심사 : 2023.07.14
  • 발행 : 2023.08.10
⟨ 이전 논문 다음 논문 ⟩

초록

The objective of this study is to develop a Stability Evaluation System for retaining walls to assess their safety in real-time during excavation. A ground investigation is typically conducted before construction to gather information about the soil properties and predict wall stability. However, these properties may not accurately reflect the actual ground being excavated. To address this issue, the study employed a differential evolution algorithm to estimate the soil parameters of the actual ground. The estimated results were then used as input for an artificial neural network to evaluate the stability of the retaining walls. The study achieved an average accuracy of over 90% in predicting differential settlement, wall displacement, anchor force, and structural stability of the retaining walls. If implemented at actual excavation sites, this approach would enable real-time prediction of wall stability and facilitate effective safety management. Overall, the developed Stability Evaluation System offers a promising solution for ensuring the stability of retaining walls during construction. By incorporating real-time soil parameter analysis, it enhances the accuracy of stability predictions and contributes to proactive safety management in excavation projects.

키워드

과제정보

This study was conducted with the support of the National R&D Project for Smart Construction Technology (No. 21SMIP-A158708-02) funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure, and Transport and managed by the Korea Expressway Corporation.

참고문헌

  1. An, J.S. and Song, K.I. (2019), "Back analysis of an operating subsea tunnel considering the degradation of ground and concrete lining", Mar. Georesour. Geotech., 37(4), 517-523. https://doi.org/10.1080/1064119X.2018.1427817.
  2. An, J.S., Kang, K.N., Choi, J.Y., Sung, W.S., Suy, V. and Song, K.I. (2020), "Tunnel back analysis based on differential evolution using stress and displacement", Adv. Civil Eng., 2020, 1-10. https://doi.org/10.1155/2020/8156573.
  3. An, J.S., Kim, B.C., Moon, H.K. and Song, K.I. (2016), "DEA optimization for operating tunnel back analysis", J. Korean Tunn. Undergr. Sp. Association, 18(2), 183-193, https://doi.org/10.9711/KTAJ.2016.18.2.183.
  4. Armaghani, D.J., Mirzaei, F., Shariati, M., Trung, N.T., Shariati, M. and Trnavac, D. (2020), "Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber", Geomech. Eng., 20(3), 191-205. https://doi.org/10.12989/gae.2020.20.3.191.
  5. A vila, F.F., Alvala, R.C., Mendes, R.M. and Amore, D.J. (2021), "The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models", Nat. Hazards, 105(1), 1139-1161. https://doi.org/10.1007/s11069-020-04324-x.
  6. Barani, O.R., Bahrami, M. and Sadrnejad, S.A. (2018), "A new finite element for back analysis of a geogrid reinforced soil retaining wall failure", Int. J. Civil Eng., 16(4), 435-441. https://doi.org/10.1007/s40999-017-0150-6.
  7. Bjerrum, L. (1963), "Allowable settlement of structures", Proceedings of the 3rd European Conference on Soil Mechanics and Foundation Engineering, Wiesbaden, Germany.
  8. Chen, H., Asteris, P.G., Jahed Armaghani, D., Gordan, B. and Pham, B.T. (2019), "Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models", Appl. Sci., 9(6), 1042. https://doi.org/10.3390/app9061042.
  9. Cherubini, C. (2000), "Probabilistic approach to the design of anchored sheet pile walls", Comput. Geotech., 26(3-4), 309-330. https://doi.org/10.1016/S0266-352X(99)00044-0.
  10. Construction Safety Management Integrated Information (CSI) (2022), Current status of derivation of risk factors by construction type (civil engineering); Ministry of Land, Infrastructure and Transport, Gyeongsangnam-do, Korea, https://www.csi.go.kr/hrp/stats/hrpStatsByWorkList.do.
  11. EPRI-EL-6800 (1990), Manual on estimating soil properties for foundation design, Electric Power Research Inst.; CA, USA and Cornell Univ.; NY, USA.
  12. Gang, B.J., Hwang, B.S. and Cho, W.J. (2018), "Empirical Estimations of Soil Constants Using Standard Penetration Test N Value", J. Korean Geoenviron. Soc., 19(6), 5-12. https://doi.org/10.14481/jkges.2018.19.6.5.
  13. Gao, W. and He, T.Y. (2017), "Displacement prediction in geotechnical engineering based on evolutionary neural network", Geomech. Eng., 13(5), 845-860. https://doi.org/10.12989/gae.2017.13.5.845.
  14. Gordan, B., Koopialipoor, M., Clementking, A., Tootoonchi, H. and Tonnizam Mohamad, E. (2019), "Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques", Eng. with Comput., 35(3), 945-954. https://doi.org/10.1007/s00366-018-0642-2.
  15. Javadi, A.A. and Rezania, M. (2009), "Applications of artificial intelligence and data mining techniques in soil modeling", Geomech. Eng., 1(1), 53-74. https://doi.org/10.12989/gae.2009.1.1.053.
  16. Korea Expressway Corporation (2010), Highway Design Practical Data Book, Korea Expressway Corporation, Gimcheon-si, Gyeongsangbuk-do, Republic of Korea
  17. Lee, J.H., Kim, J.H., Park, M.C. and Kim, J.W. (2021), "Introduction of smart measurement and system improvement plan for underground retaining construction", Seoul institute of technology.
  18. Li, H.N., Ren, L., Jia, Z.G., Yi, T.H. and Li, D.S. (2016), "State-of-the-art in structural health monitoring of large and complex civil infrastructures", J. Civil Struct. Health Monit., 6(1), 3-16. https://doi.org/10.1007/s13349-015-0108-9.
  19. Lundberg, S.M., Erion, G.G. and Lee, S.I. (2018), "Consistent individualized feature attribution for tree ensembles", Preprint, submitted February 12, 2018. https://arxiv.org/abs/1802.0388.
  20. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Math. Biophys., 5(4), 115-133. https://doi.org/10.1007/BF02478259.
  21. Meyerhof, G.G. (1956), "Penetration tests and bearing capacity of cohesionless soils", J. Soil Mech. Found. Division, 82(1), https://doi.org/10.1061/JSFEAQ.00000.
  22. Mohamed, T., Kasa, A., Rahman, N.A. and Gaber, M. (2019), "Prediction of external stability for segmental retaining wall using neural network and support vector machine", Technology, 10(2), 2491-2498. https://doi.org/10.14569/ijacsa.2021.0120648.
  23. Nair, V. and Hinton, G.E. (2010). "Rectified linear units improve restricted boltzmann machines", Proceedings of the 27th international conference on machine learning (ICML-10), 807-814.
  24. Park, L.G., Kim, T.O., Seo, Y.H., An, M.H., Yu, S.H. and Hong, Y.S. (2019), "Status of Retaining Facilities and Examples of Collapse", Geotech. Eng., 35(4), 50-67.
  25. Sakurai, S. (1997), "Lessons learned from field measurements in tunnelling", Tunn. Undergr. Sp. Tech., 12(4), 513-522. https://doi.org/10.1016/S0886-7798(98)00004-2.
  26. Salehi, M. and Erduran, E. (2022), "Identification of boundary conditions of railway bridges using artificial neural networks", J. Civil Struct. Health Monit., 12(5), 1223-1246. https://doi.org/10.1007/s13349-022-00613-0.
  27. Seong, J.H., Jeong, S.H. and Shin, J.Y. (2011), "A study on safety management improvement plan for retaining excavation construction through accident case analysis", J. Korea Inst. Struct. Maint. Inspection, 15(6), 175-183. https://doi.org/10.11112/jksmi.2011.15.6.175.
  28. Shon, S.D., Yoo, M.N., Lee, S.J. and Kang, J.W. (2013), "Development of optimum design program of sinusoidal corrugated web beam using differential evolution algorithm", J. Architect. Inst. Korea Struct. Constr., 29(12), 21-28. https://doi.org/10.5659/JAIK_SC.2013.29.12.21.
  29. Storn, R. and Price, K. (1997), "Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces", J. Global Optim., 11(4), 341-359. https://doi.org/10.1023/A:1008202821328.
  30. Woo, J.I. and Chung, D.S. (2020), "Numerical analysis of anchored in-situ wall using back-analysis technique", J. Soc. Disaster Inform., 16(1), 1-9. https://doi.org/10.15683/kosdi.2020.3.31.001.
  31. Xiang, G., Yin, D., Cao, C. and Yuan, L.(2021), "Application of artificial neural network for prediction of flow ability of soft soil subjected to vibrations", Geomech. Eng., 25(5), 395-403. https://doi.org/10.12989/gae.2021.25.5.395.
  32. Zevgolis, I.E. and Bourdeau, P.L. (2010), "Probabilistic analysis of retaining walls", Comput. Geotech., 37(3), 359-373. https://doi.org/10.1016/j.compgeo.2009.12.003.
  33. Zhao, H., Chen, B. and Li, S. (2021), "Determination of geomaterial mechanical parameters based on back analysis and reduced-order model", Comput. Geotech., 132, 104013. https://doi.org/10.1016/j.compgeo.2021.104013.
  34. Zhong, Q., Chen, S., Wang, L. and Shan, Y. (2020), "Back analysis of breaching process of Baige landslide dam", Landslides, 17(7), 1681-1692. https://doi.org/10.1007/s10346-020-01398-3.