Application of finite element modeling and deep learning for displacement estimation in deep excavations
Email:
sang.tothanh@uah.edu.vn
Từ khóa:
Deep Learning, Finite Element Method, Deep Excavation, Diaphragm Wall
Tóm tắt
Accurately predicting excavation-induced displacements remains a critical challenge in geotechnical engineering due to the inherent risks and complex soil-structure interactions during construction. To address this problem, this study proposes a hybrid predictive framework that integrates Finite Element Model (FEM) with Deep Neural Networks (DNN), called DNN-FEM. Initially, parametric simulations are conducted using commercial software in geotechnics, PLAXIS 2D, to simulate various deep excavation scenarios and generate a robust numerical dataset. This dataset is subsequently partitioned, allocating 80% of the data to train the DNN architecture and the remaining 20% to evaluate its predictive performance. The results demonstrate a high degree of agreement between the DNN-predicted displacements and the FEM-calculated values across both the training and independent test sets, as evidenced by R^2coefficients exceeding 0.99. Ultimately, this research demonstrates that the proposed DNN-FEM approach provides a highly accurate and computationally efficient tool for estimating Diaphragm Wall (DW) and soil displacements in deep excavation projectsTài liệu tham khảo
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[2]. T. Do, C. Ou and R. Chen, A study of failure mechanisms of deep excavations in soft clay using the finite element method, Computers and Geotechnics, 73 (2016) 153-163. https://doi.org/10.1016/j.compgeo.2015.12.009
[3]. Y. Dong, H. Burd and G. Houlsby, Finite-element analysis of a deep excavation case history, Géotechnique, 66 (2016), 1-15. https://doi.org/10.1680/jgeot.14.P.234
[4]. H. Tra, Q. Huynh and T. To, Interaction between retaining wall and structural piles in soft soil-deep excavation, World Journal of Engineering, (2025) 1-13. https://doi.org/10.1108/WJE-03-2025-0162
[5]. T. S. To, Q. Thien-Huynh, L. Bui and T. Cuong-Le, A Calibrated Framework Integrating Three-Dimensional Numerical Modeling with an Optimization Algorithm for the Estimation of Soil–Pile Interaction, Transportation Infrastructure Geotechnology, 13 (2026) 36. https://doi.org/10.1007/s40515-025-00783-6
[6]. H. Minh, T. Sang-To, B. Le-Van and T. Cuong-Le, A novelty solution for orthotropic composite plate based on physics informed neural network, ENGINEERING Structure and Civil Engineering, 19 (2025) 718-741. https://doi.org/10.1007/s11709-025-1178-3
[7]. T. To, H. Minh, T. Huynh, S. Khatir, M. Wahab and T. Cuong‐Le, A nonlinear optimization method for calibration of large‐scale deep cement mixing in very soft clay deep excavation, International Journal for Numerical and Analytical Methods in Geomechanics, 48 (2024) 1949-1978. https://doi.org/10.1002/nag.3714
[8]. Minh H. L., Sang-To T., Le-Van B., Khatir S. and Cuong-Le T, Improved Atom Search Optimization (ASO) for Crack Length Prediction in Steel Beams. Physical Mesomechanics, 28 (2025) 686-712. https://doi.org/10.1134/S102995992460188X
[9]. Minh H. L., Sang-To T., Khatir S., Wahab M. A., Gandomi A. H. and Cuong-Le T, Augmented deep neural network architecture for assessing damage severity in 3D concrete buildings under temperature fluctuations based on K-means optimization, Structures, 57 (2023) 105278. https://doi.org/10.1016/j.istruc.2023.105278
[10]. Dang T. X., Nguyen P. T., Nguyen T. A. and Tran H. V. V., Optimization of Barrette Wall Depths for Urban Excavation Stability Using FEM and ANOVA Testing, Civil Engineering and Architecture, 12 (2024) 3530-3544, https://doi.org/10.13189/cea.2024.120529
[11]. Thanh Sang-To, Minh Hoang-Le, Samir Khatir, Seyedali Mirjalili, Magd Abdel Wahab, Thanh Cuong-Le, Forecasting of excavation problems for high-rise building in Vietnam using planet optimization algorithm. Scientific reports, 11 (2021) 23809. https://doi.org/10.1038/s41598-021-03097-y
[12]. X. Wu, Z. Feng, J. Liu, H. Chen and Y. Liu, Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning, Automation in Construction, 165 (2024) 105516. https://doi.org/10.1016/j.autcon.2024.105516
[13]. F. Lai, S. Liu, J. Shiau, M. Liu, G. Cai and M. Huang, Data-driven modeling for evaluating deformation of a deep excavation near existing tunnels, Underground Space, (2025). https://doi.org/10.1016/j.undsp.2025.04.003
[14]. Hoang-Le Minh, Thanh Sang-To, Binh Le-Van, Thanh Cuong-Le, Optimization of high-rise concrete structure using a sequence design based on a proposed surrogate model (PSM) and finite element (FE) model updating, Archives of Civil and Mechanical Engineering, 25 (2025) 243. https://doi.org/10.1007/s43452-025-01296-2
[15]. bentley, PLAXIS 2D - Tutorial Manual, bentleysystems, (2024).
[16]. To T. S, Nguyen K. C, Nguyen Q. T, and Livaoğlu, R, A New Optimization Machine Learning Technique for Fundamental Frequency Prediction of Historic Masonry Towers Using Planet Optimization Algorithm, International Journal of Architectural Heritage, (2026) 1-24. https://doi.org/10.1080/15583058.2026.2662388
[17]. Anh T. T. P, Hayano K, and Mochizuki Y, Application of artificial neural network models for mixture design of surplus soils treated with paper sludge ash-based stabilizer, Transportation Geotechnics, 46 (2024) 101247. https://doi.org/10.1016/j.trgeo.2024.101247
[18]. TTP Anh, K Hayano and BA Thang, Image-based prediction of particle size distribution in recycled stabilized soils using convolutional neural networks, International Journal of Geo-Engineering, 17 (2026). https://doi.org/10.1186/s40703-026-00263-x
[19]. Gordon T.C. Kung, Evan C.L. Hsiao, Matt Schuster and C. Hsein Juang, A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays, Computers and Geotechnics, 34 (2007) 385-396. https://doi.org/10.1016/j.compgeo.2007.05.007
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
10/02/2026
Nhận bài sửa
21/05/2026
Chấp nhận đăng
10/06/2026
Xuất bản
15/09/2026
Chuyên mục
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Kiểu trích dẫn
Thanh Sang, T., & Tran Kien, T. (1789405200). Application of finite element modeling and deep learning for displacement estimation in deep excavations. Tạp Chí Khoa Học Giao Thông Vận Tải, 77(7), 955-964. https://doi.org/10.47869/tcsj.77.7.6
