Optimization of Invasive Weed for Optimal Dimensions of Concrete Gravity Dams

Document Type : Regular Article

Authors

1 Assistant Professor of Geotechnical Engineering, Department of Civil Engineering, Faculty of Civil and Architecture Engineering, Malayer University, Iran

2 Master of Civil Engineering, Khoramshahr Marine Science and Technology University, Khorramshahr, Iran

3 Ph.D. Student of Civil Engineering, Geotechnical, Razi University, Kermanshah, Iran

Abstract

Dam construction projects among the most extensive and most expensive projects are considered. It is always appropriate and optimal for such concrete structures to reduce the volume of concrete and consequently reduce construction costs is essential. In this study, invasive weed optimization software GNU octave, dimensions of concrete gravity dam Koyna located in India optimized stability constraints. For this purpose, a cross-section with a length unit consists of eight geometric parameters as input variables, and other geometric parameters were defined using these variables. The result showed that invasive weeds are well-optimized dimensions of the dam as the volume of concrete in the construction of the dam at the current level measures 3633 cubic meters that optimal dropped 3353 cubic meters, which is a mean of 7.7% of the value of the objective function (the volume of concrete in dams) is reduced. This amount of concrete decreased the construction of the dam, saving the cost and is more economical.

Highlights

  • A cross-section with a length unit consists of eight geometric parameters as input variables considered, and other geometric parameters were defined using these variables.
  • Invasive weeds are well-optimized dimensions of the dam as the volume of concrete in the construction of the dam.
  • The current level measures 3633 cubic meters that optimal dropped 3353 cubic meters that, is mean 7.7% of the value of the objective function (the volume of concrete in dams) is reduced.
  • This amount of decrease of concrete in the construction of the dam saves the cost of building the dam and is more economical.

Keywords

Main Subjects

References

[1]     Ghiasi V, Koushki M. Numerical investigation of ground surface settlement due to circular tunnelling influenced by variations of geometric characteristics of tunnel and mechanical properties of saturated soil and its prediction in the artificial neural network. J Model Eng 2021;19:27–39. https://doi.org/10.22075/JME.2019.18022.1735.
[2]     Ghiasi V, Moradi M. Numerical Modelling of Disconnected Piled-Raft Foundation Systems Settlement with an Emphasis on New Definition of These Systems with Hybrid System. J Model Eng 2018;16:235–45. https://doi.org/10.22075/JME.2018.12596.1232.
[3]     Rezaei Pajand M, Khaleghi K. Geometrical Models for Optimum Shape of Concrete Arch Dams. J Model Eng 2010;8:1–15. https://doi.org/10.22075/jme.2017.1549.
[4]     Ghanizadeh AR, Ziaee A, Khatami SMH, Fakharian P. Predicting Resilient Modulus of Clayey Subgrade Soils by Means of Cone Penetration Test Results and Back-Propagation Artificial Neural Network. J Rehabil Civ Eng 2022;10:146–62. https://doi.org/10.22075/jrce.2022.25013.1568.
[5]     Rezazadeh Eidgahee D, Jahangir H, Solatifar N, Fakharian P, Rezaeemanesh M. Data-driven estimation models of asphalt mixtures dynamic modulus using ANN, GP and combinatorial GMDH approaches. Neural Comput Appl 2022;34:17289–314. https://doi.org/10.1007/s00521-022-07382-3.
[6]     Naderpour H, Sharei M, Fakharian P, Heravi MAMA. Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP. J Soft Comput Civ Eng 2022;6:66–87. https://doi.org/10.22115/scce.2022.283486.1308.
[7]     Zeynali MJ, Mohammad Reza Pour O, Frooghi F. Evaluation of Particle Swarm, Genetic and Continuous Ant Colony Algorithms In Optimal Operation of Doroodzan Dam Reservoir. Water Soil Sci 2015;25:27–38.
[8]     Simoes LMC, Lapa JAM. Optimal Shape of Dams Subject to Earthquakes. Int. Conf. Comput. Struct. Technol. - Proc., BHV Topping and M. Papadrakakis; 1994, p. 119–30. https://doi.org/10.4203/ccp.25.4.4.
[9]     Cai X, McKinney DC, Lasdon LS. Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Adv Water Resour 2001;24:667–76. https://doi.org/10.1016/S0309-1708(00)00069-5.
[10]   Chen L. Real coded genetic algorithm optimization of long term reservoir operation. J Am Water Resour Assoc 2003;39:1157–65. https://doi.org/10.1111/j.1752-1688.2003.tb03699.x.
[11]   Mollazadeh M, Barani G, Salajegheh J. Study of Difference Criteria on Optimization of Gravity Dams Shape using Genetic Algorithm. Collection of papers of 5th Hydraulic Conference. Collect. Pap. 5th Hydraul. Conf., 2005.
[12]   Bozorg Haddad O, Afshar A, Mariño MA. Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization. Water Resour Manag 2006;20:661–80. https://doi.org/10.1007/s11269-005-9001-3.
[13]   Nagesh Kumar D, Raju KS, Ashok B. Optimal Reservoir Operation for Irrigation of Multiple Crops Using Genetic Algorithms. J Irrig Drain Eng 2006;132:123–9. https://doi.org/10.1061/(ASCE)0733-9437(2006)132:2(123).
[14]   Afshar A, Bozorg Haddad O, Mariño MA, Adams BJ. Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. J Franklin Inst 2007;344:452–62. https://doi.org/10.1016/j.jfranklin.2006.06.001.
[15]   Dariane AB, Moradi AM. Reservoir operating by ant colony optimization for continuous domains (ACOR) case study: Dez reservoir. Int J Math Phys Eng Sci 2009;3:125–9.
[16]   Azarafza H, Rezaei H, Behmanesh J, Besharat S. Results comparison of employing PSO, GA and SA algorithms in optimizing reservoir operation (case study: Shaharchai Dam, Urmia, Iran). Water Soil 2012;26:1101–8. https://doi.org/10.22067/JSW.V1391I0.16925.
[17]   Dariane AB, Farahmandfar Z. A comparative study of marriage in honey bees optimisation (MBO) algorithm in multi-reservoir system optimisation. Water SA 2013;39:327–34. https://doi.org/10.4314/wsa.v39i2.17.
[18]   Ghodousi H, Oskouhi M. Determination of optimal dimensions of concrete gravity dams using LINGO11 nonlinear modeling. J Civ Eng Urban 2015;5:47–52.
[19]   Alikhani A. Optimization of the cross-section of the two-arched dam by the method of abc artificial bee colony. Fifth Conf. Exhib. Iran Dam Tunnel, Tehran, Fan Aria Publishing Art Non-Commercial Institute; 2019.
[20]   Emami S, Parsa J. Optimization of Concrete Gravity Dam Section using New Election Meta-heuristic Algorithm. J Struct Constr Eng 2022;8:164–83. https://doi.org/10.22065/JSCE.2021.253952.2272.
[21]   Satish BJN, Venkatesh C, Reddy BA, Reddy KHK, Bellum RR. Design optimization of non-overflow section of a concrete gravity dam. J Build Pathol Rehabil 2022;7:31. https://doi.org/10.1007/s41024-022-00169-y.
[22]   Abdollahi A, Amini A, Hariri-Ardebili MA. An uncertainty-aware dynamic shape optimization framework: Gravity dam design. Reliab Eng Syst Saf 2022;222:108402. https://doi.org/10.1016/j.ress.2022.108402.
[23]   Sadeghifar T, Barati R. Application of Adaptive Neuro-Fuzzy Inference System to Estimate Alongshore Sediment Transport Rate (A Real Case Study: Southern Shorelines of Caspian Sea). J Soft Comput Civ Eng 2018;2:72–85. https://doi.org/10.22115/SCCE.2018.135975.1074.
[24]   Barati R, Neyshabouri SAAS, Ahmadi G. Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach. Powder Technol 2014;257:11–9. https://doi.org/10.1016/j.powtec.2014.02.045.
[25]   Alizadeh MJ, Shahheydari H, Kavianpour MR, Shamloo H, Barati R. Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network. Environ Earth Sci 2017;76:86. https://doi.org/10.1007/s12665-016-6379-6.
[26]   Kazemi M, Barati R. Application of dimensional analysis and multi-gene genetic programming to predict the performance of tunnel boring machines. Appl Soft Comput 2022;124:108997. https://doi.org/10.1016/j.asoc.2022.108997.
[27]   Hosseini K, Nodoushan EJ, Barati R, Shahheydari H. Optimal design of labyrinth spillways using meta-heuristic algorithms. KSCE J Civ Eng 2016;20:468–77. https://doi.org/10.1007/s12205-015-0462-5.
[28]   Seifollahi M, Abbasi S, Abraham J, Norouzi R, Daneshfaraz R, Lotfollahi-Yaghin MA, et al. Optimization of Gravity Concrete Dams Using the Grasshopper Algorithm (Case Study: Koyna Dam). Geotech Geol Eng 2022:1–16. https://doi.org/10.1007/s10706-022-02227-1.
[29]   Kumar M, Husain M, Upreti N, Gupta D. Genetic Algorithm: Review and Application. SSRN Electron J 2010. https://doi.org/10.2139/ssrn.3529843.
[30]   Shami TM, El-Saleh AA, Alswaitti M, Al-Tashi Q, Summakieh MA, Mirjalili S. Particle Swarm Optimization: A Comprehensive Survey. IEEE Access 2022;10:10031–61. https://doi.org/10.1109/ACCESS.2022.3142859.
[31]   Mahfoud S, Derouich A, Iqbal A, El Ouanjli N. ANT-colony optimization-direct torque control for a doubly fed induction motor: An experimental validation. Energy Reports 2022;8:81–98. https://doi.org/10.1016/j.egyr.2021.11.239.
[32]   Garlapati VK, Parashar SK, Klykov S, Vundavilli PR, Sevda S, Srivastava SK, et al. Invasive weed optimization coupled biomass and product dynamics of tuning soybean husk towards lipolytic enzyme. Bioresour Technol 2022;344:126254. https://doi.org/10.1016/j.biortech.2021.126254.
[33]   Calayir Y, Karaton M. A continuum damage concrete model for earthquake analysis of concrete gravity dam-reservoir systems. Soil Dyn Earthq Eng 2005;25:857–69. https://doi.org/10.1016/j.soildyn.2005.05.003.
[34]   Design of Gravity Dams. United States Department of the Interior Bureau of Reclamation (USBR). A Water Resour Tech Press Color 1976.
[35]   Ghodousi H, Oskouhi M. Optimization of optimal dimensions of concrete gravity dams using Honey Bee Mating (HBMO) model. Irrig Drain Struct Eng Res 2016;17:1–14. https://doi.org/10.22092/ARIDSE.2016.106407.
[36]   Abrishami J. Concrete Dams; Design and Performance. Astan-e-Qods Razavi Press, 2001.
[37]   Design of Small Dams. United States Department of the Interior Bureau of Reclamation (USBR). A Water Resour Tech Press Color 1987.

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History

  • Receive Date: 05 May 2022
  • Revise Date: 05 October 2022
  • Accept Date: 06 October 2022

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