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International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-8 Issue-75 February-2021
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Paper Title : Artificial neural networks in slope of road embankment stability applications: a review and future perspectives
Author Name : Rufaizal Che Mamat, Azuin Ramli, Abd Manan Samad, Anuar Kasa, Siti Fatin Mohd Razali and Mohd Badrul Hafiz Che Omar
Abstract :

The artificial or neural network is one of the branches of the artificial intelligence method. Over the last few decades, artificial neural networks (ANNs) have been widely used to predict embankment stability. This paper will provide a detailed review of the ANN application, which is multilayer feedforward neural networks (MLFNN) in road embankment stability. A proposal for further research needs in this area is also discussed. Due to its acceptable accuracy prediction, the ANN model is widely recognized as a successful embankment stability approach. Based on the findings of this paper, it will be able to pave the way for researchers to use the ANN in predicting the stability of road embankment comprehensively.
Keywords : Artificial neural network, Multilayer feedforward neural networks, Road embankment, Slope stability, Prediction.
Cite this article : Mamat RC, Ramli A, Samad AM, Kasa A, Razali SF, Omar MB. Artificial neural networks in slope of road embankment stability applications: a review and future perspectives. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(75):304-319. DOI:10.19101/IJATEE.2020.762127.
References :
[1]Che Mamat R. Engineering properties of Batu pahat soft clay stabilized with lime, cement and bentonite for subgrade in road construction (Doctoral dissertation, Universiti Tun Hussein Onn Malaysia). 2013: 1-129.
[Google Scholar]
[2]Mamat RC, Ramli A, Samad AM, Kasa A, Razali SF, Omar MC. Stability assessment of embankment on soft soil improved with prefabricated vertical drains using empirical and limit equilibrium approaches. International Journal of Advanced Trends in Computer Science and Engineering. 2019; 8(1.6 Sp):444-9.
[Google Scholar]
[3]Chik Z, Aljanabi QA, Kasa A, Taha MR. Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment. Arabian Journal of Geosciences. 2014; 7(11):4877-87.
[Crossref] [Google Scholar]
[4]Kanayama M, Rohe A, van Paassen LA. Using and improving neural network models for ground settlement prediction. Geotechnical and Geological Engineering. 2014; 32(3):687-97.
[Crossref] [Google Scholar]
[5]Chik Z, Aljanabi QA. Intelligent prediction of settlement ratio for soft clay with stone columns using embankment improvement techniques. Neural Computing and Applications. 2014; 25(1):73-82.
[Crossref] [Google Scholar]
[6]Bi QT, Ding SY. Neural networks model for settlement prediction of embankment. In applied mechanics and materials 2012 (pp. 722-6). Trans Tech Publications Ltd.
[Crossref] [Google Scholar]
[7]Li XY, Bu FJ. Prediction of settlement of soft clay foundation in highway using artifical neural networks. In advanced materials research 2012 (pp. 15-20). Trans Tech Publications Ltd.
[Crossref] [Google Scholar]
[8]Wang ZL, Li YC, Shen RF. Correction of soil parameters in calculation of embankment settlement using a BP network back-analysis model. Engineering Geology. 2007; 91(2-4):168-77.
[Crossref] [Google Scholar]
[9]Hasanzadehshooiili H, Mahinroosta R, Lakirouhani A, Oshtaghi V. Using artificial neural network (ANN) in prediction of collapse settlements of sandy gravels. Arabian Journal of Geosciences. 2014; 7(6):2303-14.
[Crossref] [Google Scholar]
[10]Kurnaz TF, Dagdeviren U, Yildiz M, Ozkan O. Prediction of compressibility parameters of the soils using artificial neural network. SpringerPlus. 2016; 5(1):1-11.
[Crossref] [Google Scholar]
[11]Dinçer İ. Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures. Advances in Engineering Software. 2011; 42(4):160-71.
[Crossref] [Google Scholar]
[12]Goh AT, Zhang RH, Wang W, Wang L, Liu HL, Zhang WG. Numerical study of the effects of groundwater drawdown on ground settlement for excavation in residual soils. Acta Geotechnica. 2020; 15(5):1259-72.
[Crossref] [Google Scholar]
[13]Chok YH, Jaksa MB, Kaggwa WS, Griffiths DV, Fenton GA. Neural network prediction of the reliability of heterogeneous cohesive slopes. International Journal for Numerical and Analytical Methods in Geomechanics. 2016; 40(11):1556-69.
[Crossref] [Google Scholar]
[14]Sakellariou MG, Ferentinou MD. A study of slope stability prediction using neural networks. Geotechnical & Geological Engineering. 2005; 23(4):419-45.
[Crossref] [Google Scholar]
[15]Mamat RC, Kasa A, Razali SF, Ramli A, Omar MB. Slope stability prediction of road embankment on soft ground treated with prefabricated vertical drains using artificial neural network. IAES International Journal of Artificial Intelligence. 2020; 9(2):236-43.
[Crossref] [Google Scholar]
[16]Erzin Y, Cetin T. The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Computers & Geosciences. 2013; 51:305-13.
[Crossref] [Google Scholar]
[17]Erzin Y, Cetin T. The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions. Geomechanics & Engineering. 2014; 6(1):1-5.
[Google Scholar]
[18]Abdalla JA, Attom MF, Hawileh R. Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network. Environmental Earth Sciences. 2015; 73(9):5463-77.
[Crossref] [Google Scholar]
[19]Verma AK, Singh TN, Chauhan NK, Sarkar K. A hybrid FEM–ANN approach for slope instability prediction. Journal of The Institution of Engineers (India): Series A. 2016; 97(3):171-80.
[Crossref] [Google Scholar]
[20]Gao W, Raftari M, Rashid AS, Mu’azu MA, Jusoh WA. A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes. Engineering with Computers. 2020; 36(1):325-44.
[Crossref] [Google Scholar]
[21]Kayadelen C, Günaydın O, Fener M, Demir A, Özvan A. Modeling of the angle of shearing resistance of soils using soft computing systems. Expert Systems with Applications. 2009; 36(9):11814-26.
[Crossref] [Google Scholar]
[22]Park HI. Development of neural network model to estimate the permeability coefficient of soils. Marine Georesources & Geotechnology.2011; 29(4):267-78.
[Crossref] [Google Scholar]
[23]Islam SM, Chik Z, Mustafa M, Sanusi H. Model with artificial neural network to predict the relationship between the soil resistivity and dry density of compacted soil. Journal of Intelligent & Fuzzy Systems. 2013; 25(2):351-7.
[Crossref] [Google Scholar]
[24]Viji VK, Lissy KF, Sobha C, Benny MA. Predictions on compaction characteristics of fly ashes using regression analysis and artificial neural network analysis. International Journal of Geotechnical Engineering. 2013; 7(3):282-91.
[Crossref] [Google Scholar]
[25]Kanungo DP, Sharma S, Pain A. Artificial neural network (ANN) and regression tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters. Frontiers of Earth Science. 2014; 8(3):439-56.
[Crossref] [Google Scholar]
[26]Khanlari GR, Heidari M, Momeni AA, Abdilor Y. Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods. Engineering Geology. 2012; 131:11-8.
[Crossref] [Google Scholar]
[27]Ghorbani A, Hasanzadehshooiili H. Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing. Soils and Foundations. 2018; 58(1):34-49.
[Crossref] [Google Scholar]
[28]Yilmaz I, Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications. 2011; 38(5):5958-66.
[Crossref] [Google Scholar]
[29]Gordan B, Armaghani DJ, Hajihassani M, Monjezi M. Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers. 2016; 32(1):85-97.
[Crossref] [Google Scholar]
[30]Erzin Y, Cetin T. The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Scientia Iranica. 2012; 19(2):188-94.
[Crossref] [Google Scholar]
[31]Mamat RC, Samad AM, Kasa A, Razali SF, Ramli A, Omar MB. Slope stability analysis of road embankment on soft ground treated with prefabricated vertical drains. International Journal of Advanced Science and Technology. 2020; 29(6 Spec):834-42.
[Google Scholar]
[32]Chan KF, Poon BM, Perera D. Prediction of embankment performance using numerical analyses–Practitioner’s approach. Computers and Geotechnics. 2018; 93:163-77.
[Crossref] [Google Scholar]
[33]Paliwal M, Kumar UA. Neural networks and statistical techniques: a review of applications. Expert systems with applications. 2009; 36(1):2-17.
[Crossref] [Google Scholar]
[34]Tang XW, Bai X, Hu JL, Qiu JN. Assessment of liquefaction-induced hazards using Bayesian networks based on standard penetration test data. Natural Hazards and Earth System Sciences. 2018; 18(5):1451-68.
[Crossref] [Google Scholar]
[35]Kok BV, Yilmaz M, Sengoz B, Sengur A, Avci E. Investigation of complex modulus of base and SBS modified bitumen with artificial neural networks. Expert Systems with Applications. 2010; 37(12):7775-80.
[Crossref] [Google Scholar]
[36]Biglarijoo N, Mirbagheri SA, Bagheri M, Ehteshami M. Assessment of effective parameters in landfill leachate treatment and optimization of the process using neural network, genetic algorithm and response surface methodology. Process Safety and Environmental Protection. 2017; 106:89-103.
[Crossref] [Google Scholar]
[37]Yildirim B, Gunaydin O. Estimation of california bearing ratio by using soft computing systems. Expert Systems with Applications. 2011; 38(5):6381-91.
[Crossref] [Google Scholar]
[38]Chakraborty A, Goswami D. Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences. 2017; 10(17):1-11.
[Crossref] [Google Scholar]
[39]Kaboodvandpour S, Amanollahi J, Qhavami S, Mohammadi B. Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran. Natural Hazards. 2015; 78(2):879-93.
[Crossref] [Google Scholar]
[40]Chou JS, Lin CW, Pham AD, Shao JY. Optimized artificial intelligence models for predicting project award price. Automation in Construction. 2015; 54:106-15.
[Crossref] [Google Scholar]
[41]Graupe D. Principles of artificial neural networks. World Scientific; 2013.
[Google Scholar]
[42]Fyfe C. Artificial neural networks and information theory. Department of Computing and Information System, the University of Paisley. 2000.
[Google Scholar]
[43]Orbanić P, Fajdiga M. A neural network approach to describing the fretting fatigue in aluminium-steel couplings. International Journal of Fatigue. 2003; 25(3):201-7.
[Crossref] [Google Scholar]
[44]Haykin S, Network N. A comprehensive foundation. Neural Networks. 2004; 2(2004):41.
[Google Scholar]
[45]Ashrafi HR, Jalal M, Garmsiri K. Prediction of load–displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network. Expert Systems with Applications. 2010; 37(12):7663-8.
[Crossref] [Google Scholar]
[46]Das SK, Biswal RK, Sivakugan N, Das B. Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environmental Earth Sciences. 2011; 64(1):201-10.
[Crossref] [Google Scholar]
[47]Mamat RC, Kasa A, Razali SF. The applications and future perspectives of adaptive neuro-fuzzy inference system in road embankment stability. Journal of Engineering Science & Technology Review. 2019; 12(5):75-90.
[Google Scholar]
[48]Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Mohtar WH, Hussain A. ANN based sediment prediction model utilizing different input scenarios. Water Resources Management. 2015; 29(4):1231-45.
[Crossref] [Google Scholar]
[49]He W, Chen Y, Yin Z. Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Transactions on Cybernetics. 2015; 46(3):620-9.
[Crossref] [Google Scholar]
[50]Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639):115-8.
[Crossref] [Google Scholar]
[51]Raghu S, Sriraam N. Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures. Expert Systems with Applications. 2017; 89:205-21.
[Crossref] [Google Scholar]
[52]Beşikçi EB, Arslan O, Turan O, Ölçer AI. An artificial neural network based decision support system for energy efficient ship operations. Computers & Operations Research. 2016; 66:393-401.
[Crossref] [Google Scholar]
[53]Mirjalili S. How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Applied Intelligence. 2015; 43(1):150-61.
[Crossref] [Google Scholar]
[54]Lee S, Choeh JY. Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications. 2014; 41(6):3041-6.
[Crossref] [Google Scholar]
[55]Mehrabi S, Maghsoudloo M, Arabalibeik H, Noormand R, Nozari Y. Application of multilayer perceptron and radial basis function neural networks in differentiating between chronic obstructive pulmonary and congestive heart failure diseases. Expert Systems with Applications. 2009; 36(3):6956-9.
[Crossref] [Google Scholar]
[56]Chandwani V, Agrawal V, Nagar R, Singh S. Modeling slump of ready mix concrete using artificial neural network. International Journal of Technology. 2015; 6(2):207-16.
[Crossref] [Google Scholar]
[57]Başyigit C, Akkurt I, Kilincarslan S, Beycioglu A. Prediction of compressive strength of heavyweight concrete by ANN and FL models. Neural Computing and Applications. 2010; 19(4):507-13.
[Crossref] [Google Scholar]
[58]Amirian E, Leung JY, Zanon S, Dzurman P. Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs. Expert Systems with Applications. 2015; 42(2):723-40.
[Crossref] [Google Scholar]
[59]Jamli MR, Ariffin AK, Wahab DA. Incorporating feedforward neural network within finite element analysis for L-bending springback prediction. Expert Systems with Applications. 2015; 42(5):2604-14.
[Crossref] [Google Scholar]
[60]Mamat RC, Kasa A, Razali SM. Comparative analysis of settlement and pore water pressure of road embankment on yan soft soil treated with PVDs. Civil Engineering Journal. 2019; 5(7):1609-18.
[Crossref] [Google Scholar]
[61]Uzlu E, Kankal M, Akpınar A, Dede T. Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm. Energy. 2014; 75:295-303.
[Crossref] [Google Scholar]
[62]Mamat RC, Kasa A, Razali SF. A review of road embankment stability on soft ground: problems and future perspective. IIUM Engineering Journal. 2019; 20(2):32-56.
[Google Scholar]