Skip to main content

Advertisement

SpringerLink
Log in

Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The evaluation and precise prediction of safety factor (SF) of slopes can be useful in designing/analyzing these important structures. In this study, an attempt has been made to evaluate/predict SF of many homogenous slopes in static and dynamic conditions through applying various hybrid intelligent systems namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN, particle swarm optimization (PSO)-ANN and artificial bee colony (ABC)-ANN. In fact, ICA, PSO, GA and ABC were used to adjust weights and biases of ANN model. In order to achieve the aim of this study, a database composed of 699 datasets with 5 model inputs including slope gradient, slope height, friction angle of soil, soil cohesion and peak ground acceleration and one output (SF) was established. Several parametric investigations were conducted in order to determine the most effective factors of GA, ICA, ABC and PSO algorithms. The obtained results of hybrid models were check considering two performance indices, i.e., root-mean-square error and coefficient of determination \((R^{2})\). To evaluate capability of all hybrid models, a new system of ranking, i.e., the color intensity rating, was developed. As a result, although all predictive models are able to approximate slope SF values, PSO-ANN predictive model can perform better compared to others. Based on \(R^{2}\), values of (0.969, 0.957, 0.980 and 0.920) were found for testing of ICA-ANN, ABC-ANN, PSO-ANN and GA-ANN predictive models, respectively, which show higher efficiency of the PSO-ANN model in predicting slope SF values.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Ahmad A, Razali SFM, Mohamed ZS, El-shafie A (2016) The Application of Artificial Bee Colony and Gravitational Search Algorithm in Reservoir Optimization. Water Resour Manag 30:2497–2516

    Article  Google Scholar 

  • Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13:1085–1098

    Article  Google Scholar 

  • Armaghani DJ, Hasanipanah M, Mohamad ET (2016a) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32:155–171. https://doi.org/10.1007/s00366-015-0408-z

    Article  Google Scholar 

  • Armaghani DJ, Mahdiyar A, Hasanipanah M et al (2016b) Risk assessment and prediction of flyrock distance by combined multiple regression analysis and Monte Carlo simulation of quarry blasting. Rock Mech Rock Eng 49:1–11. https://doi.org/10.1007/s00603-016-1015-z

    Article  Google Scholar 

  • Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Space Technol 63:29–43. https://doi.org/10.1016/j.tust.2016.12.009

    Article  Google Scholar 

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary computation, 2007. CEC 2007. IEEE Congress on. IEEE, pp 4661–4667

  • Baker R (2006) A relation between safety factors with respect to strength and height of slopes. Comput Geotech 33:275–277

    Article  Google Scholar 

  • Bashir ZA, El-Hawary ME (2009) Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Trans Power Syst 24:20–27

    Article  Google Scholar 

  • Beiki M, Majdi A, Givshad A (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. J Rock Mech Min 63:159–169

    Article  Google Scholar 

  • Bejarbaneh BY, Bejarbaneh EY, Amin MFM et al (2016) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-016-0983-2

    Google Scholar 

  • Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2:311–319

    Article  Google Scholar 

  • Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  • Das SK, Biswal RK, Sivakugan N, Das B (2011) Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environ Earth Sci 64:201–210

    Article  Google Scholar 

  • de Oliveira IMS, Schirru R, de Medeiros J (2009) On the performance of an artificial bee colony optimization algorithm applied to the accident diagnosis in a pwr nuclear power plant. In: 2009 international nuclear Atlantic conference (INAC 2009)

  • Duncan JM (1996) State of the art: limit equilibrium and finite-element analysis of slopes. J Geotech Eng 122:577–596

    Article  Google Scholar 

  • Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75:27–36

    Article  Google Scholar 

  • Erzin Y, Cetin T (2013) The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Comput Geosci 51:305–313

    Article  Google Scholar 

  • Ghoraba S, Monjezi M, Talebi N et al (2016) Estimation of ground vibration produced by blasting operations through intelligent and empirical models. Environ Earth Sci 75:1137

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  • Gordan B, Armaghani DJ, Adnan AB, Rashid ASA (2016a) A new model for determining slope stability based on seismic motion performance. Soil Mech Found Eng 53:344–351. https://doi.org/10.1007/s11204-016-9409-1

    Article  Google Scholar 

  • Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2016b) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32:85–97. https://doi.org/10.1007/s00366-015-0400-7

    Article  Google Scholar 

  • Hajihassani M, Armaghani D, Sohaei H, Mohamad E (2014a) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  • Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2014b) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886. https://doi.org/10.1007/s10064-014-0657-x

    Article  Google Scholar 

  • Hajihassani M, Jahed Armaghani D, Sohaei H et al (2014c) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67. https://doi.org/10.1016/j.apacoust.2014.01.005

    Article  Google Scholar 

  • Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H et al (2016a) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2434-1

    Google Scholar 

  • Hasanipanah M, Jahed Armaghani D, Khamesi H et al (2016b) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32:441–455. https://doi.org/10.1007/s00366-015-0425-y

    Article  Google Scholar 

  • Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016c) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32:705–715. https://doi.org/10.1007/s00366-016-0447-0

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge

    Book  Google Scholar 

  • Irani R, Nasimi R (2011) Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling. J Pet Sci Eng 78:6–12

    Article  Google Scholar 

  • Jahed Armaghani D, Hajihassani M, Marto A et al (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess 187:666. https://doi.org/10.1007/s10661-015-4895-6

    Article  Google Scholar 

  • Jahed Armaghani D, Hasanipanah M, Mahdiyar A et al (2016) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2598-8

    Google Scholar 

  • Jahed Armaghani D, Shoib RSNSBR, Faizi K, Rashid ASA (2017) Developing a hybrid PSO-ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391–405. https://doi.org/10.1007/s00521-015-2072-z

    Article  Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  MathSciNet  MATH  Google Scholar 

  • Kennedy J, Eberhart RC (1995) A discrete binary version of the particle swarm algorithm. In: Systems, man, and cybernetics, 1997. Computational cybernetics and simulation, 1997 IEEE international conference on. IEEE, pp 4104–4108

  • Khandelwal M, Armaghani DJ (2016) Prediction of drillability of rocks with strength properties using a Hybrid GA-ANN technique. Geotech Geol Eng 34:605–620. https://doi.org/10.1007/s10706-015-9970-9

    Article  Google Scholar 

  • Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222

    Article  Google Scholar 

  • Koopialipoor M, Armaghani DJ, Haghighi M, Ghaleini EN (2017) A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1116-2

    Google Scholar 

  • Koopialipoor M, Fallah A, Armaghani DJ et al (2018) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput. https://doi.org/10.1007/s00366-018-0596-4

    Google Scholar 

  • Lee Y, Oh S-H, Kim MW (1991) The effect of initial weights on premature saturation in back-propagation learning. In: Neural networks, 1991, IJCNN-91-Seattle international joint conference on. IEEE, pp 765–770

  • Liang H, Zhang H (2010) Identification of slope stability based on the contrast of BP neural network and SVM. In: Computer science and information technology (ICCSIT), 2010 3rd IEEE international conference on. IEEE, pp 347–350

  • Liou S-W, Wang C-M, Huang Y-F (2009) Integrative discovery of multifaceted sequence patterns by frame-relayed search and hybrid PSO-ANN. J UCS 15:742–764

    Google Scholar 

  • Mahdiyar A, Hasanipanah M, Armaghani DJ et al (2017) A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput. https://doi.org/10.1007/s00366-016-0499-1

    Google Scholar 

  • Marto A, Hajihassani M, Jahed Armaghani D et al (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J. https://doi.org/10.1155/2014/643715

    Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  • Mohamad ET, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2014) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 74:745–757. https://doi.org/10.1007/s10064-014-0638-0

    Article  Google Scholar 

  • Mohamad ET, Armaghani DJ, Momeni E et al (2016a) Rock strength estimation: a PSO-based BP approach. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2728-3

    Google Scholar 

  • Mohamad ET, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZA (2017) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 28(1):393–406

    Article  Google Scholar 

  • Momeni E, Nazir R, Armaghani DJ, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131

    Article  Google Scholar 

  • Monjezi M, Khoshalan HA, Varjani AY (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5:441–448

    Article  Google Scholar 

  • Nozohour-leilabady B, Fazelabdolabadi B (2016) On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology. Petroleum 2:79–89

    Article  Google Scholar 

  • Rodriguez FJ, García-Martínez C, Blum C, Lozano M (2012) An artificial bee colony algorithm for the unrelated parallel machines scheduling problem. In: International conference on parallel problem solving from nature. Springer, pp 143–152

  • Rukhaiyar S, Alam MN, Samadhiya NK (2017) A PSO-ANN hybrid model for predicting factor of safety of slope. Int J Geotech Eng. https://doi.org/10.1080/19386362.2017.1305652

    Google Scholar 

  • Saemi M, Ahmadi M, Varjani A (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng 59:97–105

    Article  Google Scholar 

  • Saghatforoush A, Monjezi M, Faradonbeh RS, Armaghani DJ (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32:255–266

    Article  Google Scholar 

  • Sakellariou MG, Ferentinou MD (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 23:419

    Article  Google Scholar 

  • Samui P, Kothari DP (2011) Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Sci Iran 18:53–58

    Article  MATH  Google Scholar 

  • Sarma SK (1973) Stability analysis of embankments and slopes. Geotechnique 23:423–433

    Article  Google Scholar 

  • Sarma SK (1979) Stability analysis of embankments and slopes. J Geotech Geoenviron Eng 105:1511–1524

    Google Scholar 

  • Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8:10819–10832

    Article  Google Scholar 

  • Shangguan Z, Li S, Luan M (2009) Intelligent forecasting method for slope stability estimation by using probabilistic neural networks. Electron J Geotech Eng 13:1–10

    Google Scholar 

  • Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of p-wave velocity and anisotropic property of rock using artificial neural network technique. J Sci Ind Res (India) 63:28–32

    Google Scholar 

  • Taghavifar H, Mardani A, Taghavifar L (2013) A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility. Measurement 46:2288–2299

    Article  Google Scholar 

  • Tan Ö (2006) Investigation of soil parameters affecting the stability of homogeneous slopes using the Taguchi method. Eurasian Soil Sci 39:1248–1254

    Article  Google Scholar 

  • Tonnizam Mohamad E, Hajihassani M, Jahed Armaghani D, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Model Simul 5:2501–2506

    Google Scholar 

  • Yılmaz I, Yuksek A (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41:781–795

    Article  Google Scholar 

  • Zhang C, Ouyang D, Ning J (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37:4761–4767

    Article  Google Scholar 

  • Zhou YD, Cheuk CY, Tham LG (2009) Numerical modelling of soil nails in loose fill slope under surcharge loading. Comput Geotech 36:837–850

    Article  Google Scholar 

  • Zorlu K, Gokceoglu C, Ocakoglu F et al (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mining and Metallurgy, Amirkabir University of Technology, Tehran, 15914, Iran

    Mohammadreza Koopialipoor

  2. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Danial Jahed Armaghani

  3. Environmental Engineering and Green Technology Department, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Danial Jahed Armaghani & Aminaton Marto

  4. Faculty of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO, 80401, USA

    Ahmadreza Hedayat

  5. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia

    Behrouz Gordan

Authors
  1. Mohammadreza Koopialipoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmadreza Hedayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aminaton Marto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Behrouz Gordan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammadreza Koopialipoor.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koopialipoor, M., Jahed Armaghani, D., Hedayat, A. et al. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput 23, 5913–5929 (2019). https://doi.org/10.1007/s00500-018-3253-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3253-3

Keywords

Search

Navigation