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Deep Neural Network-based Inverse Analysis with Application to a Rockfill Dam

  • Geotechnical Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

The availability of significant computational resources has played an essential role in developing advanced numerical models for the design and safety evaluation of complex structures such as rockfill dams. Determining the geomechanical parameters is deemed a crucial but challenging task for effective modelling. The general approach involves using in situ or laboratory tests or experimental relationships from the literature to assess these parameters. These measures, however, do not accurately reflect the actual situation. This paper proposes a data driven approach using deep neural networks and non-deterministic optimization algorithms to identify the soil parameters that will lead to displacements that best approximate the measured data. The methodology is applied to a rockfill dam recently built in Quebec, for which some measurements of inclinometer displacements are available. A finite element model (FEM) of two dimensions generates numerical solutions. A comparative study is performed to account for the heterogeneity of the materials by decomposing the computational domain into subdomains. Subsequently, the inverse analysis uses the surrogate model instead of the full FEM model for rapid computations. A suitable objective function is defined to account for large oscillations in the measurement data. Non-intrusive stochastic optimization algorithms such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Differential evolution (DE) are evaluated for the minimization problem. Finally, the case study confirms the capability of the proposed methodology to identify the relevant dam parameters, provide some insights into the performance, and compare three optimization algorithms.

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References

  • Akbari Hamed A (2017) Predictive numerical modeling of the behavior of rockfill dams. MS Thesis, École de Technologie Supérieure, Canada

    Google Scholar 

  • An J-S, Kang K-N, Kim S-H, Song K-I (2019) Analysis for applicability of differential evolution algorithm to geotechnical engineering field. Journal of the Korean Geotechnical Society 35(4):27–35, DOI: https://doi.org/10.7843/kgs.2019.35.4.27

    Google Scholar 

  • Arora RK (2015) Optimization: Algorithms and applications. CRC Press

  • Asteris PG, Rizal FIM, Koopialipoor M, Roussis PC, Ferentinou M, Armaghani DJ, Gordan B (2022) Slope stability classification under seismic conditions using several tree-based intelligent techniques. Applied Sciences 12(3):1753, DOI: https://doi.org/10.3390/app12031753

    Article  Google Scholar 

  • Asthana B, Khare D (2022) Recent advances in dam engineering. Springer Nature, SN

  • Bao T, Li J, Lu Y, Gu C (2020) IDE-MLSSVR-based back analysis method for multiple mechanical parameters of concrete dams. Journal of Structural Engineering 146(8):04020155, DOI: https://doi.org/10.1061/(ASCE)ST.1943-541X.0002602

    Article  Google Scholar 

  • Beale M, Hagan M, Demuth H (2019) MATLAB Deep learning toolbox users guide: PDF documentation for release R2019a. The MathWorks, Inc

  • Bishop CM (2006) Pattern recognition and machine learning. Springer

  • Bolzon G, Buljak V (2011) An effective computational tool for parametric studies and identification problems in materials mechanics. Computational Mechanics 48(6):675–687, DOI: https://doi.org/10.1007/s00466-011-0611-8

    Article  MATH  Google Scholar 

  • Boumezerane D (2022) Recent tendencies in the use of optimization techniques in geotechnics: A review. Geotechnics 2(1):114–132

    Article  Google Scholar 

  • Carbonari S, Dezi F, Arezzo D, Gara F (2022) A methodology for the identification of physical parameters of soil-foundation-bridge pier systems from identified state-space models. Engineering Structures 255:113944

    Article  Google Scholar 

  • Coello CAC, Lamont GB, Van Veldhuizen DA, Others (2007) Evolutionary algorithms for solving multi-objective problems. Springer

  • Darwin C (2007) Theory of evolution. C. King & S. Scott, Trans. Encarta-Microsoft Student

  • Das R, Soulaimani A (2021) Non-deterministic methods and surrogates in the design of rockfill dams. Applied Sciences 11(8):3699, DOI: https://doi.org/10.3390/app11083699

    Article  Google Scholar 

  • Deng-gang W, Ying-xi L, Shou-ju L (2000) Genetic algorithms for inverse analysis of displacements in geotechnical engineering. Chinese Journal of Rock Mechanics and Engineering 19(2):979–982

    Google Scholar 

  • Dige N, Diwekar U (2018) Efficient sampling algorithm for large-scale optimization under uncertainty problems. Computers & Chemical Engineering 115:431–454, DOI: https://doi.org/10.1016/j.compchemeng.2018.05.007

    Article  Google Scholar 

  • Dou S, Li J, Kang F (2017) Parameter identification of concrete dams using swarm intelligence algorithm. Engineering Computations 34(7):2358–2378, DOI: https://doi.org/10.1108/EC-03-2017-0110

    Article  Google Scholar 

  • Dou S, Li J, Kang F (2019) Health diagnosis of concrete dams using hybrid FWA with RBF-based surrogate model. Water Science and Engineering 12(3):188–195

    Article  Google Scholar 

  • Eltaeib T, Mahmood A (2018) Differential evolution: A survey and analysis. Applied Sciences 8(10):1945, DOI: https://doi.org/10.3390/app8101945

    Article  Google Scholar 

  • Eykhoff P (1974) System identification. Parameter and state estimation, John Wiley and sons, Chichester, United Kingdom

    MATH  Google Scholar 

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press

  • Gunantara N (2018) A review of multi-objective optimization: Methods and its applications. Cogent Engineering 5(1):1502242, DOI: https://doi.org/10.1080/23311916.2018.1502242

    Article  Google Scholar 

  • Hashash YM, Levasseur S, Osouli A, Finno R, Malecot Y (2010) Comparison of two inverse analysis techniques for learning deep excavation response. Computers and Geotechnics 37(3):323–333, DOI: https://doi.org/10.1016/j.compgeo.2009.11.005

    Article  Google Scholar 

  • He X, Wang F, Li W, Sheng D (2022) Deep learning for efficient stochastic analysis with spatial variability. Acta Geotechnica 17(4):1031–1051

    Article  Google Scholar 

  • He B, Armaghani DJ, Lai SH (2023) Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach. Tunnelling and Underground Space Technology 133:104979, DOI: https://doi.org/10.1016/j.tust.2022.104979

    Article  Google Scholar 

  • Hokes F, Kral P, Krnavek O, Husek M (2017) Improved sensitivity analysis in the inverse identification of the parameters of a nonlinear material model. Procedia Engineering 172:347–354, DOI: https://doi.org/10.1016/j.proeng.2017.02.039

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press

  • Hsieh WW (2009) Machine learning methods in the environmental sciences: Neural networks and kernels. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Jacquier P, Abdedou A, Delmas V, Soulaimani A (2020) Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition. Application to Flood Modeling: arXiv:2005.13506 [physics]

  • Jia Y, Chi S (2015) Back-analysis of soil parameters of the Malutang II concrete face rockfill dam using parallel mutation particle swarm optimization. Computers and Geotechnics 65:87–96, DOI: https://doi.org/10.1016/j.compgeo.2014.11.013

    Article  Google Scholar 

  • Kang F, Liu X, Li J, Li H (2022) Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model. Engineering Structures 256:113999, DOI: https://doi.org/10.1016/j.engstruct.2022.113999

    Article  Google Scholar 

  • Kang F, Wu Y, Li J, Li H (2021) Dynamic parameter inverse analysis of concrete dams based on Jaya algorithm with Gaussian processes surrogate model. Advanced Engineering Informatics 49:101348

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks 1942–1948, 4

  • Kim S, Finno RJ (2019) Inverse analysis of a supported excavation in Chicago. Journal of Geotechnical and Geoenvironmental Engineering 145(9):04019050, DOI: https://doi.org/10.1061/(ASCE)GT.1943-5606.0002120

    Article  Google Scholar 

  • Kim T, Jung Y-H (2022) Optimizing material parameters to best capture deformation responses in supported bottom-up excavation: Field monitoring and inverse analysis. KSCE Journal of Civil Engineering 26(8):3384–3401, DOI: https://doi.org/10.1007/s12205-022-1582-3

    Article  Google Scholar 

  • Labuz JF, Zang A (2012) Mohr-Coulomb failure criterion. The ISRM Suggested Methods for Rock Characterization, Testing and Monitoring: 2007–2014, Springer, 227–231

  • Levasseur S, Malecot Y, Boulon M, Flavigny E (2010) Statistical inverse analysis based on genetic algorithm and principal component analysis: Applications to excavation problems and pressuremeter tests. International Journal for Numerical and Analytical Methods in Geomechanics 34(5):471–491

    Article  MATH  Google Scholar 

  • Li Y, Hariri-Ardebili MA, Deng T, Wei Q, Cao M (2023) A surrogate-assisted stochastic optimization inversion algorithm: Parameter identification of dams. Advanced Engineering Informatics 55:101853, DOI: https://doi.org/10.1016/j.aei.2022.101853

    Article  Google Scholar 

  • Lin C, Li T, Chen S, Lin C, Liu X, Gao L, Sheng T (2020) Structural identification in long-term deformation characteristic of dam foundation using meta-heuristic optimization techniques. Advances in Engineering Software 148:102870, DOI: https://doi.org/10.1016/j.advengsoft.2020.102870

    Article  Google Scholar 

  • Lin S, Zheng H, Han B, Li Y, Han C, Li W (2022) Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. Acta Geotechnica 17(4): 1477–1502

    Article  Google Scholar 

  • Mathworks T (2007) MATLAB optimization toolbox user’s guide. Math Work

  • Murata T, Ishibuchi H (1995) MOGA: Multi-objective genetic algorithms. IEEE International Conference on Evolutionary Computation 289–294

  • Nocedal J, Wright S (2006) Numerical optimization. Springer Science

  • Phoon K-K, Zhang W (2022) Future of machine learning in geotechnics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 1–16

  • Pietruszczak S (2010) Fundamentals of plasticity in geomechanics. Crc Press Boca Raton, FL

    MATH  Google Scholar 

  • Plaxis B (2017) PLAXIS Reference manual. Delft University of Technology & Plaxis BV, The Netherlands

    Google Scholar 

  • Salazar F, Hariri-Ardebili MA (2022) Coupling machine learning and stochastic finite element to evaluate heterogeneous concrete infrastructure. Engineering Structures 260:114190

    Article  Google Scholar 

  • Shahriari M, Pardo D, Moser B, Sobieczky F (2020) A deep neural network as surrogate model for forward simulation of borehole resistivity measurements. Procedia Manufacturing 42:235–238, DOI: https://doi.org/10.1016/j.promfg.2020.02.075

    Article  Google Scholar 

  • Shahzadi G, Soulaïmani A (2021) Deep neural network and polynomial chaos expansion-based surrogate models for sensitivity and uncertainty propagation: An application to a rockfill dam. Water 13(13):1830, DOI: https://doi.org/10.3390/w13131830

    Article  Google Scholar 

  • Shan F, He X, Jahed Armaghani D, Zhang P, Sheng D (2022) Success and challenges in predicting TBM penetration rate using recurrent neural networks. Tunnelling and Underground Space Technology 130:104728, DOI: https://doi.org/10.1016/j.tust.2022.104728

    Article  Google Scholar 

  • Shan F, He X, Xu H, Armaghani DJ, Sheng D (2023) Applications of machine learning in mechanised tunnel construction: A systematic Review. Eng 4(2):1516–1535, DOI: https://doi.org/10.3390/eng4020087

    Article  Google Scholar 

  • Shang L, Nguyen H, Bui X-N, Vu TH, Costache R, Hanh LTM (2022) Toward state-of-the-art techniques in predicting and controlling slope stability in open-pit mines based on limit equilibrium analysis, radial basis function neural network, and brainstorm optimization. Acta Geotechnica 17(4):1295–1314

    Article  Google Scholar 

  • Sharifzadeh M, Tarifard A, Moridi MA (2013) Time-dependent behavior of tunnel lining in weak rock mass based on displacement back analysis method. Tunnelling and Underground Space Technology 38:348–356

    Article  Google Scholar 

  • Skentou AD, Bardhan A, Mamou A, Lemonis ME, Kumar G, Samui P, Armaghani DJ, Asteris PG (2023) Closed-form equation for estimating unconfined compressive strength of granite from three non-destructive tests using soft computing models. Rock Mechanics and Rock Engineering 56(1):487–514, DOI: https://doi.org/10.1007/s00603-022-03046-9

    Article  Google Scholar 

  • Smith M (2015) Rockfill settlement measurement and modelling of the Romaine-2 dam during construction. International Commission on Large Dams; Proceedings of the 25th International Congress, Stavanger

  • Sobol IM (1993) Sensitivity analysis for non-linear mathematical models. Mathematical Modelling and Computational Experiment 1:407–414

    MATH  Google Scholar 

  • Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4):341–359, DOI: https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  • Su H, Li J, Wu Z (2007) Feedback analysis for mechanical parameters of dam and its foundation with optimization algorithm. Journal of Hydraulic Engineering 38:129–134

    Google Scholar 

  • Vahdati P (2014) Identification of soil parameters in an embankment dam by mathematical optimization. Luleå

  • Vahdati P, Levasseur S, Mattsson H, Knutsson S (2013) Inverse Mohr-Coulomb soil parameter identification of an earth and rockfill dam by genetic algorithm optimization. The Electronic Journal of Geotechnical Engineering 18(X):5419–5440

    Google Scholar 

  • Vannobel P, Smith M, Lefebvre G, Karray M, Éthier Y (2013) Control of rockfill placement for the romaine-2 asphaltic core dam in northern Quebec. Canadian Geotechnical Journal, https://espace2.etsmtl.ca/id/eprint/8918

  • Yagiz S, Yazitova A, Karahan H (2020) Application of differential evolution algorithm and comparing its performance with literature to predict rock brittleness for excavatability. International Journal of Mining, Reclamation and Environment, 1–14

  • Yang L, Li Z (2019) Inverse analysis of rock creep model parameters based on improved simulated annealing differential evolution algorithm. Geotechnical and Geological Engineering 37(2):639–649, DOI: https://doi.org/10.1007/s10706-018-0634-4

    Article  Google Scholar 

  • Yin Z-Y, Jin Y-F, Shen JS, 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 42(1):70–94

    Article  Google Scholar 

  • Zhang W, Gu X, Tang L, Yin Y, Liu D, Zhang Y (2022) Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge. Gondwana Research

  • Zhang W, Li H, Li Y, Liu H, Chen Y, Ding X (2021) Application of deep learning algorithms in geotechnical engineering: A short critical review. Artificial Intelligence Review 54(8): 5633–5673

    Article  Google Scholar 

  • Zhang W, Liu Z (2022) Editorial for machine learning in geotechnics. Acta Geotechnica 1–1

  • Zhao H, Yin S (2016) Inverse analysis of geomechanical parameters by the artificial bee colony algorithm and multi-output support vector machine. Inverse Problems in Science and Engineering 24(7):1266–1281, DOI: https://doi.org/10.1080/17415977.2016.1178257

    Article  MathSciNet  Google Scholar 

  • Zhou J, Li E, Wei H, Li C, Qiao Q, Armaghani DJ (2019) Random forests and cubist algorithms for predicting shear strengths of rockfill materials. Applied Sciences 9(8):1621, DOI: https://doi.org/10.3390/app9081621

    Article  Google Scholar 

  • Zhou W, Li S, Ma G, Chang X, Ma X, Zhang C (2016) Parameters inversion of high central core rockfill dams based on a novel genetic algorithm. Science China Technological Sciences 59(5):783–794, DOI: https://doi.org/10.1007/s11431-016-6017-2

    Article  Google Scholar 

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Acknowledgments

Hydro Quebec and the Natural Sciences and Engineering Research Council of Canada supported research for this project.

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Authors and Affiliations

  1. Dept. of Mechanical Engineering, Ecole de Technologie Superieure, Montreal, H3C 1K3, QC, Canada

    Gullnaz Shahzadi & Azzeddine Soulaimani

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  1. Gullnaz Shahzadi
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  2. Azzeddine Soulaimani
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Correspondence to Azzeddine Soulaimani.

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Shahzadi, G., Soulaimani, A. Deep Neural Network-based Inverse Analysis with Application to a Rockfill Dam. KSCE J Civ Eng 28, 155–168 (2024). https://doi.org/10.1007/s12205-023-0355-y

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  • DOI: https://doi.org/10.1007/s12205-023-0355-y

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