Abstract
In the present study, the estimation of vertical settlement of earthen dams caused by earthquakes has been investigated using an artificial neural network model and wavelet-artificial neural network combination. The accuracy of the methods used based on the crest settlement data obtained from 13 types of dams and a total of 151 earth dams with eight series of data was considered as input parameter and one output parameter. From a total of 151 earth dams, 121 samples were randomly selected and used for network training. Also, network validation was performed with 29 data sets, and the remaining sample was considered to evaluate the method's applicability. The results showed that the rbior6.8 wavelet function had the highest accuracy and best performance with a correlation coefficient of 83% and RMSE, 0.044, and the dmey wavelet function showed the lowest accuracy and the weakest performance with a correlation coefficient of 70% and 0.162, RMSE. Also, the Sym4 function with the correlation coefficient of 81% and the RMSE, of 0.55 provided the best performance after the rbio6.8 wavelet function in the second place. After Sym4, bior6.8 function with 78% accuracy, haar function with 77% accuracy, coif2 function with 73% accuracy, db4 function with 72% accuracy and finally dmey function with 70% accuracy. The proposed model with 2.75% error has a more accurate prediction than finite element methods with 19.92% and 4.06%. In total, the results of this combined model are considered suitable because the correlation coefficient varied between 70 and 83 percent.
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Change history
18 May 2023
A Correction to this paper has been published: https://doi.org/10.1007/s10706-023-02479-5
References
Ahbab A, Akhlaghi T, Safari MJS, Avci E (2021) Evaluation of the static and pseudo-static stability and effectiveness of an improvement technique for slopes of the Vanyar Dam reservoir. KSCE J Civ Eng 25(2):468–481
Andrus RD, Stokoe KH (2000) Liquefaction resistance of soils from shear-wave velocity. J Geotech Geoenviron Eng 126(11):1015–1025. https://doi.org/10.1061/(ASCE)1090-0241(2000)126:11(1015)
Barkhordari K, Entezari ZH (2015) Prediction of permanent earthquake-induced deformation in earth dams and embankments using artificial neural networks. Civil Eng Infrastruct J 48(2):271–283
Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3–31. https://doi.org/10.1016/S0167-7012(00)00201-3
Bhattacharyya S (2015) Handbook of research on advanced hybrid intelligent techniques and applications. IGI Global. https://doi.org/10.4018/978-1-4666-9474-3
Cascone E, Rampello S (2003) Decoupled seismic analysis of an earth dam. Soil Dyn Earthq Eng 23(5):349–365. https://doi.org/10.1016/S0267-7261(03)00035-6
Chatfield C (2018) Introduction to multivariate analysis. Routledge
Christopher Frey H, Patil SR (2002) Identification and review of sensitivity analysis methods. Risk Anal 22(3):553–578
Daneshfaraz R, Abam M, Heidarpour M, Abbasi S, Seifollahi M, Abraham J (2021) The impact of cables on local scouring of bridge piers using experimental study and ANN. ANFIS Algorithms Water Supply 22(1):1075–1093. https://doi.org/10.2166/ws.2021.215
Egbueri JC (2021) Use of joint supervised machine learning algorithms in assessing the geotechnical peculiarities of erodible tropical soils from southeastern Nigeria. Geomech Geoeng. https://doi.org/10.1080/17486025.2021.2006803
Fellenius W (1936) Calculation of stability of earth dam. In Transactions. 2nd Congress Large Dams, Washington, DC. 4:445–462. https://doi.org/10.1007/978-3-662-07680-4_5
Gordan B, Raja MA, Armaghani DJ, Adnan A (2022) Review on dynamic behaviour of earth dam and embankment during an earthquake. Geotech Geol Eng 40(1):3–33. https://doi.org/10.1007/s10706-021-01919-4
Happ C, Greven S (2018) Multivariate functional principal component analysis for data observed on different (dimensional) domains. J Am Stat Assoc 113(522):649–659. https://doi.org/10.1080/01621459.2016.1273115
Hariri-Ardebili MA, Pourkamali-Anaraki F (2018) Support vector machine based reliability analysis of concrete dams. Soil Dyn Earthq Eng 104:276–295. https://doi.org/10.1201/9780429499661
Hariri-Ardebili MA, Salazar F (2020) Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems. Soft Comput 24(15):11583–11604. https://doi.org/10.1007/s00500-019-04623-x
Hertz JA (2018) Introduction to the theory of neural computation. CRC Press. https://doi.org/10.1201/9780429499661
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647
Nikkhakian B, Alembagheri M, Shayan RS (2020) Parametric investigation of canyon shape effects on the seismic response of 3D concrete gravity dam model. Geotech Geol Eng 38:6755–6771. https://doi.org/10.1007/s10706-020-01467-3
Rampello S, Cascone E, Grosso N (2009) Evaluation of the seismic response of a homogeneous earth dam. Soil Dyn Earthq Eng 29(5):782–798. https://doi.org/10.1016/j.soildyn.2008.08.006
Rankovic V, Grujović N, Divac D, Milivojević N (2014) Development of support vector regression identification model for prediction of dam structural behaviour. Struct Saf 48:33–39. https://doi.org/10.1016/j.strusafe.2014.02.004
Rubio-Bellido C, Pérez-Fargallo A, Pulido-Arcas J (2018) Artificial neural networks, energy optimization and prediction in office buildings: a case study of office building design in Chile, Springer, pp 65–78, https://doi.org/10.1016/j.energy.2016.12.022
Salazar F, Toledo MA, Oñate E, Morán R (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9–17. https://doi.org/10.1016/j.strusafe.2015.05.001
Saltelli A, Chan K, Scott EM (2000) Sensitivity analysis. vol 1, Wiley New York, pp 770–771. ISBN: 978-0-470-74382-9
Seed HB (1967) Earthquake-resistant design of earth dams. Can Geotech J 4(1):1–27
Seed HB (1979a) Considerations in the earthquake-resistant design of earth and rockfill dams. Geotechnique 29(3):215–263
Seed HB (1979b) Soil liquefaction and cyclic mobility evalution for level ground during earthquakes. J Geotech Eng Div 105(2):201–255
Seifollahi M, Lotfollahi-Yaghin MA, Kalateh F, Daneshfaraz R, Abbasi S, Abraham J (2021) Estimation of the local scour from a cylindrical bridge pier using a compilation wavelet model and artificial neural network. J Hydraul Struct 7(3):1–22. https://doi.org/10.22055/jhs.2021.38300.1187
Seifollahi M, Abbasi S, Pourtaghi A, Daneshfaraz R, Abraham J, Parvaresh M, Alkan A (2022a) Performance efficiency of data-based hybrid intelligent approaches to predict crest settlement in rockfill dams. Arab J Geosci 15(23):1701. https://doi.org/10.1007/s12517-022-11005-5
Seifollahi M, Abbasi S, Lotfollahi-yaghin MA, Daneshfaraz R, Kalateh F, Fahimi-Farzam M (2022b) Investigation of the performance of artificial intelligence methods in estimating the crest settlement of rockfill dam with a central core. JWSS-Isfahan Univ Technol 26(2):119–134. https://doi.org/10.47176/jwss.26.2.37954
Seifollahi M, Abbasi S, Mohammadi F, Danehfaraz R, Asemi B (2022c) Prediction of crest settlement in rock-fill dams using ANN and ANFIS. In: 12th International River Engineering Conference Shahid Chamran University of Ahvaz, 24–26 Jan, Ahvaz, Iran
Seifollahi M, Abbasi S, Abraham J, Norouzi R, Daneshfaraz R, Lotfollahi-Yaghin MA, Alkan A (2022d) Optimization of gravity concrete dams using the grasshopper algorithm (case study: Koyna Dam). Geotech Geol Eng 40(11):5481–5496. https://doi.org/10.1007/s10706-022-02227-1
Shakouri B, Mohammadi M (2020) Evaluation of penetration depth for cutoff walls in the core of earth dams. Geotech Geol Eng 38(1):151–167. https://doi.org/10.1007/s10706-019-01004-x
Shi L, Tang X, Lv J (2015) PCA-based PSO-BP neural network optimization algorithm. In: The 27th Chinese Control and Decision Conference (2015 CCDC), pp 1720–1725
Souza AN, Silva AR, Santos OF Jr, Freitas Neto O (2022) Settlement analysis of Açu Dam, Brazil. Geotech Geol Eng 40(3):1565–1583. https://doi.org/10.1007/s10706-021-01952-3
Stojanovic B, Milivojevic M, Ivanovic M, Milivojevic N, Divac D (2013) Adaptive system for dam behavior modeling based on linear regression and genetic algorithms. Adv Eng Softw 65:182–190. https://doi.org/10.1016/j.advengsoft.2013.06.019
Su H, Li X, Yang B, Wen Z (2018) Wavelet support vector machine-based prediction model of dam deformation. Mech Syst Signal Process 110:412–427. https://doi.org/10.1016/j.ymssp.2018.03.022
Tani S, Nakashima M (1999) Earthquake damage to earth dams in Japan—maximum epicentral distance to cause damage as a function of magnitude. Soil Dyn Earthq Eng 18(8):593–602. https://doi.org/10.1016/S0267-7261(99)00017-2
Wu G (2001) Earthquake-induced deformation analyses of the Upper San Fernando Dam under the 1971 San Fernando earthquake. Can Geotech J 38(1):1–15
Yu Y, Xie L, Zhang B (2005) Stability of earth–rockfill dams: influence of geometry on the three-dimensional effect. Comput Geotech 32(5):326–339. https://doi.org/10.1016/j.compgeo.2005.03.003
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Abbasi, S., Seifollahi, M., Daneshfaraz, R. et al. Estimation of Vertical Settlement of Earthen Dams Caused by Earthquake Using ANN Model and Wavelet-ANN Composition. Geotech Geol Eng 41, 3169–3186 (2023). https://doi.org/10.1007/s10706-023-02451-3
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DOI: https://doi.org/10.1007/s10706-023-02451-3