Skip to main content
SpringerLink
Log in

An integrated method for evaluating and predicting long-term operation safety of concrete dams considering lag effect

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Effective operation safety evaluation of concrete dams is critical for ensuring the longevity and quality service of a dam. This paper introduces a novel method for quantifying the safety status of concrete dams and predicting future long-term safety performance, considering lag effect of indices. First, lag effect of operation indices is quantified using the modified moving average-cosine similarity method, based on which a comprehensive safety evaluation index system is established. Second, analytic hierarchy process is used to determine the subjective weighting of each index. Considering data correlation, a new method named coefficient of discreteness and independence is proposed to calculate the objective weighting of each index using maximal information coefficient. The final actual weighting of each index is assumed to be a linear combination of the above subjective and objective weightings. Third, based on the long-term monitoring data of a concrete dam, the safety score of a concrete dam can be quantified using technique for order preference by similarity to an ideal solution. Finally, neural networks (NN) are used to predict future long-term safety performance as a faster and simpler way to obtain future safety score. The effectiveness of this proposed method is verified through a case study. The case study showed that structural safety, environmental safety, and total safety scores of a concrete dam can fluctuate periodically, but the overall performance trend is relatively stable, as expected in real-world cases. NN were found to be accurate in predicting future safety performance.

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

Similar content being viewed by others

References

  1. Su H, Zhang S, Wen Z, Li H (2017) Prototype monitoring data-based analysis of time-varying material parameters of dams and their foundation with structural reinforcement. Eng Comput 33:1027–1043. https://doi.org/10.1007/s00366-017-0514-1

    Article  Google Scholar 

  2. Talon A, Curt C (2017) Selection of appropriate defuzzification methods: application to the assessment of dam performance. Expert Syst Appl 70:160–174. https://doi.org/10.1016/j.eswa.2016.09.004

    Article  Google Scholar 

  3. Su H, Wen Z, Sun X, Yan X (2018) Multisource information fusion-based approach diagnosing structural behavior of dam engineering. Struct Control Health Monit 25(2):e2073. https://doi.org/10.1002/stc.2073

    Article  Google Scholar 

  4. Dou S, Li J, Kang F (2019) Health diagnosis of concrete dams using hybrid FWA with RBF-based surrogate model. Water Sci Eng 12(3):188–195. https://doi.org/10.1016/j.wse.2019.09.002

    Article  Google Scholar 

  5. Li M, Shen Y, Ren Q, He Li (2019) A new distributed time series evolution prediction model for dam deformation based on constituent elements. Adv Eng Inform 39:41–52. https://doi.org/10.1016/j.aei.2018.11.006

    Article  Google Scholar 

  6. Chen S, Gu C, Lin C, Zhang K, Zhu Y (2020) Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement. Eng Comput. https://doi.org/10.1007/s00366-019-00924-9

    Article  Google Scholar 

  7. Salazar F, Toledo MÁ, González JM, Oñate E (2017) Early detection of anomalies in dam performance: a methodology based on boosted regression trees. Struct Control Health Monit 24(11):e2012. https://doi.org/10.1002/stc.2012

    Article  Google Scholar 

  8. Li X, Wen Z, Su H (2019) An approach using random forest intelligent algorithm to construct a monitoring model for dam safety. Eng Comput. https://doi.org/10.1007/s00366-019-00806-0

    Article  Google Scholar 

  9. Spross J, Gasch T (2019) Reliability-based alarm thresholds for structures analysed with the finite element method. Struct Saf 76:174–183. https://doi.org/10.1016/j.strusafe.2018.09.004

    Article  Google Scholar 

  10. Xu BS, Liu BB, Zheng DJ, Chen L, Wu CC (2012) Analysis method of thermal dam deformation. Sci China Technol Sci 55:1765–1772. https://doi.org/10.1007/s11431-012-4839-0

    Article  Google Scholar 

  11. Hongmei G, Zhihua W, Dandan J, Guoxing C, Liping J (2015) Fuzzy evaluation on seismic behavior of reservoir dams during the 2008 Wenchuan earthquake, China. Eng Geol 197:1–10. https://doi.org/10.1016/j.enggeo.2015.07.023

    Article  Google Scholar 

  12. Li Z, Li W, Ge W (2018) Weight analysis of influencing factors of dam break risk consequences. Nat Hazards Earth Syst Sci 18(12):3355–3362. https://doi.org/10.5194/nhess-18-3355-2018

    Article  Google Scholar 

  13. Bid S, Siddique G (2019) Human risk assessment of Panchet Dam in India using TOPSIS and WASPAS multi-criteria decision-making (MCDM) methods. Heliyon 5(6):e01956. https://doi.org/10.1016/j.heliyon.2019.e01956

    Article  Google Scholar 

  14. Gordan B, Koopialipoor M, Clementking A et al (2019) Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques. Eng Comput 35(3):945–954

    Article  Google Scholar 

  15. Gudipati VK, Cha EJ (2019) A framework for optimization of target reliability index for a building class based on aggregated cost. Struct Saf 81:101873. https://doi.org/10.1016/j.strusafe.2019.101873

    Article  Google Scholar 

  16. Kang F, Liu J, Li J, Li S (2017) Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Health Monit 24(10):e1997. https://doi.org/10.1002/stc.1997

    Article  Google Scholar 

  17. Nejad FP, Jaksa MB (2017) Load-settlement behavior modeling of single piles using artificial neural networks and CPT data. Comput Geotech 89:9–21. https://doi.org/10.1016/j.compgeo.2017.04.003

    Article  Google Scholar 

  18. Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97. https://doi.org/10.1007/s00366-015-0400-7

    Article  Google Scholar 

  19. Yang J, Qu X, Chang M (2019) An intelligent singular value diagnostic method for concrete dam deformation monitoring. Water Sci Eng 12(3):205–212. https://doi.org/10.1016/j.wse.2019.09.006

    Article  Google Scholar 

  20. Mata J (2011) Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 33(3):903–910. https://doi.org/10.1016/j.engstruct.2010.12.011

    Article  Google Scholar 

  21. Yang W, Xu K, Lian J, Ma C, Bin L (2018) Integrated flood vulnerability assessment approach based on TOPSIS and Shannon entropy methods. Ecol Indic 89:269–280. https://doi.org/10.1016/j.ecolind.2018.02.015

    Article  Google Scholar 

  22. Zhu F, Zhong PA, Sun Y, Yeh WWG (2017) Real-time optimal flood control decision making and risk propagation under multiple uncertainties. Water Resour Res 53(12):10635–10654. https://doi.org/10.1002/2017wr021480

    Article  Google Scholar 

  23. Hassanvand MR, Karami H, Mousavi SF (2019) Use of multi-criteria decision-making for selecting spillway type and optimizing dimensions by applying the harmony search algorithm: Qeshlagh Dam Case Study. Lakes Reserv Res Manag 24(1):66–75. https://doi.org/10.1111/lre.12250

    Article  Google Scholar 

  24. Salazar F, Toledo MA, Oñate E, Morán R (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9–17. https://doi.org/10.1016/j.strusafe.2015.05.001

    Article  Google Scholar 

  25. Ferrario E, Pedroni N, Zio E, Lopez-Caballero F (2017) Bootstrapped artificial neural networks for the seismic analysis of structural systems. Struct Saf 67:70–84. https://doi.org/10.1016/j.strusafe.2017.03.003

    Article  Google Scholar 

  26. Ghaleini EN, Koopialipoor M, Momenzadeh M, Sarafraz ME, Mohamad ET, Gordan B (2019) A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Eng Comput 35:647–658. https://doi.org/10.1007/s00366-018-0625-3

    Article  Google Scholar 

  27. Bui DT, Nhu VH, Hoang ND (2018) Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and multi-layer perceptron neural network. Adv Eng Inform 38:593–604. https://doi.org/10.1016/j.aei.2018.09.005

    Article  Google Scholar 

  28. Ribas JR, Arce ME, Sohler FA, Suárez-García A (2019) Multi-criteria risk assessment: case study of a large hydroelectric project. J Clean Prod 227:237–247. https://doi.org/10.1016/j.jclepro.2019.04.043

    Article  Google Scholar 

  29. Jing M, Jie Y, Shou-yi L, Lu W (2018) Application of fuzzy analytic hierarchy process in the risk assessment of dangerous small-sized reservoirs. Int J Mach Learn Cybern 9(1):113–123. https://doi.org/10.1007/s13042-015-0363-4

    Article  Google Scholar 

  30. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC (2011) Detecting novel associations in large data sets. Science 334(6062):1518–1524. https://doi.org/10.1126/science.1205438

    Article  MATH  Google Scholar 

  31. Kinney JB, Atwal GS (2014) Equitability, mutual information, and the maximal information coefficient. Proc Natl Acad Sci 111(9):3354–3359. https://doi.org/10.1073/pnas.1309933111

    Article  MathSciNet  MATH  Google Scholar 

  32. Wang S, Zhao Y, Shu Y, Yuan H, Geng J, Wang S (2018) Fast search local extremum for maximal information coefficient (MIC). J Comput Appl Math 327:372–387. https://doi.org/10.1016/j.cam.2017.05.038

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Key Research and Development Program (2018YFC0406905) and the National Natural Science Foundation of China (Grant nos. 51879185 and 51622904).

Author information

Authors and Affiliations

  1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300350, China

    Mingchao Li, Wen Si, Qiubing Ren & Han Liu

  2. Department of Construction Management, University of Houston, Houston, TX, 77204, USA

    Lingguang Song

Authors
  1. Mingchao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen Si
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiubing Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingguang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Han Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingchao Li.

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, M., Si, W., Ren, Q. et al. An integrated method for evaluating and predicting long-term operation safety of concrete dams considering lag effect. Engineering with Computers 37, 2505–2519 (2021). https://doi.org/10.1007/s00366-020-00956-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-00956-6

Keywords

Search

Navigation