Development of an Ensemble Prediction Model for Lateral Deformation of Retaining Wall Under Construction

Seunghwan Seo; Moonkyung Chung

doi:10.7843/kgs.2023.39.4.5

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2023 Vol.39, Issue 4 Next Page

Development of an Ensemble Prediction Model for Lateral Deformation of Retaining Wall Under Construction 시공 중 흙막이 벽체 수평변위 예측을 위한 앙상블 모델 개발

30 April 2023. pp. 5-17

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Abstract

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

Keywords

Boosting

Ensemble

Excavation

Machine learning

Retaining wall

도심지 지하굴착 공사가 대형화되면서 공사 중 안전사고에 대한 위험요인이 더욱 증가하고 있다. 이에 따라 공사현장의 위험요소를 모니터링하고 사전에 예측할 수 있는 기술이 필요하다. 굴착으로 인한 흙막이 벽체의 변형을 예측하는 방법에는 크게 경험식과 수치해석 두 가지 방법으로 분류할 수 있으며, 최근에는 인공지능 기술의 발달과 함께 머신러닝 기법을 활용한 예측 모델이 한 가지 방법으로 자리 잡고 있다. 본 연구에서는 예측력과 효율성이 우수한 부스팅 계열 알고리즘 및 앙상블 모델을 이용하여 시공 중 흙막이 벽체 변형을 예측하는 모델을 구축하였다. 지하흙막이 공사의 설계-시공-유지관리 과정에서 도출되는 자료들을 복합적으로 활용하여 데이터베이스를 구축하고, 이 자료를 토대로 학습모델을 만들고 성능을 평가하였다. 모델 성능 평가 결과, 높은 정확도로 흙막이 벽체 변형을 예측할 수 있었으며, 지반계측 자료를 학습에 활용함으로써 실제 시공과정의 특성이 반영된 예측결과를 제시할 수 있었다. 본 연구에서 구축한 예측 모델을 활용하여 시공 중 흙막이 벽체의 안정성 평가 및 모니터링에 활용할 수 있을 것으로 기대된다.

References

Chen, T. and Guestrin, C. (2016), XGBoost: A Scalable Tree Boosting System, Proceedings of the KDD(Knowledge Discovery and Data Mining) '16, San Francisco, CA, USA, pp.1-10. 10.1145/2939672.2939785

Clough, G. W. and O'Rourke, T. D. (1990), Construction Induced Movements of in Situ Walls, Proceedings of the Specialty Conference on Design and Performance of Earth Retaining Structures, ASCE, Reston, VA, USA, pp.439-470.

Friedman, J. H. (2001), Greedy Function Approximation: A Gradient Boosting Machine, The Annals Statistics, Vol.29, No.5, pp.1189-1232. 10.1214/aos/1013203451

Goh, A. T. C., Wong, K. S., and Broms, B. B. (1995), Estimation of Lateral Wall Movements in Braced Excavations Using Neural Networks, Canadian Geotechnical Journal, Vol.32, No.6, pp.1059-1064. 10.1139/t95-103

Goh, A. T. C., Zhang, F., Zhang, W., Zhang, Y., and Liu, H. (2017), A Simple Estimation Model for 3D Braced Excavation Wall Deflection, Computers and Geotechnics, Vol.83, No.3, pp.106-113. 10.1016/j.compgeo.2016.10.022

Hwang, R. N., Lee, T. Y., and Chou, C. R. (2012), Evaluation of Performance of Diaphragm Walls by Wall Deflection Paths, Journal of Geoengineering, Vol.7, No.1, pp.1-12.

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. Y. (2017), LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Proceedings of the 31st Conference on Neural Information Processing System, Long Beach, CA, USA, pp.1-9.

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

Kung, G. T. C., Hsiao, E. C. L., Schuster, M., and Juang, C. H. (2007), A Neural Network Approach to Estimating Deflection of Diaphragm Walls Caused by Excavation in Clays, Computers and Geotechnics, Vol.34, No.5, pp.385-396. 10.1016/j.compgeo.2007.05.007

Lee, S. and Kim, S. K. (2008), A Study on Deformation Analysis of the Earth Retaining Wall, J. of the Korean Geotechnical Society, Vol.24, No.2, pp.27-36.

Long, M. (2001), Database for Retaining Wall and Ground Movements due to Deep Excavations, Journal of Geotechnical and Geoenvironmental Engineering, Vol.127, No.3, pp.203-224. 10.1061/(ASCE)1090-0241(2001)127:3(203)

Moormann, C. (2004), Analysis of Wall and Ground Movements due to Deep Excavations in Soft Soil based on a New Worldwide Database, Soils and Foundations, Vol.44, No.1, pp.87-98. 10.3208/sandf.44.87

Park, C. S. and Joung, S. M. (2020), Numerical Investigation on the Excavation Width and Property of Deformation of Earth Retaining Wall, J. of the Korean Geotechnical Society, Vol.36, No.12, pp.57-68.

Peck, R. B. (1969), Deep Excavations and Tunneling in Soft Ground, Proceedings of the 7th International Conference on Soil Mechanic and Foundation Engineering, Mexico City, Mexico, pp.225-290.

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2018), Catboost: Unbiased Boosting with Categorical Features, Proceedings of the 32nd Conference on Neural Information Processing System, Montreal, Canada, pp.1-11.

Ray, R., Kumar, D., Samui, P., Roy, L. B., Goh, A. T. C., and Zhang, W. (2021), Application of Soft Computing Techniques for Shallow Foundation Reliability in Geotechnical Engineering, Geoscience Frontiers, Vol.12, No.1, pp.375-383. 10.1016/j.gsf.2020.05.003

Zhang, W., Zhang, R., Wu C., Goh, A.T.C., Lacasse, S., Liu, Z., and Liu H. (2020), State-of-the-art Review of Soft Computing Application in Underground Excavations, Geoscience Frontiers, 11, pp.1095-1106. 10.1016/j.gsf.2019.12.003

Zhang, R., Wu, C., Goh, A. T. C., B¨ohlke, T., and Zhang, W. (2021), Estimation of Diaphragm Wall Deflections for Deep Braced Excavation in Anisotropic Clays Using Ensemble Learning, Geoscience Frontiers, Vol.12, No.1, pp.365-373. 10.1016/j.gsf.2020.03.003

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 :4
Pages :5-17
Received Date : 2023-02-27
Revised Date : 2023-03-18
Accepted Date : 2023-03-27
DOI :https://doi.org/10.7843/kgs.2023.39.4.5

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

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