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Reliability-Based Design Optimization of Detention Rockfill Dams and Investigation of the Effect of Uncertainty on Their Performance Using Meta-Heuristic Algorithm

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Artificial Intelligence in Mechatronics and Civil Engineering

Part of the book series: Emerging Trends in Mechatronics ((emerg. Trends in Mechatronics))

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Abstract

Flood is one of the natural disasters which is of particular importance due to the financial, human, and environmental damages which directly and indirectly inflicts on human societies. For this reason, researchers today have turned to appropriate solutions for flood management to reduce the effects of floods. One of the most suitable structural solutions is the construction of detention rockfill dams to control and mitigate flood damage. Such dams are very popular due to their rapid construction and easy operation. At first, for designing detention rockfill dams, one must select suitable locations for dams. In the second step, the preliminary design of the dam is done to obtain the height and length of the dam, and in the last step, the final design and optimization of the dam are done. In this research, the second and third design steps, i.e., the preliminary and final designs, are performed to obtain the initial height and length of the dam. Then the optimization of the dams is done to provide structural safety factors. For the preliminary design, the input hydrograph equations, the reservoir's volume-height relationship, the dam's stage-discharge equation, and the flow routing equation in the detention rockfill dams and their combination with each other are used. Metaheuristic algorithms are also used for the final design and optimization of the detention rockfill dam. In this research, a self-adaptive genetic algorithm has been used to optimize the dimensions of the detention rockfill dam. Then, using the Monte Carlo simulation method, the effects of uncertainty of design parameters on the hydraulic and structural performance of detention rockfill dam are investigated. It has been shown how uncertainty can change hydraulic performance by studing the dam storage volume and flow through the dam. The structural implementation is also evaluated due to the uncertainty propagation on the safety factors. At the end of this chapter, a reliability-based design optimization (RBDO) of the detention rockfill dam was carried out using self-adaptive NSGA-II.

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References

  1. Chuntian C, Chau KW (2002) Three-person multiobjective conflict decision in reservoir flood control. Eur J Oper Res 142(3):625–631

    Article  MATH  Google Scholar 

  2. Li XY, Chau KW, Cheng CT, Li YS (2006) A web-based flood forecasting system for Shuangpai region. Adv Eng Softw 37(3):146–158

    Article  Google Scholar 

  3. Wu CL, Chau KW (2006) A flood forecasting neural network model with genetic algorithm. Int J Environ Pollut 28(3–4):261–273

    Article  Google Scholar 

  4. Wang WC, Chau KW, Xu DM, Qiu L, Liu CC (2017) The annual maximum flood peak discharge forecasting using Hermite projection pursuit regression with SSO and LS method. Water Resour Manage 31(1):461–477

    Article  Google Scholar 

  5. Mosavi A, Ozturk P, Chau KW (2018) Flood prediction using machine learning models: literature review. Water 10(11):1536

    Article  Google Scholar 

  6. Yaseen ZM, Sulaiman SO, Deo RC, Chau KW (2019) An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408

    Article  Google Scholar 

  7. Samani HM, Samani JM, Shaiannejad M (2003) Reservoir routing using steady and unsteady flow through rockfill dams. J Hydraul Eng 129(6):448–454

    Article  Google Scholar 

  8. Hooshyaripor F, Tahershamsi A (2015) Effect of reservoir side slopes on dam-break flood waves. Eng Appl Comput Fluid Mech 9(1):458–468

    Google Scholar 

  9. Hooshyaripor F, Tahershamsi A, Razi S (2017) Dam break flood wave under different reservoir’s capacities and lengths. Sādhanā 42(9):1557–1569

    Article  Google Scholar 

  10. Akan AO (1990) Single-outlet detention-pond analysis and design. J Irrig Drain Eng 116(4):527–536

    Article  Google Scholar 

  11. McEnroe BM (1992) Preliminary sizing of detention reservoirs to reduce peak discharges. J Hydraul Eng 118(11):1540–1549

    Article  Google Scholar 

  12. Abt SR, Grigg NS (1978) An approximate method for sizing detention reservoirs 1. JAWRA J Am Water Resour Assoc 14(4):956–965

    Article  Google Scholar 

  13. Wycoff RL, Singh UP (1976) Preliminary hydrologic design of small flood detention reservoirs 1. JAWRA J Am Water Resour Assoc 12(2):337–349

    Article  Google Scholar 

  14. Akan AO (1989) Detention pond sizing for multiple return periods. J Hydraul Eng 115(5):650–664

    Article  Google Scholar 

  15. Akan AO, Al-Muttair FF and Al-Turbak AS (1987) Design aid for detention basins. Design of hydraulic structures. Proceedings international symposium, Fort Collins, Colorado, pp 177–182

    Google Scholar 

  16. Horn DR (1987) Graphic estimation of peak flow reduction in reservoirs. J Hydr Engrg ASCE 113(11):1441–1450

    Google Scholar 

  17. Baker WR (1977) Stormwater detention basin design for small drainage areas

    Google Scholar 

  18. Riahi-Madvar H, Dehghani M, Akib S, Shamshirband S, Chau KW (2019) Developing a mathematical framework in preliminary designing of detention rockfill dams for flood peak reduction. Eng Appl Comput Fluid Mech 13(1):1119–1129

    Google Scholar 

  19. Aksoy H (2000) Use of gamma distribution in hydrological analysis. Turk J Eng Environ Sci 24(6):419–428

    Google Scholar 

  20. Gray DM (1961) Synthetic unit hydrographs for small watersheds. J Hydraul Div 87(4):33–54

    Article  Google Scholar 

  21. Machajski J, Kostecki S (2018) Hydrological analysis of a dyke pumping station for the purpose of improving its functioning conditions. Water 10(6):737

    Article  Google Scholar 

  22. Nash JE (1959) Systematic determination of unit hydrograph parameters. J Geophys Res 64(1):111–115

    Article  Google Scholar 

  23. Bhunya PK, Ghosh NC, Mishra SK, Ojha CS, Berndtsson R (2005) Hybrid model for derivation of synthetic unit hydrograph. J Hydrol Eng 10(6):458–467

    Article  Google Scholar 

  24. Singh PK, Mishra SK, Jain MK (2014) A review of the synthetic unit hydrograph: from the empirical UH to advanced geomorphological methods. Hydrol Sci J 59(2):239–261

    Article  Google Scholar 

  25. Samani HMV, Samani JMV, Shaiannejad M (2003) Reservoir routing using steady and unsteady flow through rockfill dams. J Hydraul Eng ASCE 129(6):448–454

    Article  Google Scholar 

  26. Kshirsagar DY (2014) Effect of variation of earthquake intensity on stability of gravity dam. J Indian Water Resour Soc. 34(3):1–6

    Google Scholar 

  27. Deepika R, Suribabu CR (2015) Optimal design of gravity dam using differential evolution algorithm. Iran Univ Sci Technol 5(3):255–266

    Google Scholar 

  28. Punmia BC (1992) Irrigation and water power engineering. Laxmi Publications Pvt Limited

    Google Scholar 

  29. Moradi Kia F, Ghafouri HR, Riyahi MM (2022) Uncertainty analysis and risk identification of the gravity dam stability using fuzzy set theory. J Hydraul Struct 7(4):76–92

    Google Scholar 

  30. Riyahi MM, Bahrami Chegeni I (2022) Gravity retaining wall stability risk analysis based on reliability using fuzzy set theory. J Struct Constr Eng

    Google Scholar 

  31. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  32. Lin YK, Yeh CT (2012) Multi-objective optimization for stochastic computer networks using NSGA-II and TOPSIS. Eur J Oper Res 218(3):735–746

    Article  MathSciNet  MATH  Google Scholar 

  33. Riyahi MM, Riahi-Madvar H (2022) Uncertainty analysis in probabilistic design of detention rockfill dams using Monte-Carlo simulation model and probabilistic frequency analysis of stability factors. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-24037-x

  34. Mays LW (2010) Water resources engineering. Wiley

    Google Scholar 

  35. Nowak AS, Collins KR (2012) Reliability of structures. CRC Press

    Book  Google Scholar 

  36. Rashki M (2018) Hybrid control variates-based simulation method for structural reliability analysis of some problems with low failure probability. Appl Math Model 60:220–234

    Article  MathSciNet  MATH  Google Scholar 

  37. Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Stat Assoc 44:335–341

    Article  MATH  Google Scholar 

  38. Rajabi MM, Ataie-Ashtiani B, Janssen H (2015) Efficiency enhancement of optimized Latin hypercube sampling strategies: application to Monte Carlo uncertainty analysis and meta-modeling. Adv Water Resour 76:127–139

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Mohammad Mehdi Riyahi

  2. Department of Water Engineering, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

    Hossien Riahi-Madvar

  3. Faculty of Engineering, Lorestan University, Khorramabad, Iran

    Iman Bahrami Chegeni

Authors
  1. Mohammad Mehdi Riyahi
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  2. Hossien Riahi-Madvar
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  3. Iman Bahrami Chegeni
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Correspondence to Iman Bahrami Chegeni .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran

    Ehsan Momeni

  2. Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW, Australia

    Danial Jahed Armaghani

  3. School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford, UK

    Aydin Azizi

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Riyahi, M.M., Riahi-Madvar, H., Bahrami Chegeni, I. (2023). Reliability-Based Design Optimization of Detention Rockfill Dams and Investigation of the Effect of Uncertainty on Their Performance Using Meta-Heuristic Algorithm. In: Momeni, E., Jahed Armaghani, D., Azizi, A. (eds) Artificial Intelligence in Mechatronics and Civil Engineering. Emerging Trends in Mechatronics. Springer, Singapore. https://doi.org/10.1007/978-981-19-8790-8_8

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  • DOI: https://doi.org/10.1007/978-981-19-8790-8_8

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