Skip to main content
SpringerLink
Log in

The Prediction of Concrete Dam Displacement Using Copula-PSO-ANFIS Hybrid Model

  • Research Article-Civil Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The concrete dam displacement monitoring model is an essential part of dam health. Due to the complicated nonlinear mapping relationship between concrete dam displacement and various environmental quantities, as well as conventional statistical models, neural networks, and machine learning methods fail to consider each input's fuzzy uncertainty factors. Therefore, the model's prediction accuracy is usually affected by selecting impact factors and modeling methods. This paper uses the Copula theory to perform nonlinear correlation tests on displacement influencing factors for the above problems. Furthermore, on this basis, this paper proposes a hybrid model, which uses an adaptive neuro-fuzzy inference system (ANFIS) to establish a regression model and uses the particle swarm optimization (PSO) algorithm to find the optimal parameters of the model. This paper takes a roller-compacted concrete gravity dam as an example. It explores the effect of two clustering methods (subtractive clustering and fuzzy C-means clustering) on the ANFIS model's performance based on the dam's measured data. The results show that the MAPE in the subtractive clustering is about 26% less than the fuzzy C-means clustering in the test stage. Finally, this paper compares the prediction results of the Copula-ANFIS-PSO model with the other five conventional methods. The analysis of the results of six models with four error indicators shows that the error in the Copula-ANFIS-PSO model is about 46% less than other models. It provides a new method for concrete dam displacement monitoring.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Chen, S.H.: Hydraulic Structures. CRC Press, London (2015) https://doi.org/10.1007/978-3-662-47331-3

    Book  Google Scholar 

  2. Akpinar, U.; Binici, B.; Arici, Y.: Earthquake stresses and effective damping in concrete gravity dams. Earthq. Struct. 6(3), 251–266 (2014). https://doi.org/10.12989/eas.2014.6.3.251

    Article  Google Scholar 

  3. Mehdi, H.J.; Abolghasem, A.: Application of Froude dynamic similitude in anaerobic baffled reactors to prediction of hydrodynamic characteristics of a prototype reactor using a model reactor. Water Sci. Eng. (2017). https://doi.org/10.1016/j.wse.2017.03.002

    Article  Google Scholar 

  4. Zheng, D.J.; Li, X.Q.; Yang, M.; Su, H.Z.; Gu, C.S.: Copula entropy and information diffusion theory-based new prediction method for high dam monitoring. Earthq. Struct. 14(2), 143–153 (2018). https://doi.org/10.12989/eas.2018.14.2.143

    Article  Google Scholar 

  5. Shao, C.; Gu, C.; Yang, M.; Xu, Y.; Su, H.: A novel model of dam displacement based on panel data. Struct. Control. Health Monit. 25(1), e20371–e22037 (2018). https://doi.org/10.1002/stc.2037

    Article  Google Scholar 

  6. Mei, Y.T.; Xu, H.L.; Wang, F.; Wu, B.B.; Wan, L.L.: Time-varying prediction model of dam monitoring data based on principal component analysis. Water Power 37(10), 100–103 (2011)

    Google Scholar 

  7. Shen, W.W.; Ren, J.M.: Multiple stepwise regression analysis crack open degree data in gravity dam. Appl. Mech. Mater. 477–478, 888–891 (2013)

    Google Scholar 

  8. Su, H.Z.; Wen, Z.P.; Wu, Z.R.: Study on an intelligent inference engine in early-warning system of dam health. Water Resour. Manage 25(6), 1545–1563 (2011). https://doi.org/10.1007/s11269-010-9760-3

    Article  Google Scholar 

  9. Wen, H.Y., Zhou, L., Chen, G.Y., Hu, J.Y., He, M.L.: Research on dam displacement analysis model considering multi-factors. Appl. Mech. Mater. 351, 13l8–1324 (2013). https://doi.org/10.4028/www.scientific.net/AMM.351-352.1318

  10. Mt, O.L.: Application of fuzzy sets method to identify seepage path through dams. J. Hydraul. Eng. 129(7), 546–548 (2003). https://doi.org/10.1061/(ASCE)0733-9429(2003)129:7(546)

    Article  Google Scholar 

  11. Liu, H.C.; Liu, L.; Bian, Q.H.; Lin, Q.L.; Dong, N.; Xu, P.C.: Failure mode and effects analysis using fuzzy evidential seasoning approach and grey theory. Expert Syst. Appl. 338(4), 4403–4415 (2011). https://doi.org/10.1016/j.eswa.2010.09.110

    Article  Google Scholar 

  12. Liu, S.F.; Lin, Y.: Grey Systems: Theory and Applications. Springer, Berlin (2010)

    Book  Google Scholar 

  13. Sortis, A.D.; Paoliani, P.: Statistical analysis and structural identification in concrete dam monitoring. Eng. Struct. 29(1), 110–120 (2007). https://doi.org/10.1016/j.engstruct.2006.04.022

    Article  Google Scholar 

  14. Mata, J.: Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng. Struct. 33(8), 903–910 (2011). https://doi.org/10.1016/j.engstruct.2010.12.011

    Article  Google Scholar 

  15. Bayrak, T.: Modelling the relationship between water level and vertical displacements on the Yamula Dam. Nat. Hazards Earth Syst. Sci 7(2), 289–297 (2007). https://doi.org/10.5194/nhess-7-289-2007

    Article  Google Scholar 

  16. Tabari, M.R.; M. : Prediction of river runoff using fuzzy theory and direct search optimization algorithm coupled model. Arab. J. Sci. Eng. 41(10), 4039–4051 (2016)

    Article  Google Scholar 

  17. Zhong, D.H.; Shi, M.N.; Cui, B.; Wang, J.J.; Guan, T.: Research progress of the intelligent construction of dams. J. Hydraul. Eng. 50(1), 38–52 (2019). https://doi.org/10.13243/j.cnki.slxb.20181131

    Article  Google Scholar 

  18. Li, Y.; Bao, T.; Gong, J.; Shu, X.; Zhang, K.: The prediction of dam displacement time series using STL, extra-trees, and stacked LSTM neural network. IEEE Access 99, 1–1 (2020). https://doi.org/10.1109/ACCESS.2020.2995592

    Article  Google Scholar 

  19. Huang, Y.; Yang, L.; Liu, S.; Wang, G.: Multi-step wind speed forecasting based on ensemble empirical mode decomposition, long short term memory network and error correction strategy. Energies 10(12), 1–22 (2019). https://doi.org/10.3390/en12101822

    Article  Google Scholar 

  20. Bruce, A.; Donoho, D.; Gao, H.Y.: Wavelet analysis for signal processing. IEEE Spectr. 33(10), 26–35 (2002). https://doi.org/10.1109/6.540087

    Article  Google Scholar 

  21. Li, B.; Yang, J.; Hu, D.X.: Dam monitoring data analysis methods: a literature review. Struct. Control. Health Monit. (2020). https://doi.org/10.1002/stc.2501

    Article  Google Scholar 

  22. Kao, C.Y.; Loh, C.H.: Monitoring of long-term static displacement data of Fei-Tsui arch dam using artificial neural network-based approaches. Struct. Control. Health Monit. 20(3), 282–303 (2013). https://doi.org/10.1002/stc.492

    Article  Google Scholar 

  23. Rankovi, V.; Grujovi, N.; Divac, D., et al.: Modelling of dam behaviour based on neuro-fuzzy identification. Eng. Struct. 35, 107–113 (2012). https://doi.org/10.1016/j.engstruct.2011.11.011

    Article  Google Scholar 

  24. Salaza, F.; Rafael, M.; Miguel, Á.T.; Eugenio, O.: Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch. Comput. Methods Eng. (2015). https://doi.org/10.1007/s11831-015-9157-9

    Article  Google Scholar 

  25. Tabari, M.M.R.; Zarif, S.H.R.: Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models. Soft. Comput. 23, 9629–9645 (2019)

    Article  Google Scholar 

  26. Salleh, M.; Hussain, K.: A review of training methods of ANFIS for applications in business and economics. Int. J. U & E Serv. Sci. Technol. 9(7), 165–172 (2016)

    Article  Google Scholar 

  27. Nourani, V.; Partoviyan, A.: Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall-runoff modeling. Stochast. Environ. Res. Risk Assess. 32(2), 545–562 (2018)

    Article  Google Scholar 

  28. Ehsanzadeh, B.; Ahangari, K.: A novel approach in estimation of the soilcrete columns diameter and optimization of the high pressure jet grouting using adaptive neuro fuzzy inference system (ANFIS). Open J. Geol. 4(8), 386–398 (2014). https://doi.org/10.4236/ojg.2014.48030

    Article  Google Scholar 

  29. Li, X.D.; Zhong, R.B., et al.: Prediction of curtain grouting efficiency based on ANFIS. Bull. Eng. Geol. Environ. 78, 281–309 (2020)

    Article  Google Scholar 

  30. Adedeji, P.A.; Akinlabi, S.; Madushele, N.: Wind turbine power output short-term forecast: a comparative study of data clustering techniques in a PSO-ANFIS model. J. Clean. Prod. (2020). https://doi.org/10.1016/j.jclepro.2020.120135

    Article  Google Scholar 

  31. Seyedpoor, S.M.; Salajegheh, J.; Salajegheh, E.: Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks. Eng. Optim. 41(5), 473–493 (2009). https://doi.org/10.1080/03052150802596076

    Article  MathSciNet  MATH  Google Scholar 

  32. Bui, K.T.T.; Bui, D.T.; Zou, J.; Doan, C.; Revhaug, I.: A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput. Appl. (2018). https://doi.org/10.1007/s00521-016-2666-0

    Article  Google Scholar 

  33. Sklar, A.: Random variables, joint distribution functions, and copulas. Kybernetik-Praha 9(6), 449–460 (1973). https://doi.org/10.1098/rspa.2009.0502

    Article  MathSciNet  MATH  Google Scholar 

  34. Nelsen, R.B.: An introduction to copulas. Technometrics (2000). https://doi.org/10.2307/1271100

    Article  MATH  Google Scholar 

  35. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993). https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  36. Chahkoutahi, F.; Khashei, M.: A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting. Energy (2017). https://doi.org/10.1016/j.energy.2017.09.009

    Article  Google Scholar 

  37. Ratrout, N.T.: Subtractive clustering-based K-means technique for determining optimum time-of-day breakpoints. J. Comput. Civ. Eng. 25(5), 380–387 (2011)

    Article  Google Scholar 

  38. Khalil, B.; Ali, C.: Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theor. Appl. Climatol. 137, 1–13 (2018)

    Google Scholar 

  39. Nayak, J.; Naik, B.; Behera, H.S.: Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014. Springer, India (2015)

    Google Scholar 

  40. Zhang, Z.J.: A PSO-ANFIS based hybrid approach for short term PV power prediction in microgrids. Electr. Power Compon. Syst. 36, 95–103 (2018)

    Google Scholar 

Download references

Acknowledgements

This research was supported by the project Funded by the Key projects of natural science basic research program of Shaanxi province (Grant No. 2018JZ5010), The joint fund project of Natural science basic research program of Shaanxi province and Hanjiang to Weihe River Water Diversion Project Construction Co. Ltd., Shaanxi Province (Grant No. 2019JLM-55) and the water science plan project of Shaanxi province (Grant No. 2018SLKJ-5).

Author information

Authors and Affiliations

  1. State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an, 710048, China

    Fei Tong, Jie Yang, Chunhui Ma, Lin Cheng & Gaochao Li

Authors
  1. Fei Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chunhui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gaochao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fei Tong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tong, F., Yang, J., Ma, C. et al. The Prediction of Concrete Dam Displacement Using Copula-PSO-ANFIS Hybrid Model. Arab J Sci Eng 47, 4335–4350 (2022). https://doi.org/10.1007/s13369-021-06100-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-06100-w

Keywords

Search

Navigation