A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Abstract

The study proposes a framework combining machine learning (ML) models into a logical hierarchical system which evaluates the stability of the sheet wall before other predictions. The study uses the hardening soil (HS) model to develop a 200-sample finite element analysis (FEA) database, to develop the ML models. Consequently, a system containing three trained ML models is proposed to first predict the stability status (random forest classification, RFC) followed by 1) the cantilever top horizontal displacement of sheet wall (artificial neural network regression models, RANN1) and 2) vertical settlement of soil (RANN2). The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models, based on the application of random sampling technique in the data splitting process. Investigation on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000 value. This implies a high performance of RFC on the database with medians of accuracy, precision, and recall, on test set are 1.0000, 1.0000, and 0.92857, respectively. The regression ANN models have coefficient of determinations on test set, as high as 0.9521 for RANN1, and 0.9988 for RANN2, respectively. The parametric study for these regressions is also provided to evaluate the relative insight influence of inputs to output.

Graphical abstract

Keywords

finite element analysis / cantilever sheet wall / machine learning / artificial neural network / random forest

References

[1] KwonJ. Investigation of the influence of an excavation on adjacent excavations, using neural networks. Journal of the Southern African Institute of Mining and Metallurgy, 1998, 98( 3): 147– 156

[2] RamadanM I, RamadanE H, KhashilaM M. Cantilever contiguous pile wall for supporting excavation in clay. Geotechnical and Geological Engineering, 2018, 36( 3): 1545– 1558

[3] PoulosH, ChenL. Pile response due to unsupported excavation-induced lateral soil movement. Canadian Geotechnical Journal, 1996, 33( 4): 670– 677

[4] BransbyP, MilliganG. Soil deformations near cantilever sheet pile walls. Geotechnique, 1975, 25( 2): 175– 195

[5] SinghA P, ChatterjeeK. Ground settlement and deflection response of cantilever sheet pile wall subjected to surcharge loading. Indian Geotechnical Journal, 2020, 50( 4): 540– 549

[6] Es-haghiM S, AbbaspourM, RabczukT. Factors and failure patterns analysis for undrained seismic bearing capacity of strip footing above void. International Journal of Geomechanics, 2021, 21( 10): 04021188

[7] PhanH C, DharA S. Predicting pipeline burst pressures with machine learning models. International Journal of Pressure Vessels and Piping, 2021, 191 : 104384

[8] AnitescuC, AtroshchenkoE, AlajlanN, RabczukT. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials and Continua, 2019, 59( 1): 345– 359

[9] SamaniegoE, AnitescuC, GoswamiS, Nguyen-ThanhV M, GuoH, Hamdia K, ZhuangX, RabczukT. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362 : 112790

[10] VermaA K, SinghT N, ChauhanN K, SarkarK. A hybrid FEM–ANN approach for slope instability prediction. Journal of The Institution of Engineers (India): Series A, 2016, 97( 3): 171– 180

[11] PengC, WuW, Zhang B. Three-dimensional simulations of tensile cracks in geomaterials by coupling meshless and finite element method. International Journal for Numerical and Analytical Methods in Geomechanics, 2015, 39( 2): 135– 154

[12] ChakrabortyA, GoswamiD. Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences, 2017, 10( 17): 385

[13] ChernS TsaiJ H ChienL K HuangC Y. Predicting lateral wall deflection in top-down excavation by neural network. International Journal of Offshore and Polar Engineering, 2009, 19(2): 151− 157

[14] MoayediH, MosallanezhadM, RashidA S A, JusohW A W, MuazuM A. A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: Theory and applications. Neural Computing & Applications, 2020, 32( 2): 495– 518

[15] Es-haghiM S SarcheshmehpourM. A novel strategy for tall building optimization via combination of asymmetric genetic algorithm and machine learning methods. In: The 1st Online Conference on Algorithms. MDPI, 2021

[16] DuongH T PhanH C LeT T BuiN D. Optimization design of rectangular concrete-filled steel tube short columns with Balancing Composite Motion Optimization and data-driven model. Structures, 2020, 28: 757– 765

[17] PhanH C, Le-ThanhL, Nguyen-XuanH. A semi-empirical approach and uncertainty analysis to pipes under hydrogen embrittlement degradation. International Journal of Hydrogen Energy, 2022, 47( 8): 5677– 5691

[18] PhanH C, BuiN D. Failure assessment of defected pipe under strike-slip fault with data-driven models accounting to the model uncertainty. Neural Computing & Applications, 2021, 34 : 1541– 1555

[19] Attoh-OkineN, FekpeE S. Strength characteristics modeling of lateritic soils using adaptive neural networks. Construction & Building Materials, 1996, 10( 8): 577– 582

[20] PalaM, CaglarN, ElmasM, CevikA, SaribiyikM. Dynamic soil–structure interaction analysis of buildings by neural networks. Construction & Building Materials, 2008, 22( 3): 330– 342

[21] NazzalM D, TatariO. Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus. International Journal of Pavement Engineering, 2013, 14( 4): 364– 373

[22] GroholskiD R, HashashY M. Development of an inverse analysis framework for extracting dynamic soil behavior and pore pressure response from downhole array measurements. International Journal for Numerical and Analytical Methods in Geomechanics, 2013, 37( 12): 1867– 1890

[23] ChanW, ChowY, LiuL. Neural network: An alternative to pile driving formulas. Computers and Geotechnics, 1995, 17( 2): 135– 156

[24] NazirR, MoayediH, PratiksoA, MosallanezhadM. The uplift load capacity of an enlarged base pier embedded in dry sand. Arabian Journal of Geosciences, 2015, 8( 9): 7285– 7296

[25] IsmailA, JengD S. Modelling load–settlement behaviour of piles using high-order neural network (HON-PILE model). Engineering Applications of Artificial Intelligence, 2011, 24( 5): 813– 821

[26] SamuiP, SitharamT. Site characterization model using artificial neural network and kriging. International Journal of Geomechanics, 2010, 10( 5): 171– 180

[27] YilmazO, EserM, BerilgenM. Applications of engineering seismology for site characterization. Journal of Earth Science, 2009, 20( 3): 546– 554

[28] CaoZ, Wang Y, LiD. Quantification of prior knowledge in geotechnical site characterization. Engineering Geology, 2016, 203 : 107– 116

[29] DwivediV K, DubeyR K, ThockhomS, PancholiV, ChopraS, RastogiB K. Assessment of liquefaction potential of soil in Ahmedabad Region, Western India. Journal of Indian Geophysical Union, 2017, 21( 2): 116– 123

[30] Hsein JuangC, ChenC J, TienY M. Appraising cone penetration test based liquefaction resistance evaluation methods: Artificial neural network approach. Canadian Geotechnical Journal, 1999, 36( 3): 443– 454

[31] HannaA M, UralD, SaygiliG. Evaluation of liquefaction potential of soil deposits using artificial neural networks. Engineering Computations, 2007, 24( 1): 5– 16

[32] LiuZ, Shao J, XuW, ChenH, ZhangY. An extreme learning machine approach for slope stability evaluation and prediction. Natural Hazards, 2014, 73( 2): 787– 804

[33] GordanB, Jahed ArmaghaniD, HajihassaniM, MonjeziM. Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers, 2016, 32( 1): 85– 97

[34] LiA, Khoo S, LyaminA V, WangY. Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm. Automation in Construction, 2016, 65 : 42– 50

[35] IliaI, KoumantakisI, RozosD, KoukisG, TsangaratosP. A geographical information system (GIS) based probabilistic certainty factor approach in assessing landslide susceptibility: The case study of Kimi, Euboea, Greece. In: IAEG XII Congress: Engineering Geology for Society and Territory. Turin: Springer, 2015, 1199– 1204

[36] SouzaF, EbeckenN. A data mining approach to landslide prediction. WIT Transactions on Information and Communication Technologies, 2004, 33

[37] MelchiorreC, MatteucciM, AzzoniA, ZanchiA. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology, 2008, 94( 3-4): 379– 400

[38] HuangF K WangG S. ANN-based reliability analysis for deep excavation. In: EUROCON 2007—The International Conference on “Computer as a Tool”. Warsaw: IEEE, 2007

[39] GohA T, WongK, BromsB. Estimation of lateral wall movements in braced excavations using neural networks. Canadian Geotechnical Journal, 1995, 32( 6): 1059– 1064

[40] JanJ, Hung S L, ChiS Y, ChernJ C. Neural network forecast model in deep excavation. Journal of Computing in Civil Engineering, 2002, 16( 1): 59– 65

[41] JunY Haiming C. Artificial neural network’s application in intelligent prediction of surface settlement induced by foundation pit excavation. In: 2009 Second International Conference on Intelligent Computation Technology and Automation. Zhangjiajie: IEEE, 2009

[42] KoyC, Yune C Y. Numerical analysis on consolidation of soft clay by sand drain with heat injection. Journal of the Korean Geotechnical Society, 2017, 33( 11): 45– 57

[43] ZhouJ, ShiX, Du K, QiuX, LiX, Mitri H S. Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. International Journal of Geomechanics, 2017, 17( 6): 04016129

[44] NikbakhtS, AnitescuC, RabczukT. Optimizing the neural network hyperparameters utilizing genetic algorithm. Journal of Zhejiang University, Science A, 2021, 22( 6): 407– 426

[45] ZhangW, ZhangR, WuC, Goh A T C, Lacasse S, LiuZ, LiuH. State-of-the-art review of soft computing applications in underground excavations. Geoscience Frontiers, 2020, 11( 4): 1095– 1106

[46] SchanzT, VermeerP A, BonnierP G. Beyond 2000 in Computational Geotechnics. London: Routledge, 1999, 281– 296

[47] OuC Y, LaiC H. Finite-element analysis of deep excavation in layered sandy and clayey soil deposits. Canadian Geotechnical Journal, 1994, 31( 2): 204– 214

[48] MansourM, RashedA, FaragA. Adopting numerical models for prediction of ground movements induced by deep excavation. International Journal of Recent Technology and Engineering, 2020, 8( 6): 976– 988

[49] BrinkgreveR B J SwolfsW M EnginE WatermanD ChesaruA BonnierP GalaviV. PLAXIS 2D Reference Manual. Delft: Delft University of Technology and PLAXIS bv, 2011

[50] LeT T, PhanH C. Prediction of ultimate load of rectangular CFST columns using interpretable machine learning method. Advances in Civil Engineering, 2020, 2020 : 8855069

[51] LeT T. Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method. Journal of Composite Materials, 2021, 55( 6): 787– 811

[52] BreimanL BreimanL FriedmanJ H OlshenR A StoneC J. Classification and Regression Trees. Boca Raton: Chapman & Hall, 1984

[53] GéronA. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Sebastopol: O’Reilly Media, 2019

[54] PhamT D, BuiN D, NguyenT T, PhanH C. Predicting the reduction of embankment pressure on the surface of the soft ground reinforced by sand drain with random forest regression. IOP Conference Series: Materials Science and Engineering, 2020, 869( 7): 072027

[55] McCullochW S, PittsW. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 1943, 5( 4): 115– 133

[56] LeCunY, BengioY. Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, 1995, 3361( 10): 1– 14

[57] Carreira-PerpinanM A, HintonG E. On contrastive divergence learning. In: The Tenth International Workshop on Artificial Intelligence and Statistics. Barbados: PMLR, 2005, 33– 40

[58] HintonG E, OsinderoS, TehY W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18( 7): 1527– 1554

[59] RanzatoM A HuangF J BoureauY L LeCunY. Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE, 2007

[60] ChogueurA, AbdeldjalilZ, ReiffsteckP. Parametric and comparative study of a flexible retaining wall. Periodica Polytechnica. Civil Engineering, 2018, 62( 2): 295– 307

[61] ShahinM A, JaksaM B, MaierH R. Artificial neural network applications in geotechnical engineering. Australian Geomechanics, 2001, 36( 1): 49– 62

[62] PhanH C, LeT T, BuiN D, DuongH T, PhamT D. An empirical model for bending capacity of defected pipe combined with axial load. International Journal of Pressure Vessels and Piping, 2021, 191 : 104368

[63] PhanH C, DuongH T. Predicting burst pressure of defected pipeline with principal component analysis and adaptive neuro fuzzy inference system. International Journal of Pressure Vessels and Piping, 2021, 189 : 104274

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