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Shape Optimization of Double-Arch Dams by Using Parameters Obtained Through Bayesian Estimators

  • Enrico ZaccheiEmail author
  • José Luis Molina
Research paper
  • 26 Downloads

Abstract

The aim of this paper is to define the optimum shape of double-arch dams. This is studied here considering the shape of existing double-arch dams located in Spain. The analysis has been carried out in two consecutive stages. The first one refers to defining issues about Bayesian estimators to obtain the value for designing the optimum dam shape. In the second stage, the shape equations are iterated step-by-step. Data are taken from the inventory of Spanish existing dams. To obtain the non-available data, the Gaussian distribution under the Bayesian theorem hypotheses has been employed. This theorem converts the prior distribution using unknown parameters into the posterior distribution which provides expected parameters, i.e. the Bayesian estimators. The main challenge of the analysis is to identify the parameters which define the optimum shape of an existing dam. For this, over 30 dams have been selected and over 700 data have been collected. One of the main practical implications of this research comprises a reduction of the concrete volume, which implies a reduction of the financial costs and the environmental impact.

Keywords

Shape optimization Bayesian estimators Double-arch dams Spanish dams 

Notes

Acknowledgements

The first author acknowledges the “Servicios Informáticos CPD” of the University of Salamanca for the Wolfram Mathematica license and the University of Salamanca to pay the rights (when applicable) to completely download all papers in the references.

References

  1. Akbari A, Taghi Ahmadi M, Moharrami H (2011) Advances in concrete arch dams shape optimization. Appl Math Model 35:3316–3333.  https://doi.org/10.1016/j.apm.2011.01.020 CrossRefzbMATHGoogle Scholar
  2. Alrarejos-García L, Escuder-Bueno I, Morales-Torres A (2015) Advances on the failure analysis of the dam-foundation interface of concrete dams. Materials 8:8255–8278.  https://doi.org/10.3390/ma8125442 CrossRefGoogle Scholar
  3. AutoCAD (2010) Version 2010. Autodesk, IncGoogle Scholar
  4. Baker JW, Gupta A (2016) Bayesian treatment of induced seismicity in probabilistic seismic-hazard analysis. Bull Seismol Soc Am 106:860–870.  https://doi.org/10.1785/0120150258 CrossRefGoogle Scholar
  5. Bartoli G, Betti M, Facchini L, Marra AM, Monchetti S (2017) Bayesian model updating of historic masonry towers through dynamic experimental data. Proc Eng 199:1258–1263.  https://doi.org/10.1016/j.proeng.2017.09.267 CrossRefGoogle Scholar
  6. Beck JL, Katafygiotis LS (1998) Updating models and their uncertainties. I: Bayesian statistical framework. J Eng Mech 124:455–461.  https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4 CrossRefGoogle Scholar
  7. Buffi G, Manciola P, De Lorenzis L, Cavalagli N, Comodini F, Gambi A, Gusella V, Mezzi M, Niemeier W, Tamagnini C (2017a) Calibration of finite element models of concrete arch-gravity dams using dynamical measures: the case of Ridracoli. Proc Eng 199:110–115.  https://doi.org/10.1016/j.proeng.2017.09.169 CrossRefGoogle Scholar
  8. Buffi G, Manciola P, Grassi S, Barberini M, Gambi A (2017b) Survey of the Ridracoli dam: UAV-based photogrammetry and traditional topographic techniques in the inspection of vertical structures. Geomat Nat Hazard Risk 8:1–18.  https://doi.org/10.1080/19475705.2017.1362039 CrossRefGoogle Scholar
  9. Cardarelli E, Cercato M, Di Filippo G (2010) Geophysical investigation for the rehabilitation of a flood control embankment. Near Surf Geophys 8:287–296.  https://doi.org/10.3997/1873-0604.2010018 CrossRefGoogle Scholar
  10. Cardarelli E, Cercato M, De Donno G (2014) Characterization of an earth-filled dam through the combined use of electrical resistivity tomography, P- and SH-wave seismic tomography and surface wave data. J Appl Geophys 106:87–95.  https://doi.org/10.1016/j.jappgeo.2014.04.007 CrossRefGoogle Scholar
  11. Conte JP, Astroza R, Ebrahimian H (2015) Bayesian methods for nonlinear system identification of civil structures. MATEC Web Conf 24:1–7.  https://doi.org/10.1051/matecconf/20152403002 CrossRefGoogle Scholar
  12. Fan Q, Zhou S, Yang N (2015) Optimization design of foundation excavation for Xiluodu super-high arch dam in China. J Rock Mech Geotech Eng 7:120–135.  https://doi.org/10.1016/j.jrmge.2015.03.001 CrossRefGoogle Scholar
  13. Fanelli M, Lombardi G (1992) On the Lombardi “slenderness coefficient” for assessing the cracking potential of arch dams. In: Proceeding of the international symposium on arch dams, Nanjing, China, pp 1–6Google Scholar
  14. Gholizadeh S, Seyedpoor SM (2011) Shape optimization of arch dams by metaheuristics and neural networks for frequency constraints. Sci Iran 18:1020–1027.  https://doi.org/10.1016/j.scient.2011.08.001 CrossRefGoogle Scholar
  15. Gu H, Wu Z, Huang X, Song J (2015) Zoning modulus inversion method for concrete dams based on chaos genetic optimization algorithm. Math Probl Eng 2015:1–9.  https://doi.org/10.1155/2015/817241 CrossRefGoogle Scholar
  16. Hamidian D, Seyedpoor SM (2010) Shape optimal design of arch dams using an adaptive neuro-fuzzy inference system and improved particle swarm optimization. Appl Math Model 34:1574–1585.  https://doi.org/10.1016/j.apm.2009.09.001 MathSciNetCrossRefzbMATHGoogle Scholar
  17. Hariri-Ardebili MA, Fugani L, Meghella M, Saouma VE (2016) A new class of seismic damage and performance indices for arch dams via ETA method. Eng Struct 110:145–160.  https://doi.org/10.1016/j.engstruct.2015.11.021 CrossRefGoogle Scholar
  18. IGN-UPM (2013) Actualización de Mapas de Peligrosidad Sísmica de España 2012. Editorial Centro Nacional de Información Geográfica, Madrid, p 267. ISBN 978-84-416-2685-0Google Scholar
  19. Inventory of Dams and Reservoirs (2017) SNCZI. http://sig.mapama.es/snczi/visor.html. Accessed 2017
  20. Jin F, Chen Z, Wang J, Yang J (2010) Practical procedure for predicting non-uniform temperature on the exposed face of arch dams. Appl Therm Eng 30:2146–2156.  https://doi.org/10.1016/j.applthermaleng.2010.05.027 CrossRefGoogle Scholar
  21. Kaveh A, Ghaffarian R (2014) Shape optimization of arch dams with frequency constraints by enhanced charged system search algorithm and neural network. Int J Civ Eng 13:102–111.  https://doi.org/10.22068/IJCE.13.1.102 CrossRefGoogle Scholar
  22. Khatibinia M, Khosravi Sh (2014) A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams. Appl Soft Comput 16:223–233.  https://doi.org/10.1016/j.asoc.2013.12.008 CrossRefGoogle Scholar
  23. Khosravi S, Heydari MM (2013) Modelling of concrete gravity dam including dam-water-foundation rock interaction. World Appl Sci J 22:538–546.  https://doi.org/10.5829/idosi.wasj.2013.22.04.551 CrossRefGoogle Scholar
  24. Li Z, Gu C, Wu Z (2013) Nonparametric change point diagnosis method of concrete dam crack behavior abnormality. Math Probl Eng 2013:1–13.  https://doi.org/10.1155/2013/969021 MathSciNetCrossRefzbMATHGoogle Scholar
  25. Li Y, Wang J, Xu Z (2016) Design optimization of a concrete face rock-fill dam by using genetic algorithm. Math Probl Eng 2016:1–11.  https://doi.org/10.1155/2016/4971048 MathSciNetCrossRefGoogle Scholar
  26. Permanent Committee of Seismic Resistant Codes (2002) NCSE-02. Seismic resistant construction code: general rules and rules for buildings. SpainGoogle Scholar
  27. Reinoso J, Gonçalves JE, Pereira C, Bleninger T (2017) Cartography for civil engineering projects: photogrammetry supported by unmanned aerial vehicles. Iran J Sci Technol Trans Civ Eng.  https://doi.org/10.1007/s40996-017-0076-x CrossRefGoogle Scholar
  28. Ridolfi E, Buffi G, Venturi S, Manciola P (2017) Accuracy analysis of a dam model from drone surveys. Sensors 17:1777–1796.  https://doi.org/10.3390/s17081777 CrossRefGoogle Scholar
  29. Ross SM (2008) Probability and statistics for engineers and scientists, 2nd edn. Apogeo Editor, Italy, p 614Google Scholar
  30. Saber Mahani A, Shojaee S, Salajegheh E, Khatibinia M (2015) Hybridizing two-stage meta-heuristic optimization model with weighted least squares support vector machine for optimal shape of double-arch dams. Appl Soft Comput 27:205–218.  https://doi.org/10.1016/j.asoc.2014.11.014 CrossRefGoogle Scholar
  31. Savage JL, Houk IE (1931) Checking arch dam designs with models. Civ Eng 1:695–699.  https://doi.org/10.1007/978-1-4615-4601-6_6 CrossRefGoogle Scholar
  32. Seyedpoor SM, Gholizadeh S (2008) Optimum shape design of arch dams by a combination of simultaneous perturbation stochastic approximation and genetic algorithm methods. Adv Struct Eng 11:501–510.  https://doi.org/10.1260/136943308786412069 CrossRefGoogle Scholar
  33. Seyedpoor SM, Salajegheh J, Salajegheh E (2010) Shape optimal design of arch dams including dam-water-foundation rock interaction using a grading strategy and approximation concepts. Appl Math Model 34:1149–1163.  https://doi.org/10.1016/j.apm.2009.08.005 MathSciNetCrossRefzbMATHGoogle Scholar
  34. Seyedpoor SM, Salajegheh J, Salajegheh E, Gholizadeh S (2011) Optimal design of arch dams subjected to earthquake loading by a combination of simultaneous perturbation stochastic approximation and particle swarm algorithms. Appl Soft Comput 11:39–48.  https://doi.org/10.1016/j.asoc.2009.10.014 CrossRefGoogle Scholar
  35. Shouyi L, Lujun D, Lijuan Z, Wei Z (2009) Optimization design of arch dam shape whit modified complex method. Adv Eng Soft 40:804–808.  https://doi.org/10.1016/j.advengsoft.2009.01.013 CrossRefzbMATHGoogle Scholar
  36. Spanish Association of Dams and Reservoirs (2017) SEPREM. http://www.seprem.es/index.php. Accessed 2017
  37. US Army Corps of Engineers (1994) USACE. Arch dan design. Manual No. 1110-2-2201. Washington, DCGoogle Scholar
  38. Wolfram Mathematica (2017) Version 11 Student Edition. Wolfram Research, IncGoogle Scholar
  39. Xiao-fei Z, Shou-yi L, Yao-long C (2009) Optimization of geometric shape of Xiamen arch dam. Adv Eng Soft 40:105–109.  https://doi.org/10.1016/j.advengsoft.2008.03.013 CrossRefGoogle Scholar
  40. Yuen KV (2010) Bayesian methods for structural dynamics and civil engineering. Wiley, AsiaCrossRefGoogle Scholar
  41. Yuen KV, Beck JL, Katafygiotis LS (2006) Unified probabilistic approach for model updating and damage detection. J Appl Mech 73:555–564.  https://doi.org/10.1115/1.2150235 CrossRefzbMATHGoogle Scholar
  42. Zacchei E, Molina JL (2018) Estimation of optimal area and volume for double arch-dams. MATEC Web Conf 211:1–6.  https://doi.org/10.1051/matecconf/201821114002 CrossRefGoogle Scholar
  43. Zhang L, Liu Y, Zhang G, Zhang S (2015) Study on real-time simulation analysis and inverse analysis system for temperature and stress of concrete dam. Math Probl Eng 2015:1–8.  https://doi.org/10.1155/2015/306165 CrossRefGoogle Scholar
  44. Zhu K, Gu C, Qiu J, Liu W, Fang C, Li B (2016) Determining the optimal placement of sensors on a concrete arch dam using a quantum genetic algorithm. J Sens 2016:1–10.  https://doi.org/10.1155/2016/2567305 CrossRefGoogle Scholar

Copyright information

© Shiraz University 2018

Authors and Affiliations

  1. 1.Higher Polytechnic School of ÁvilaUniversity of Salamanca (USAL)ÁvilaSpain

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