Evaluation and Comparison of Breach Parametric Model for Embankment Dams

Abstract:

Reliable approximations for breach parameters using a parametric model due to embankment dam failure are very significant factors in dam safety and mitigation measures. Therefore, utilizing information on historically unsuccessful embankment dams, several empirical models have been formulated by different researchers. There is still no universal method for calculating the occurrence of dam breaches. The main objective of this research is to evaluate and compare the selected parametric dam breach models that are available in the literature. Four models (Xu et al., Vescher et al., Froehlich, and USB Reclamation) were chosen to forecast the breach event's parameters and peak discharges. Historically, 59 failed embankment dams’ data in various countries around the world were used. In order to assess and compare the breach models and recommend the best-performing model for predicting the peak discharge, breach width, and failure time, ten (10) statistical quantitative indicators were used. The breach model developed by Xu et al. has good performance. Finally, to show the impacts of erodibility, validation, and sensitivity of the selected model, it was checked using eight dams’ data. During the validation, the calculated and observed results were in agreement. Sensitivity revealed that large fluctuations in the breach parameter and peak discharge were seen when the erodibility coefficient increased or decreased.

• Periodical: Advanced Engineering Forum (Volume 52)
• Pages: 27-44
• DOI: https://doi.org/10.4028/p-z42X2n
• Online since: July 2024
• Authors: Getachew Bereta Geremew*
• Keywords: Breach Parameters, Dam Breach, Embankment Dam, Peak Outflow, Statistical Indicators

References

[1] T. L. Wahl,Dam breach modeling-an overview of analysis methods, Joint Federal Interagency Conference on Sedimentation and Hydrologic Modeling (2010) pp.1-7.

[2] S. Yue, E. Damisse, and M. Ansar,Methodology for Earthen Dam Breach Analysis, in World Env. and Water Reso. Congress 2016© ASCE(2016), pp.100-110.

DOI: 10.1061/9780784479872.011

[3] L. Zhang, M. Peng, D. Chang, and Y. Xu, Dam failure mechanisms and risk assessment, John Wiley & Sons Singapore Pte. Ltd., 2016.

[4] W. T. L., Uncertainty of predictions of embankment dam breach parameters, J. of Hydraulic Engineering (2004) Vol.130, pp.389-397.

DOI: 10.1061/(asce)0733-9429(2004)130:5(389)

[5] L. S. Bentaher, and H. G. Elmazoghi,Estimation of dam breach widths using a neuro-fuzzy computing technique, Seventeenth International Water Technology Conference, IWTC17(2013), pp.1-13.

[6] P. O. Ali, and E. Tarkan, Peak discharge prediction due to embankment dam break by using sensitivity analysis based ANN, KSCE Journal of Civil Engineering (2014), Vol. 18(6), pp.1868-1876.

DOI: 10.1007/s12205-014-0047-8

[7] S. A. N. Vallejo, Hydraulic engineering failure of large dams, Msc., Cvil engineering, Graz University of Technology, 2016.

[8] T. C. MacDonald, and J. Langridge-Monopolis, Breaching characteristics of dam failures, J. of Hydraulic Engineering (1984), Vol.110(5), pp.567-586.

DOI: 10.1061/(asce)0733-9429(1984)110:5(567)

[9] S. L. Hunt, G. J. Hanson, K. R. Cook, and K. C. Kadavy, Breach widening observations from earthen embankment tests, American Society of Agricultural Engineers (2005), Vol.48(3), pp.1115-1120.

DOI: 10.13031/2013.18521

[10] A. E. T. Committee, Earthen Embankment Breaching, J. of Hydraulic Eng.ASCE(2011), Vol. 137(12), pp.1549-1564.

DOI: 10.1061/(asce)hy.1943-7900.0000498

[11] Zhong Qiming, Wang Meng, Wu Weiming, and Chen Shengshui, Comparison of simplified physically based dam breach models, Springer 84,(2016), p.1385–1418.

DOI: 10.1007/s11069-016-2492-9

[12] Xu Y, and Zhang L M, Breaching Parameters for Earth and Rockfill Dams, J. Geotech. Geoenviron.Eng. (2009), Vol. 135, pp.1957-1970.

DOI: 10.1061/(asce)gt.1943-5606.0000162

[13] J. Jan, and Ř. Jaromír, The failure of embankment dams due to overtopping, 2008.

[14] W. T. L., Prediction of embankment dam breach parameters, U.S. Department of the Interior Bureau of Reclamation Dam Dam Safety Resea, 1998.

[15] Bo Wang, Ting Zhang, Qin Zhou, Chao Wu, Yun-liang Chen, and Ping Wu, A case study of the Tangjiashan landslide dam-break, J.Hydrodyn. (2014), Vol. 27(2), pp.223-233.

DOI: 10.1016/s1001-6058(15)60476-0

[16] USBR, Guidelines for defining inundated areas downstream from Bureau of Reclamation dams, Reclamation Planning Instruction, No. 82-11; 15 June 1982, U.S. Bureau of Reclamation, 1982.

[17] USBR, Downstream hazard classification guidelines Assistant Commissioner-Engineering and Research, Denver, Colorado ACER Technical Memorandum No. 11, 1988.

[18] F. D. C., Peak outflow from breached embankment dam, J. Water Resour. Plan. Manage. Div., ASCE (1995a), Vol.121(1), pp.90-97.

DOI: 10.1061/(asce)0733-9496(1995)121:1(90)

[19] F. David, Embankment dam breach parameters revisited, Water Resource Eng. (1995b), pp.887-891.

[20] M. Mark, H. Mohamed, KortenhausAndreas, and V. Paul, Breaching Processes: A state of the art review 2009b.

[21] W. Weiming, Introduction to DLBreach-A Simplified Physically-Based Dam/Levee Breach Model (Version 2016.4), Clarkson University,Clarkson Avenue Potsdam, NY 13699, USA, 2016.

[22] W. T. L., H. G. J., C. Jean-Robert, M. M. W., K. René, M. J. T., and G. M. D., Development of next-generation embankment dam breach models, J. Hydraul. Eng.(2008), pp.767-779.

[23] X. Jiang, J. Huang, Z. W. Y. I. Niu, F. Chen, Z. Zou, and Z. Zhu, The influence of materials on the breaching process of natural dams, Springer-Verlag GmbH Germany (2017), pp.1-13.

[24] D. M. L., and M. G. H., Predicting loss of life in cases of dam failure and flash flood, Society for risk analysis (1993), Vol.13(2), pp.193-205.

DOI: 10.1111/j.1539-6924.1993.tb01069.x

[25] G. W. J., A Procedure for estimating loss of life caused by dam failure, U.S. Department of Interior Bureau of Reclamation Dam Safety Office Denver, Colorado, 1999.

[26] S. K. P., Sensitivity of outflow peaks and flood stages to the selection of dam breach parameters and simulation models, J. of Hydrology (1984), Vol.1-4(68), pp.295-310.

DOI: 10.1016/0022-1694(84)90217-8

[27] T. L. Wahl,Predicting Embankment Dam Breach Parameters-A Needs Assessment, XXVIIth IAHR Congress San Francisco, California (1997) pp.1-8.

[28] D. M. Gee,Comparison of Dam Breach Parameter Estimators, World Environmental and Water Resources Congress 2009: Great Rivers © 2009 ASCE, 2009.

DOI: 10.1061/41036(342)339

[29] B. C. A., and G. W. J., Assessing the threat to life from dam failure (1988), Vol. 24(6), pp.1303-1309.

[30] P. M. W., T. C. I., and A. S. R., Predicting Peak Outflow from Breached Embankment Dams, J. Hydrol. Eng. (2010), Vol. 15(5), pp.338-349.

DOI: 10.1061/(asce)he.1943-5584.0000197

[31] F. D.L., BREACH: An erosion model for earthen dam failures, Natl.Weather Serv.NOAA,Silver Spring, Maryland, 1998.

[32] W. T. L., Evaluation of erodibility-based embankment dam breach equations series, U.S. Dept. of the Interior, Bureau of Reclamation, Denver, Colorado, Hydraulic Laboratory Report HL-2014-02, 2014.

[33] S. V. Y. P., Dam breach modelling technology, Kluwer Academic Publishers, 1996.

[34] C. G. Dan, Sensitivity and Uncertainty Analysis Theory, Chapman & HALL/CRC, 2003.

[35] B. T. A., W. Aimrun, Y. Badronnisa, and K. M. Rowshon, Dam breach parameters and their influence on flood hydrographs for mosul dam, J. of Engineering Science and Technology (2017), Vol.12(11), pp.2896-2908.

[36] W. C. J., and M. Kenji, Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Climate research clim research (2005), Vol.30, pp.79-82.

DOI: 10.3354/cr030079

[37] Y. A.K., and C. S.S., Solar radiation prediction using Artificial Neural Network techniques: A review, Renewable and sustainable energy reviews (2014) Vol. 33, pp.772-81.

DOI: 10.1016/j.rser.2013.08.055

[38] B. Omar, K. Abdallah, and M. Kamal, Comparison of solar radiation models and their validation under Algerian climate -The case of direct irradiance, Energy conversion and management (2015), Vol.98, pp.236-251.

DOI: 10.1016/j.enconman.2015.03.067

[39] G. Mustafa, Ö. Mehmet, B. Aslı, and D. g. A. s. Tu˘gba, Integratingmetaheuristics and Artificial Neural Networks for improved stock price prediction, Expert systems with applications (2016), Vol.44(2016), pp.320-331.

DOI: 10.1016/j.eswa.2015.09.029

[40] B. T. Eddine, M. Mohamed-Salah, and M. I. Eddine, General models for estimation of the monthly mean daily diffuse solar radiation (Case study: Algeria), Energy conversion and management (2014), Vol. 81, pp.211-219.

DOI: 10.1016/j.enconman.2014.02.035

[41] J. Yingni, Estimation of monthly mean daily diffuse radiation in China, Applied Energy (2009) Vol. 86(9), pp.1458-1464.

DOI: 10.1016/j.apenergy.2009.01.002

[42] Y. Mehmet, and C. A. Naci, A critical review on the estimation of daily global solar radiation from sunshine duration, Energy conversion and management (2006), Vol. 47(2005), pp.2441-2450.

DOI: 10.1016/j.enconman.2005.11.002

[43] E. N. A., and M. M. G., New approche for estimating global solar radation across sudan, Energy conversion and management (2000), 41(2000), pp.419-434.

DOI: 10.1016/s0196-8904(99)00123-5

[44] S. R. J., Improved statistical procedure for the evaluation of solar radiation estimation models, Solar energy (1993), Vol. 51(4), pp.289-291.

DOI: 10.1016/0038-092x(93)90124-7

[45] Y. Wanxiang, L. Zhengrong, W. Yuyan, J. Fujian, and H. Lingzhou, Evaluation of global solar radiation models for Shanghai, China, Energy conversion and management (2014), Vol. 84(2014), pp.597-612.

DOI: 10.1016/j.enconman.2014.04.017

[46] A.-M. Z.A., M. A.H., and A.-S. S.M., Sunshine-based global radiation models: A review and case study, Energy conversion and management (2014), Vol. 81(2014), pp.209-216.

DOI: 10.1016/j.enconman.2014.04.021