Development of Stability Evaluation Algorithm for C.I.P. Retaining Walls During Excavation

Dong-Gun Lee; Jeong-Yeon Yu; Ji-Yeol Choi; Ki-Il Song

doi:10.7843/kgs.2023.39.9.13

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2023 Vol.39, Issue 9 Preview Page Next Page

Development of Stability Evaluation Algorithm for C.I.P. Retaining Walls During Excavation 가시설 벽체(C.I.P.)의 굴착중 안정성 평가 알고리즘 개발

30 September 2023. pp. 13-24

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Abstract

To investigate the stability of temporary retaining walls during excavation, it is essential to develop reverse analysis technologies capable of precisely evaluating the properties of the ground and a learning model that can assess stability by analyzing real-time data. In this study, we targeted excavation sites where the C.I.P method was applied. We developed a Deep Neural Network (DNN) model capable of evaluating the stability of the retaining wall, and estimated the physical properties of the ground being excavated using a Differential Evolution Algorithm. We performed reverse analysis on a model composed of a two-layer ground for the applicability analysis of the Differential Evolution Algorithm. The results from this analysis allowed us to predict the properties of the ground, such as the elastic modulus, cohesion, and internal friction angle, with an accuracy of 97%. We analyzed 30,000 cases to construct the training data for the DNN model. We proposed stability evaluation grades for each assessment factor, including anchor axial force, uneven subsidence, wall displacement, and structural stability of the wall, and trained the data based on these factors. The application analysis of the trained DNN model showed that the model could predict the stability of the retaining wall with an average accuracy of over 94%, considering factors such as the axial force of the anchor, uneven subsidence, displacement of the wall, and structural stability of the wall.

Keywords

Back analysis

C.I.P.

DNN

Factor of safety

Numerical analysis

Retaining wall

가시설 흙막이의 굴착중 안정성 분석에 대한 연구를 위해서는 지반의 정확한 물성을 평가할 수 있는 역해석 기술과 실시간으로 계측되는 데이터를 분석하여 안정성을 평가할 수 있는 학습모델의 개발이 필요하다. 본 연구에서는 CIP공법이 적용된 굴착 현장을 대상으로 차분진화 알고리즘을 통해 굴착 중인 지반의 물성치를 추정하고, 벽체의 안정성을 평가할 수 있는 DNN 모델을 개발하였다. 차분진화 알고리즘의 적용성 분석을 위하여 2층 지반으로 구성된 모델에 대한 역해석을 수행하였고, 역해석 결과 지반의 탄성계수, 점착력, 내부마찰각을 97%의 정확도로 예측할 수 있는 것으로 분석되었다. DNN 모델의 학습데이터 구축을 위하여 30,000개의 케이스에 대하여 해석을 수행하였다. 앵커 축력, 부등침하, 벽체 변위, 벽체 구조적 안정성 등 각각의 평가요소에 대한 안정성 평가 등급을 제시하였고, 그에 따라 데이터를 학습하였다. 학습된 DNN 모델의 적용성 분석 결과, 앵커의 축력, 부등침하, 벽체의 변위, 벽체의 구조안정성에 대해 평균 94% 이상으로 벽체의 안정성을 예측할 수 있는 것으로 평가되었다.

References

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", Advances in Civil Engineering, Vol.2020, pp.1-10. 10.1155/2020/8156573

An, J. S., Kim, B. C., Moon, H. K., and Song, K. I. (2016), "DEA Optimization for Operating Tunnel Back Analysis", Journal of Korean Tunnelling and Underground Space Association, Vol.18, No.2, pp.183-193. 10.9711/KTAJ.2016.18.2.183

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", Geomechanics and Engineering, Vol.20, No.3, pp.191-205.

Ávila, F. F., Alvalá, 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", Natural Hazards, Vol.105, No.1, pp.1139-1161. 10.1007/s11069-020-04324-x

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", International Journal of Civil Engineering, Vol.16, No.4, pp.435-441. 10.1007/s40999-017-0150-6

Bjerrum, L. (1963), "Allowable Settlement of Structures", the 3rd European Conference on Soil Mechanics and Foundation Engineering, Wiesbaden, Germany.

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", Applied Sciences, Vol.9, No.6, pp.1042. 10.3390/app9061042

Cherubini, C. (2000), "Probabilistic Approach to the Design of Anchored Sheet Pile Walls", Computers and Geotechnics, Vol.26, No.3, pp.309-330. 10.1016/S0266-352X(99)00044-0

Gang, B.J., Hwang, B.S., and Cho, W.J. (2018), "Empirical Estimations of Soil Constants Using Standard Penetration Test N Value", Journal of the Korean Geoenvironmental Society, Vol.19, No.6, pp.5-12.

Gao, Wei and He, T.Y. (2017), "Displacement Prediction in Geotechnical Engineering based on Evolutionary Neural Network", Geomechanics and engineering, Vol.13, No.5, pp.845-860.

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", Engineering with Computers, Vol.35, No.3, pp.945-954. 10.1007/s00366-018-0642-2

Kim, D. Y. and Song, M. K. (2022), "Inverse Estimation Method for Spatial Randomness of Material Properties and Its Application to Topology Optimization on Shape of Geotechnical Structures", Journal of Korean Geosynthetics Society, Vol.21, No.3, pp.1-10.

Kim, H. T., Park, S. W., Kwon, Y. H., and Kim, J. H. (2000), Development of a System Predicting Maximum Displacements of Earth Retaining Walls at Various Excavation Stages Using Artificial Neural Network. Journal of the korean geotechnical society, Vol.16, No.1, pp.83-97.

Korea Expressway Corporation (2010), "Highway Design Practical Data Book", Korea Expressway Corporation, Gimcheon

Lee, J. H., Kim, J. H., Park, M. C., and Song, Y. G. (2022), "Seoul Research Institute of Technology, a Study on the Introduction of Smart Measurement Technology for Excavation Construction Sites", Journal of the korean geotechnical society, Vol.38, No.6 pp.56-61.

Lee, J. K., Choi, J. S., Kim, T. H., and Geem, Z. W. (2022), "Calculation of Shear Strength of Rock Slope Using Deep Neural Network", Journal of the Korean Geosynthetics Society, Vol.21, No.2, pp.21-30.

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", Journal of Civil Structural Health Monitoring, Vol.6, No.1, pp.3-16. 10.1007/s13349-015-0108-9

Ma, J. H. and Yun, T. S. (2022), "Prediction of Slope Failure Arc Using Multilayer Perceptron", Journal of the Korean Geotechnical Society, Vol.38, No.8, pp.39-52.

Meyerhof, G. G. (1956), "Penetration Tests and Bearing Capacity of Cohesionless Soils", Journal of the Soil Mechanics and Foundations Division, Vol.82, No.1, pp.866-1. 10.1061/JSFEAQ.0000001

Ministry of Land, Infrastructure and Transport (2016), "Temporary Earth Retaining Design Criteria, KDS 21 30 00:2016".

Mohamed, T., Kasa, A., Rahma n, N. A., and Gaber, M. (2019), "PREDICTION OF EXTERNAL STABILITY FOR SEGMENTAL RETAINING WALL USING NEURAL NETWORK AND SUPPORT VECTOR MACHINE", Technology, Vol.10, No.2, pp.2491-2498.

Seo, S. H. and Chung, M. K. (2023), "Development of an Ensemble Prediction Model for Lateral Deformation of Retaining Wall Under Construction", Journal of the Korean Geotechnical Society, Vol.39, No.4, pp.5-17.

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", Journal of the Architectural Institute of Korea Structure & Construction, Vol.29, No.12, pp.21-28.

SIT (2020), "Introduction of Smart Measurement for Underground Retaining Construction and System Improvement Plan", SIT, Seoul.

Storn, R. and Price, K. (1997), "Differential Evolution-a Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces", Journal of Global Optimization, Vol.11, No.4, pp.341-359. 10.1023/A:1008202821328

Vesterstrom, J. and Thomsen, R. (2004), "A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems", In Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No.04TH8753) Vol.2, pp.1980-1987. 10.1109/CEC.2004.1331139

Woo, J. I. and Chung, D. S. (2020), "Numerical Analysis of Anchored In-situ Wall Using Back-Analysis Technique", Journal of the Society of Disaster Information, Vol.16, No.1, pp.1-9.

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", Geomechanics and Engineering, Vol.25, No.5, pp.395-403. 10.1016/j.amc.2021.126190

Yin, Z. Y., Jin, Y. F., Shen, J. S., and Hicher, P. Y. (2018), "Optimization Techniques for Identifying Soil Parameters in Geotechnical Engineering: Comparative Study and Enhancement", International Journal for Numerical and Analytical Methods in Geomechanics, Vol.42, No.1, pp.70-94. 10.1002/nag.2714

Zevgolis, I.E. and Bourdeau, P.L. (2010), "Probabilistic Analysis of Retaining Walls", Computers and Geotechnics, Vol.37, No.3, pp.359-373. 10.1016/j.compgeo.2009.12.003

Zhao, H., Chen, B., and Li, S. (2021), "Determination of Geomaterial Mechanical Parameters based on Back Analysis and Reduced-order Model", Computers and Geotechnics, Vol.132, pp.104013. 10.1016/j.compgeo.2021.104013

Information

Publisher :The Korean Geotechnical Society
Publisher(Ko) :한국지반공학회
Journal Title :Journal of the Korean Geotechnical Society
Journal Title(Ko) :한국지반공학회 논문집
Volume : 39
No :9
Pages :13-24
Received Date : 2023-07-04
Revised Date : 2023-07-14
Accepted Date : 2023-07-21
DOI :https://doi.org/10.7843/kgs.2023.39.9.13

Journal of the Korean Geotechnical Society ISSN:1229-2427(Print) 2288-646X(Online) 한국지반공학회 논문집

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