Skip to main content
SpringerLink
Log in

A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes

  • Original Paper
  • Published:
Journal of Civil Structural Health Monitoring Aims and scope Submit manuscript

Abstract

To further enhance the prediction accuracy of the monitoring model and improve the rationality of the method for determining the monitoring indexes, this paper considers the impact of the spatial variability of rockfill materials on the displacement of the dam. Based on the stochastic finite element method (SFEM), a new rockfill dam displacement monitoring hybrid model is established, and a method to determine the hybrid index for rockfill dam displacement monitoring is proposed by multi-index amalgamation. First, the SFEM is used to simulate the spatial variability of the rockfill mechanical parameters in the process of calculating the water pressure component and the time-dependent component. On this basis, the expression of each component is fitted to construct a stochastic finite element method hybrid model (SFEMH model) of the rockfill dam. Subsequently, by predicting the displacement of the dam and separating each displacement component, the displacement monitoring hybrid index of the rockfill dam is determined by combining the confidence interval method and the most unfavourable components. Finally, the SFEMH model and hybrid index are applied to analyse the actual monitoring data of a rockfill dam. The results show that the proposed model and the method for determining the displacement monitoring hybrid index are scientific and reasonable. The prediction accuracy and effect of the proposed novel model outperform those of other methods in terms of many evaluation indicators, and the reliability of the monitoring indexes is significantly improved. The proposed hybrid model and hybrid index provide a new method for the efficient operation management and accurate safety performance evaluation of rockfill dams.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Availability of data and code

The data sets supporting the results of this article are included within the article. The custom code used for its processing are available from the corresponding author upon reasonable request.

References

  1. Wen LF, Chai JR, Xu ZG, Qin Y, Li YL (2017) Monitoring and numerical analysis of behaviour of Miaojiaba concrete-face rockfill dam built on river gravel foundation in China. Comput Geotech 85:230–248. https://doi.org/10.1016/j.compgeo.2016.12.018

    Article  Google Scholar 

  2. Wang YJ (2006) Monitoring design on concrete slab dams. Dam Saf 01:1–4

    Google Scholar 

  3. Wang SW, Xu C, Gu CS, Su HZ, Hu K, Xia Q (2020) Displacement monitoring model of concrete dams using the shape feature clustering-based temperature principal component factor. Struct Control Hlth 27(10):e2603. https://doi.org/10.1002/stc.2603

    Article  Google Scholar 

  4. Yang J, Qu X, Hu D, Song J, Cheng L, Li B (2021) Research on singular value detection method of concrete dam deformation monitoring. Measurement. https://doi.org/10.1016/j.measurement.2021.109457

    Article  Google Scholar 

  5. Wen LF, Chai JR, Xu ZG, Qin Y, Li YL (2018) A statistical review of the behaviour of concrete-face rockfill dams based on case histories. Geotechnique 68(9):749–771. https://doi.org/10.1680/jgeot.17.P.095

    Article  Google Scholar 

  6. Xu B, Xia H (2016) Reviews on analysis methods of behavior and hazards of cracks in concrete dams. J Water Resour Water Eng 27(06):162–168

    Google Scholar 

  7. Bukenya P et al (2014) Health monitoring of concrete dams: a literature review. J Civ Struct Health Monit 4(4):235–244. https://doi.org/10.1007/s13349-014-0079-2

    Article  Google Scholar 

  8. Eslami A, Ghorbani A, Shahraini SV (2020) Health monitoring and dynamic analysis of an earth-fill dam at the stage of first impounding: case study-Siahoo dam of Iran. J Civ Struct Health Monit 10(3):425–442. https://doi.org/10.1007/s13349-020-00393-5

    Article  Google Scholar 

  9. Chen S, Gu C, Lin C et al (2020) Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine. Measurement 166:108161. https://doi.org/10.1016/j.measurement.2020.108161

    Article  Google Scholar 

  10. Kang F, Liu X, Li JJ (2020) Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines. Struct Health Monit 19(4):987–1002. https://doi.org/10.1177/1475921719872939

    Article  Google Scholar 

  11. Li B, Yang J, Hu DX (2020) Dam monitoring data analysis methods: a literature review. Struct Control Health 27(3):e2501.1-e2501.14. https://doi.org/10.1002/stc.2501

    Article  Google Scholar 

  12. Zhang H, Chen JK, Wu ZY, Wang WN (2012) The framework research of dam safety monitor multiple models analysis system in computer. Appl Mech Mater 170–173:2152–2157. https://doi.org/10.4028/www.scientific.net/AMM.170-173.2152

    Article  Google Scholar 

  13. Bonaldi P, Fanelli M, Giuseppetti G (1977) Displacement forecasting for concrete dams. Intemat Water Power Dam Construct 29(9):42–50

    Google Scholar 

  14. Gu CS, Fu X, Shao CF, Shi ZW, Su HZ (2020) Application of spatiotemporal hybrid model of deformation in safety monitoring of high arch dams: a case study. Int J Env Res Pub He 17(1):319. https://doi.org/10.3390/ijerph17010319

    Article  Google Scholar 

  15. Ribeiro LS, Wilhelm VE, Faria EF, Correa JM, Santos ACP (2019) A comparative analysis of long-term concrete deformation models of a buttress dam. Eng Struct 193:301–307. https://doi.org/10.1016/j.engstruct.2019.05.043

    Article  Google Scholar 

  16. Gu CS, Zhao EF (2019) Dam safety monitoring theories and methods. Hohai University Press, Nanjing

    Google Scholar 

  17. Salazar F, Morán R, Toledo MÁ, Eugenio O (2017) Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch Comput Method E 24(1):1–21. https://doi.org/10.1007/s11831-015-9157-9

    Article  MATH  Google Scholar 

  18. Liu X, Kang F, Ma C, Li H (2021) Concrete arch dam behavior prediction using kernel-extreme learning machines considering thermal effect. J Civ Struct Health Monit 11(2):283–299. https://doi.org/10.1007/s13349-020-00452-x

    Article  Google Scholar 

  19. Gu CS, Su HZ (2015) Current status and prospects of long-term service and risk assessment of concrete dams. Adv Sci Technol Water Resour 35(05):1–12. https://doi.org/10.3880/j.issn.1006-7647.2015.05.001

    Article  Google Scholar 

  20. Chen H, Liu DH (2019) Stochastic finite element analysis of rockfill dam considering spatial variability of dam material porosity. Eng Comput 36(9):2929–2959. https://doi.org/10.1108/EC-06-2018-0266

    Article  Google Scholar 

  21. Liu DH, Chen H (2019) Relationship between porosity and the constitutive model parameters of rockfill materials. J Mater Civil Eng 31(2):04018384.1-04018384.14. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002598

    Article  Google Scholar 

  22. Guo XF, Dias D, Pan QJ (2019) Probabilistic stability analysis of an embankment dam considering soil spatial variability. Comput Geotech 113:103093.1-103093.12. https://doi.org/10.1016/j.compgeo.2019.103093

    Article  Google Scholar 

  23. Mouyeaux A, Carvajal C, Bressolette P, Peyras L, Breul P, Bacconnet C (2018) Probabilistic stability analysis of an earth dam by stochastic finite element method based on field data. Comput Geotech 101:34–47. https://doi.org/10.1016/j.compgeo.2018.04.017

    Article  Google Scholar 

  24. Gu CH, Cao X, Xu B (2019) Stochastic inversion method for concrete dams on the basis of bayesian back analysis theory. Adv Civ Eng 2019:5943913. https://doi.org/10.1155/2019/5943913

    Article  Google Scholar 

  25. Wu ZR (2003) Safety monitoring theory and its application of hydraulic structures. Higher Education Press, Beijing

    Google Scholar 

  26. Lumb P (1966) The variability of natural soils. Can Geotech J 3(2):74–97

    Article  Google Scholar 

  27. Vanmarcke EH (1977) Probabilistic modeling of soil profiles. J Geotech Eng Div Asce 103(11):1227–1246

    Article  Google Scholar 

  28. Stefanou G (2009) The stochastic finite element method: past, present and future. Comput Method Appl M 198:1031–1051. https://doi.org/10.1016/j.cma.2008.11.007

    Article  MATH  Google Scholar 

  29. Cho SE, Park HC (2010) Effect of spatial variability of cross-correlated soil properties on bearing capacity of strip footing. Int J Numer Anal Met 34(1):1–26. https://doi.org/10.1002/nag.791

    Article  MATH  Google Scholar 

  30. Kasama K, Whittle AJ, Zen K (2012) Effect of spatial variability on the bearing capacity of cement-treated ground. Soils Found 52(4):600–619. https://doi.org/10.1016/j.sandf.2012.07.003

    Article  Google Scholar 

  31. Suchomel R, Mašin D (2010) Comparison of different probabilistic methods for predicting stability of a slope in spatially variable c-phi soil. Comput Geotech 37(1–2):132–140. https://doi.org/10.1016/j.compgeo.2009.08.005

    Article  Google Scholar 

  32. Jiang SH, Li QD, Zhou CB, Fang GG (2014) Slope reliability analysis considering effect of autocorrelation functions. Chin J Geotech Eng 36(03):508–518

    Google Scholar 

  33. Tan XH, Wang X, Khoshnevisan S, Hou XL, Zha FS (2017) Seepage analysis of earth dams considering spatial variability of hydraulic parameters. Eng Geol 228:260–269. https://doi.org/10.1016/j.enggeo.2017.08.018

    Article  Google Scholar 

  34. Zhu B, Pei HF, Yang Q (2019) Reliability analysis of submarine slope considering the spatial variability of the sediment strength using random fields. Appl Ocean Res 86:340–350. https://doi.org/10.1016/j.apor.2019.03.011

    Article  Google Scholar 

  35. Wu QX (2005) Structural reliability analysis and stochastic finite element method: theory, method, engineering application and program design. China Machine Press, Beijing

    Google Scholar 

  36. Bong T, Stuedlein AW (2018) Efficient methodology for probabilistic analysis of consolidation considering spatial variability. Eng Geol 237:53–63. https://doi.org/10.1016/j.enggeo.2018.02.009

    Article  Google Scholar 

  37. Chen DF, Xu DP, Ren GF, Jiang Q, Liu GF, Wan LP, Li N (2019) Simulation of cross-correlated non-Gaussian random fields for layered rock mass mechanical parameters. Comput Geotech 112:104–119. https://doi.org/10.1016/j.compgeo.2019.04.012

    Article  Google Scholar 

  38. Jiang SH, Li DQ, Zhang LM, Zhou CB (2014) Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method. Eng Geol 168:120–128. https://doi.org/10.1016/j.enggeo.2013.11.006

    Article  Google Scholar 

  39. Fu XD, Qian PY, Liu ZD (2001) The reliability analysis for slope stability by perturbation stochastic finite element method. Rock Soil Mech 04:413–418

    Google Scholar 

  40. Li DQ, Jiang SH, Chen YF, Zhou CB (2014) Reliability analysis of serviceability performance for an underground cavern using a non-intrusive stochastic method. Environ Earth Sci 71(3):1169–1182. https://doi.org/10.1007/s12665-013-2521-x

    Article  Google Scholar 

  41. Olsson AMJ, Sandberg GE (2002) Latin hypercube sampling for stochastic finite element analysis. J Eng Mech 128(1):121–125. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:1(121)

    Article  Google Scholar 

  42. Chen JY, Liu PF, Xu Q, Li J (2020) Seismic analysis of hardfill dams considering spatial variability of material parameters. Eng Struct 211:110439. https://doi.org/10.1016/j.engstruct.2020.110439

    Article  Google Scholar 

  43. Zhang Z (2019) Study on rheological properties of fully weathered argillaceous siltstone and tunnel deformation control technology. Dissertation, Xi’an University of Architecture and Technology

  44. Li YL, Li SY, Ding ZF, Tu X (2013) The sensitivity analysis of Duncan-chang E-B model parameters based on the orthogonal test method. J Hydraul Eng 44(07):873–879

    Google Scholar 

  45. Chen Y, Gu CS, Shao CF, Qin XN (2019) Parameter sensitivity and inversion analysis for a concrete face rockfill dam based on CS-BPNN. Adv Civ Eng 2019:9742961. https://doi.org/10.1155/2019/9742961

    Article  Google Scholar 

  46. Sun PM, Bao TF, Gu CS, Jiang M, Wang T, Shi ZW (2016) Parameter sensitivity and inversion analysis of a concrete faced rock-fill dam based on HS-BPNN algorithm. Sci China Technol Sci 59(9):1442–1451. https://doi.org/10.1007/s11431-016-0213-y

    Article  Google Scholar 

  47. Ma CH, Yang J, Cheng L, Li T, Li YQ (2019) Adaptive inversion analysis of material parameters of rock-fill dam based on QGA-MMRVM. Rock Soil Mech 40(06):2397–2406

    Google Scholar 

  48. Yang J, Ma CH, Xiang Y, Wang JE, Cheng L (2018) Uncertainty inverse analysis of dam material parameters based on relevance vector machine and stochastic finite element method. Scientia Sinica Technologica 48(10):1113–1121

    Article  Google Scholar 

  49. Huang Y, Wan Z (2018) Study on viscoelastic deformation monitoring index of an RCC gravity dam in an alpine region using orthogonal test design. Math Probl Eng 2018:8743505. https://doi.org/10.1155/2018/8743505

    Article  Google Scholar 

  50. Li ZC, Hou HJ (2010) Theory and methods on dam safety monitoring indexes. Water Power 36(05):64–67

    Google Scholar 

  51. Yang G, Griffiths DV, Zhu S (2019) Seismic slope stability analysis of earth-rockfill dams considering spatial variability of rockfill materials via random finite element method. China Earthguake Eng J 41(04):939–948

    Google Scholar 

  52. Pramthawee P, Jongpradist P, Sukkarak R (2017) Integration of creep into a modified hardening soil model for time-dependent analysis of a high rockfill dam. Comput Geotech 91:104–116. https://doi.org/10.1016/j.compgeo.2017.07.008

    Article  Google Scholar 

  53. Li HF, Zhang YQ (2012) Creep rate and creep model of rockfill. Procedia Eng 28:796–802. https://doi.org/10.1016/j.proeng.2012.01.812

    Article  Google Scholar 

  54. Shi Y, Yang J, Wu J, He J (2018) A statistical model of deformation during the construction of a concrete face rockfill dam. Struct Control Hlth 25(2):e2074. https://doi.org/10.1002/stc.2074

    Article  Google Scholar 

  55. Yao FH, Guan SH, Yang H, Chen Y, Qiu HF, Ma G, Liu QW (2019) Long-term deformation analysis of Shuibuya concrete face rockfill dam based on response surface method and improved genetic algorithm. Water Sci Eng 12(3):196–204

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the natural science basic research program of Shaanxi province and the water science plan project of Shaanxi province for supporting this research.

Funding

This work was supported by the key projects of natural science basic research program of Shaanxi province (project No. 2018JZ5010), the water science plan project of Shaanxi province (project No. 2018SLKJ-5), and the joint funds of natural science fundamental research program of Shaanxi province of China and the Hanjiang-to-weihe river valley water diversion project (Project No. 2019JLM-55).

Author information

Authors and Affiliations

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

    Ran Li, Yang Jie, Ma Chunhui, Cheng Lin & Wang Jian’e

  2. Institute of Water Resources and Hydro-electric Engineering, Xi’an University of Technology, Xi’an, China

    Ran Li, Yang Jie, Ma Chunhui, Cheng Lin & Wang Jian’e

  3. Hanjiang-to-weihe River Valley Water Diversion Project Construction Co., Ltd., Shaanxi, Xi’an, China

    Zhang Pengli, Wang Jiaming & Cui Chao

  4. Shaanxi Railway Institute, Weinan, China

    Zhou Mingjuan

Authors
  1. Ran Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Jie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhang Pengli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wang Jiaming
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ma Chunhui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cui Chao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Cheng Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wang Jian’e
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zhou Mingjuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ran Li.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Written informed consent for publication was obtained from all participants.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, R., Jie, Y., Pengli, Z. et al. A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes. J Civil Struct Health Monit 12, 817–832 (2022). https://doi.org/10.1007/s13349-022-00562-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13349-022-00562-8

Keywords

Search

Navigation