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Geotechnical and Geological Engineering

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25.03.2023 | Original Paper

Estimation of Seepage Flow Using Optimized Artificial Intelligent Models

verfasst von: Issam Rehamnia, Bachir Benlaoukli, Mustafa Chouireb, Indra Prakash, Mahdis Amiri, Binh Thai Pham

Erschienen in: Geotechnical and Geological Engineering | Ausgabe 4/2023

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Abstract

Estimation of seepage flow through earthen dam is very important for evaluating stability and safety of the structure. In the present study, we have developed novel optimized artificial intelligent models namely ANN-GA and ANN-BBO which are combinations of Biogeography-Based Optimization (BBO) and Genetic Algorithm (GA) and Artificial Neural Network (ANN) algorithm for the estimation of seepage flow through Karmis earthen dam, Algeria. For the development of models, thirteen years’ period (2006–2018) water level data of the reservoir and dam gallery was used as input data and actual seepage measurement data as output data. Standard statistical measures namely Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient (R) were used to evaluate performance of ANN-GA and ANN-BBO models. Results indicated that performance of both the developed hybrid models is very good but of ANN-BBO model (R: 0.989) is slightly better in comparison to ANN-GA model (R: 0.987) in accurately predicting seepage flow of earthen dam. Thus, proposed optimized artificial intelligence models, especially ANN-BBO can be used for correctly estimating seepage of earthen dams.

Vorheriger Artikel Coefficient of Earth Pressure at Rest of Light Weight Soil Under Complex Loading

Nächster Artikel Study on Failure Mechanism and Soil Resistance for Laterally-Loaded Large-Diameter Monopiles

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Adnan RM, Malik A, Kumar A, Parmar KS, Kisi O (2019) Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arab J Geosci 12(19):606. https://doi.org/10.1007/s12517-019-4781-6CrossRef

Ali M, Deo RC, Maraseni T, Downs NJ (2019) Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms. J Hydrol 576:164–184. https://doi.org/10.1016/j.jhydrol.2019.06.032CrossRef

Al-Janabi AMS, Ghazali AH, Ghazaw YM, Afan HA, Al-Ansari N, Yaseen ZM (2020) Experimental and numerical analysis for earth-fill dam seepage. Sustainability 12(6):2490CrossRef

Athani SS, Shivamanth SCH, Dodagoudar GR (2015) Seepage and stability analyses of earth dam using finite element method. Aquatic Procedia 4:876–883. https://doi.org/10.1016/j.aqpro.2015.02.110CrossRef

Bekele B, Song C, Eun J, Kim S (2022) Exploratory seepage detection in a laboratory-scale earthen dam based on distributed temperature sensing method. Geotechnical and Geological Engineering:1–16

Bewes J, Low A, Morphett A, Pate FD, Henneberg M (2019) Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls. J Forensic Leg Med 62:40–43. https://doi.org/10.1016/j.jflm.2019.01.004CrossRef

Bièvre G, Lacroix P, Oxarango L, Goutaland D, Monnot G, Fargier Y (2017) Integration of geotechnical and geophysical techniques for the characterization of a small earth-filled canal dyke and the localization of water leakage. J Appl Geophys 139:1–15. https://doi.org/10.1016/j.jappgeo.2017.02.002CrossRef

Blair G, Imai K (2012) Statistical analysis of list experiments. Polit Anal 20(1):47–77. https://doi.org/10.1093/pan/mpr048CrossRef

Chahar BR (2004) Determination of length of a horizontal drain in homogeneous earth dams. J Irrig Drain Eng 130(6):530–536. https://doi.org/10.1061/(ASCE)0733-9437(2004)130:6(530)CrossRef

Chouireb M, Djehiche A (2019) Simulation of seepage flow through an earthen dam with vertical drain and comparison of results with observations data (case study: Harreza dam-Algeria). Arab J Geosci 12(13):406. https://doi.org/10.1007/s12517-019-4542-6

Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). CATENA 113:236–250. https://doi.org/10.1016/j.catena.2013.08.006CrossRef

Dane AD, Veldhuis A, Boer DKGd, Leenaers AJG, Buydens LMC (1998) Application of genetic algorithms for characterization of thin layered materials by glancing incidence X-ray reflectometry. Phys B 253(3):254–268. https://doi.org/10.1016/S0921-4526(98)00398-6CrossRef

Ehteram M, Ferdowsi A, Faramarzpour M, Al-Janabi AMS, Al-Ansari N, Bokde ND, Yaseen ZM (2021) Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis. Alex Eng J 60(2):2193–2208. https://doi.org/10.1016/j.aej.2020.12.034CrossRef

Fallah A, Jabbari E, Babaee R (2019) Development of the Kansa method for solving seepage problems using a new algorithm for the shape parameter optimization. Comput Math Appl 77(3):815–829. https://doi.org/10.1016/j.camwa.2018.10.021CrossRef

Fatemi Aghda SA, Mirfakhraei M (2020) Improved routing in dynamic environments with moving obstacles using a hybrid Fuzzy-Genetic algorithm. Futur Gener Comput Syst 112:250–257. https://doi.org/10.1016/j.future.2020.05.024CrossRef

Fayed M, Elhadary M, Ait Abderrahmane H, Zakher BN (2019) The ability of forecasting flapping frequency of flexible filament by artificial neural network. Alex Eng J 58(4):1367–1374. https://doi.org/10.1016/j.aej.2019.11.007CrossRef

Fell R, Wan CF, Cyganiewicz J, Foster M (2003) Time for development of internal erosion and piping in embankment dams. J Geotech Geoenviron Eng 129(4):307–314. https://doi.org/10.1061/(ASCE)1090-0241(2003)129:4(307)CrossRef

Fisher WD, Camp TK, Krzhizhanovskaya VV (2016) Crack detection in earth dam and levee passive seismic data using support vector machines. Procedia Comput Sci 80:577–586. https://doi.org/10.1016/j.procs.2016.05.339CrossRef

Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99. https://doi.org/10.1023/A:1022602019183CrossRef

Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning.

Hammouri AI (2020) A modified biogeography-based optimization algorithm with guided bed selection mechanism for patient admission scheduling problems. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.01.013CrossRef

Hilali-Jaghdam I, Ben Ishak A, Abdel-Khalek S, Jamal A (2020) Quantum and classical genetic algorithms for multilevel segmentation of medical images: a comparative study. Comput Commun 162:83–93. https://doi.org/10.1016/j.comcom.2020.08.010CrossRef

Jie Y-x, Liu L-z, Xu W-j, Li G-x (2013) Application of NEM in seepage analysis with a free surface. Math Comput Simul 89:23–37. https://doi.org/10.1016/j.matcom.2013.03.006CrossRef

Jing T, Yongbiao L (2012) Penalty function element free method to solve complex seepage field of earth fill dam. IERI Procedia 1:117–123. https://doi.org/10.1016/j.ieri.2012.06.019CrossRef

Karakatič S, Podgorelec V (2015) A survey of genetic algorithms for solving multi depot vehicle routing problem. Appl Soft Comput 27:519–532. https://doi.org/10.1016/j.asoc.2014.11.005CrossRef

Karthikeyan P, Baskar S (2015) Genetic algorithm with ensemble of immigrant strategies for multicast routing in Ad hoc networks. Soft Comput 19(2):489–498. https://doi.org/10.1007/s00500-014-1269-xCrossRef

Kaveh A, Kamalinejad M, Biabani Hamedani K (2021) Enhanced versions of the shuffled shepherd optimization algorithm for the optimal design of skeletal structures. Structures 29:1463–1495. https://doi.org/10.1016/j.istruc.2020.12.032CrossRef

Liemohn MW, Shane AD, Azari AR, Petersen AK, Swiger BM, Mukhopadhyay A (2021) RMSE is not enough: guidelines to robust data-model comparisons for magnetospheric physics. J Atmos Solar-Terr Phys. https://doi.org/10.1016/j.jastp.2021.105624CrossRef

Liu X, Zhang H, Liu Q, Dong S, Xiao C (2021) A cross-entropy algorithm based on Quasi-Monte Carlo estimation and its application in hull form optimization. Int J Naval Archit Ocean Eng 13:115–125. https://doi.org/10.1016/j.ijnaoe.2021.01.001CrossRef

Lu YL, Chen WF, Wang ET, Guan SH, Yan XR, Chen WX (2009) Genetic diversity and biogeography of rhizobia associated with Caragana species in three ecological regions of China. Syst Appl Microbiol 32(5):351–361. https://doi.org/10.1016/j.syapm.2008.10.004CrossRef

Lucchese LV, de Oliveira GG, Pedrollo OC (2021) Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using artificial neural networks. CATENA 198:105067. https://doi.org/10.1016/j.catena.2020.105067CrossRef

Ma S, Tong L, Ye F, Xiao J, Bénard P, Chahine R (2019) Hydrogen purification layered bed optimization based on artificial neural network prediction of breakthrough curves. Int J Hydrogen Energy 44(11):5324–5333. https://doi.org/10.1016/j.ijhydene.2018.12.142CrossRef

Mentaschi L, Besio G, Cassola F, Mazzino A (2013) Problems in RMSE-based wave model validations. Ocean Model 72:53–58. https://doi.org/10.1016/j.ocemod.2013.08.003CrossRef

Miao XY, Chu JK, Qiao J, Zhang LH (2012) Predicting seepage of earth dams using neural network and genetic algorithm. Adv Mater Res 403–408:3081–3085. https://doi.org/10.4028/www.scientific.net/AMR.403-408.3081CrossRef

Mouyeaux A, Carvajal C, Bressolette P, Peyras L, Breul P, Bacconnet C (2019) Probabilistic analysis of pore water pressures of an earth dam using a random finite element approach based on field data. Eng Geol 259:105190. https://doi.org/10.1016/j.enggeo.2019.105190CrossRef

Nguyen DD, Roussis PC, Pham BT, Ferentinou M, Mamou A, Vu DQ, Bui Q-AT, Trong DK, Asteris PG (2022a) Bagging and multilayer perceptron hybrid intelligence models predicting the swelling potential of soil. Trans Geotech 36:100797CrossRef

Nguyen TT, Nguyen DD, Nguyen SD, Prakash I, Van Tran P, Pham BT (2022b) Forecasting construction price index using artificial intelligence models: support vector machines and radial basis function neural network. Journal of Science and Transport Technology:9–19

Nourani V, Aminfar MH, Alami MT, Sharghi E, Singh VP (2014) Unsteady 2-D seepage simulation using physical analog, case of Sattarkhan embankment dam. J Hydrol 519:177–189. https://doi.org/10.1016/j.jhydrol.2014.07.011CrossRef

Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759. https://doi.org/10.1016/j.envsoft.2009.10.016CrossRef

Prakash I, Pancholi DM (2008) Geotechnical Assessment and Evaluation of the Impact of Kachchh (Bhuj) India 2001 Earthquake. International Conference on Case Histories in Geotechnical Engineering. 11:1–12. https://scholarsmine.mst.edu/icchge/6icchge/session03/11/

Reeves C (2003) Genetic algorithms. In: Glover F, Kochenberger GA (eds) Handbook of Metaheuristics. Springer US, Boston, MA, pp 55–82. https://doi.org/10.1007/0-306-48056-5_3

Rehamnia I, Benlaoukli B, Heddam S (2020) Modeling of seepage flow through concrete face rockfill and embankment dams using three heuristic artificial intelligence approaches: a comparative study. Environ Process 7(1):367–381. https://doi.org/10.1007/s40710-019-00414-6CrossRef

Rehamnia I, Benlaoukli B, Jamei M, Karbasi M, Malik A (2021) Simulation of seepage flow through embankment dam by using a novel extended Kalman filter based neural network paradigm: case study of Fontaine Gazelles Dam. Algeria Meas 176:109219CrossRef

Richards KS, Reddy KR (2007) Critical appraisal of piping phenomena in earth dams. Bull Eng Geol Env 66(4):381–402. https://doi.org/10.1007/s10064-007-0095-0CrossRef

Roushangar K, Garekhani S, Alizadeh F (2016a) Forecasting daily seepage discharge of an earth dam using wavelet–mutual information–Gaussian process regression approaches. Geotech Geol Eng 34:1313–1326CrossRef

Roushangar K, Garekhani S, Alizadeh F (2016b) Forecasting daily seepage discharge of an earth dam using wavelet-mutual information–Gaussian process regression approaches. Geotech Geol Eng 34(5):1313–1326. https://doi.org/10.1007/s10706-016-0044-4CrossRef

Saleh LA (2018) Studying the seepage phenomena under a concrete dam using SEEP/W and artificial neural network models. IOP Conf Series Mater Sci Eng 433:012029. https://doi.org/10.1088/1757-899x/433/1/012029CrossRef

Salmasi F, Nouri M (2019) Effect of upstream semi-impervious blanket of embankment dams on seepage. ISH J Hydraul Eng 25(2):143–152. https://doi.org/10.1080/09715010.2017.1381862CrossRef

Salmasi F, Norouzi R, Abraham J, Nourani B, Samadi S (2020) Effect of inclined clay core on embankment dam seepage and stability through LEM and FEM. Geotech Geol Eng 38:6571–6586CrossRef

Sharghi E, Nourani V, Behfar N (2018) Earthfill dam seepage analysis using ensemble artificial intelligence based modeling. J Hydroinf 20(5):1071–1084. https://doi.org/10.2166/hydro.2018.151CrossRef

Simon D (2008) Biogeography-Based Optimization. IEEE Trans Evol Comput 12(6):702–713. https://doi.org/10.1109/TEVC.2008.919004CrossRef

Soueid Ahmed A, Revil A, Steck B, Vergniault C, Jardani A, Vinceslas G (2019) Self-potential signals associated with localized leaks in embankment dams and dikes. Eng Geol 253:229–239. https://doi.org/10.1016/j.enggeo.2019.03.019CrossRef

Su H, Li J, Wen Z, Guo Z, Zhou R (2019) Integrated certainty and uncertainty evaluation approach for seepage control effectiveness of a gravity dam. Appl Math Model 65:1–22. https://doi.org/10.1016/j.apm.2018.07.004CrossRef

Szczepanski R, Tarczewski T, Grzesiak LM (2021) Application of optimization algorithms to adaptive motion control for repetitive process. ISA Trans. https://doi.org/10.1016/j.isatra.2021.01.007CrossRef

Thai PB, Nguyen DD, Thi Q-AB, Nguyen MD, Vu TT, Prakash I (2022) Estimation of load-bearing capacity of bored piles using machine learning models. Science of the Earth 44 (4)

Tharwat A, Schenck W (2021) A conceptual and practical comparison of PSO-style optimization algorithms. Expert Syst Appl 167:114430. https://doi.org/10.1016/j.eswa.2020.114430CrossRef

Tien Bui D, Bui Q-T, Nguyen Q-P, Pradhan B, Nampak H, Trinh PT (2017) A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric for Meteorol 233:32–44. https://doi.org/10.1016/j.agrformet.2016.11.002CrossRef

Vu DQ, Nguyen DD, Bui Q-AT, Trong DK, Prakash I, Pham BT (2021) Estimation of California bearing ratio of soils using random forest based machine learning. Journal of Science and Transport Technology pp 48–61

Wang T, Chen J, Li P, Yin Y, Shen C (2019) Natural tracing for concentrated leakage detection in a rockfill dam. Eng Geol 249:1–12. https://doi.org/10.1016/j.enggeo.2018.12.018CrossRef

Wei B, Gu M, Li H, Xiong W, Xu Z (2018) Modeling method for predicting seepage of RCC dams considering time-varying and lag effect. Struct Cont Health Monit 25(2):e2081. https://doi.org/10.1002/stc.2081CrossRef

Wu D, Ren F, Qiao L, Zhang W (2018) Active disturbance rejection controller design for dynamically positioned vessels based on adaptive hybrid biogeography-based optimization and differential evolution. ISA Trans 78:56–65. https://doi.org/10.1016/j.isatra.2017.10.010CrossRef

Yang B, Cheng W, Hu X, Zhu C, Yu X, Li X, Huang T (2019) Seeking community structure in networks via biogeography-based optimization with consensus dynamics. Phys A Stat Mech Appl 527:121188. https://doi.org/10.1016/j.physa.2019.121188CrossRef

Yaseen ZM, Ameen AMS, Aldlemy MS, Ali M, Abdulmohsin Afan H, Zhu S, Sami Al-Janabi AM, Al-Ansari N, Tiyasha T, Tao H (2020) State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations. Sustainability 12(4):1676CrossRef

Ye M, Qiu H, Wang Y, Zhou Z, Zheng F, Ma T (2020) A method of repairing single node failure in the distributed storage system based on the regenerating-code and a hybrid genetic algorithm. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.11.124CrossRef

Yuan S, Zhong H (2016) Three dimensional analysis of unconfined seepage in earth dams by the weak form quadrature element method. J Hydrol 533:403–411. https://doi.org/10.1016/j.jhydrol.2015.12.034CrossRef

Zhang X, Wang D, Fu Z, Liu S, Mao W, Liu G, Jiang Y, Li S (2020) Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation. Appl Math Model 86:74–91. https://doi.org/10.1016/j.apm.2020.05.016CrossRef

Zhang Y, Gu X (2020) Biogeography-based optimization algorithm for large-scale multistage batch plant scheduling. Expert Syst Appl 162:113776. https://doi.org/10.1016/j.eswa.2020.113776CrossRef

Zhu P, Leng YB, Zhou Y, Jiang GL (2011) Safety inspection strategy for earth embankment dams using fully distributed sensing. Procedia Eng 8:520–526. https://doi.org/10.1016/j.proeng.2011.03.094CrossRef

Titel: Estimation of Seepage Flow Using Optimized Artificial Intelligent Models
verfasst von: Issam Rehamnia
Bachir Benlaoukli
Mustafa Chouireb
Indra Prakash
Mahdis Amiri
Binh Thai Pham
Publikationsdatum: 25.03.2023
Verlag: Springer International Publishing
Erschienen in: Geotechnical and Geological Engineering / Ausgabe 4/2023
Print ISSN: 0960-3182
Elektronische ISSN: 1573-1529
DOI: https://doi.org/10.1007/s10706-023-02423-7

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