Skip to main content
SpringerLink
Log in

A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

This paper presents intelligent models for solving problems related to retaining walls in geotechnics. To do this, safety factors of 2800 retaining walls were modeled and recorded considering different effective parameters of retaining walls (RWs), i.e., height of the wall, wall thickness, friction angle, density of the soil, and density of the rock. Two intelligent methodologies including a pre-developed artificial neural network (ANN) and a combination of artificial bee colony (ABC) and ANN were selectively developed to approximate safety factors of RWs. In the new network, ABC was used to optimize weight and biases of ANN to receive higher level of accuracy and performance prediction. Many ANN and ABC–ANN models were built considering the most influential parameters of them and their performances were evaluated using coefficient of determination (R2) and root mean square error (RMSE) performance indices. After developing the mentioned models, it was found that the new hybrid model is able to increase network performance capacity significantly. For instance, R2 values of 0.982 and 0.985 for training and testing of ABC–ANN model, respectively, compared to these values of 0.920 and 0.924 for ANN model showed that the new hybrid model can be introduced as a capable enough technique in the field of this study for estimating safety factors of RWs.

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

Similar content being viewed by others

References

  1. Coulomb CA (1776) Essai sur une Application des Règles de Maximis et Minimis à Quelques Problèmes de Statique Relatifs à L’Architecture. Mèmoires de la Mathèmatique et de Phisique, présentés à l’Académie Royale des Sciences, par divers savans, et lûs dans sés Assemblées. Annee 1793:343–382

    Google Scholar 

  2. Rankine WJM (1857) On the mathematical theory of the stability of earthwork and masonry. J Frankl Inst 63:84–85

    Article  Google Scholar 

  3. Terzaghi K (1943) Theoretical soil mechanics. Wiley, New York

    Book  Google Scholar 

  4. Leshchinsky D, Vulova C (2001) Numerical investigation of the effects of geosynthetic spacing on failure mechanisms in MSE block walls. Geosynth Int 8:343–365

    Article  Google Scholar 

  5. Yu G-Y, Bai Y-S, Sheng P, Guo R-P (2009) Mechanical performance of a double-face reinforced retaining wall in an area disturbed by mining. Min Sci Technol 19:36–39

    Google Scholar 

  6. Chan YC (1996) Study of old masonry retaining walls in Hong Kong. GEO Report No. 31, Geotechnical Engineering Office, Civil Engineering Department, Hong Kong

  7. Rankine WJM (1857) On the stability of loose earth. Philos Trans R Soc Lond 9–27

  8. Tsagareli ZV (1965) Experimental investigation of the pressure of a loose medium on retaining walls with a vertical back face and horizontal backfill surface. Soil Mech Found Eng 2:197–200

    Article  Google Scholar 

  9. Chang M-F (1997) Lateral earth pressures behind rotating walls. Can Geotech J 34:498–509

    Article  Google Scholar 

  10. O’Neal TS, Hagerty DJ (2011) Earth pressures in confined cohesionless backfill against tall rigid walls—a case history. Can Geotech J 48:1188–1197

    Article  Google Scholar 

  11. Iskander M, Chen Z, Omidvar M et al (2013) Active static and seismic earth pressure for c–f soils. Soils Found 53:639–652

    Article  Google Scholar 

  12. Armaghani DJ, Hasanipanah M, Mohamad ET (2016) 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 

  13. 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 Sp Technol 63:29–43. https://doi.org/10.1016/j.tust.2016.12.009

    Article  Google Scholar 

  14. Koopialipoor M, Jahed Armaghani D, 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 

  15. 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 

  16. Saghatforoush A, Monjezi M, Shirani Faradonbeh R, Jahed Armaghani D (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput. https://doi.org/10.1007/s00366-015-0415-0

    Google Scholar 

  17. 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 

  18. Mohamad ET, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZ (2016) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 28(1):393–406

    Google Scholar 

  19. Hasanipanah M, Armaghani DJ, Amnieh HB, Koopialipoor M, Arab H (2018) A risk-based technique to analyze flyrock results through rock engineering system. Geotech Geol Eng. https://doi.org/10.1007/s10706-018-0459-1

    Google Scholar 

  20. 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 

  21. Koopialipoor M, Armaghani DJ, Hedayat A et al (2018) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput. https://doi.org/10.1007/s00500-018-3253-3

    Google Scholar 

  22. Safa M, Shariati M, Ibrahim Z et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos Struct 21:679–688

    Article  Google Scholar 

  23. Safa A, Rashidinejad HR, Khalili M et al (2016) Higher circulating levels of chemokines CXCL10, CCL20 and CCL22 in patients with ischemic heart disease. Cytokine 83:147–157

    Article  Google Scholar 

  24. Mansouri I, Shariati M, Safa M et al (2017) Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. J Intell Manuf. https://doi.org/10.1007/s10845-017-1306-6

    Google Scholar 

  25. Duncan JM (2000) Factors of safety and reliability in geotechnical engineering. J Geotech Geoenviron Eng 126:307–316

    Article  Google Scholar 

  26. Toghroli A, Mohammadhassani M, Suhatril M et al (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct 17:623–639

    Article  Google Scholar 

  27. Toghroli A, Suhatril M, Ibrahim Z et al (2016) Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J Intell Manuf. https://doi.org/10.1007/s10845-016-1217-y

    Google Scholar 

  28. Fenton GA, Griffiths DV, Williams MB (2007) Reliability of traditional retaining wall design. Risk Var Geotech Eng. Thomas Telford Publishing, pp 165–172

  29. Gudehus G, Touplikiotis A (2018) On the stability of geotechnical systems and its fractal progressive loss. Acta Geotech 13:317–328

    Google Scholar 

  30. Li M, Jiang R, Ge SS, Lee TH (2017) Role playing learning for socially concomitant mobile robot navigation. arXiv Prepr. arXiv:1705.10092

  31. Ma J, Jiang X, Gong M (2018) Two-phase clustering algorithm with density exploring distance measure. CAAI Trans Intell Technol 3:59–64

    Article  Google Scholar 

  32. Guan X, Liao S, Bai J et al (2017) Urban land-use classification by combining high-resolution optical and long-wave infrared images. Geospat Inf Sci 20:299–308

    Article  Google Scholar 

  33. Zhao B, Gao L, Liao W, Zhang B (2017) A new kernel method for hyperspectral image feature extraction. Geospat Inf Sci 20:309–318

    Article  Google Scholar 

  34. Tracewski L, Bastin L, Fonte CC (2017) Repurposing a deep learning network to filter and classify volunteered photographs for land cover and land use characterization. Geospat Inf Sci 20:252–268

    Article  Google Scholar 

  35. 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 

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

    Google Scholar 

  37. Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H et al (2016) 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 

  38. Gandomi AH, Kashani AR, Roke DA, Mousavi M (2017) Optimization of retaining wall design using evolutionary algorithms. Struct Multidiscip Optim 55:809–825

    Article  Google Scholar 

  39. Peck RB, Hanson WE, Thornburn TH (1974) Foundation engineering. Wiley, New York

    Google Scholar 

  40. Zurada JM (1992) Introduction to artificial neural systems. West, St. Paul

    Google Scholar 

  41. 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 

  42. Ch S, Mathur S (2012) Particle swarm optimization trained neural network for aquifer parameter estimation. KSCE J Civ Eng 16:298–307

    Article  Google Scholar 

  43. Simpson PK (1990) Artificial neural systems. Pergamon Press, Oxford

    Google Scholar 

  44. Mohandes MA (2012) Modeling global solar radiation using particle swarm optimization (PSO). Sol Energy 86:3137–3145

    Article  Google Scholar 

  45. Haykin S (1999) Neural networks. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  46. Priddy KL, Keller PE (2005) Artificial neural networks: an introduction. SPIE Press, ‎Bellingham

    Book  Google Scholar 

  47. Ahmadi MA, Shadizadeh SR (2012) New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel 102:716–723

    Article  Google Scholar 

  48. Armaghani DJ, Mohamad ET, Hajihassani M et al (2016) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comput 32:189–206

    Article  Google Scholar 

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

    Google Scholar 

  50. 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 

  51. 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 

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

    Article  Google Scholar 

  53. Rodriguez FJ, García-Martínez C, Blum C, Lozano M (2012) An artificial bee colony algorithm for the unrelated parallel machines scheduling problem. In: Int. Conf. Parallel Probl. Solving from Nat. Springer, Berlin, pp 143–152

  54. de Oliveira IMS, Schirru R, de Medeirose JACC (2009) On the performance of an artificial bee colony optimization algorithm applied to the accident diagnosis in a pwr nuclear power plant. In: International nuclear atlantic conference-INAC, vol 1, Rio de Janeiro, Brazil

  55. 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 

  56. 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 

  57. 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 

  58. Karaboga D, Akay B (2007) Artificial bee colony (ABC) algorithm on training artificial neural networks. In: IEEE 15th conference on signal processing and communications applications 2007. SIU 2007, 11 June 2007. IEEE, pp 1–4

  59. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  MATH  Google Scholar 

  60. Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the international conference on Neural Networks, pp 11–14, IEEE Press

  61. Ripley BD (1993) Statistical aspects of neural networks. Netw Chaos Stat Prob Asp 50:40–123

    Article  MATH  Google Scholar 

  62. Paola JD (1994) Neural network classification of multispectral imagery. Master Tezi, Univ. Arizona, Tucson

    Google Scholar 

  63. Wang C (1994) A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania, USA

  64. Masters T (1993) Practical neural network recipes in C++. Morgan Kaufmann, Burlington

    MATH  Google Scholar 

  65. Kanellopoulos I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725

    Article  Google Scholar 

  66. Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236

    Article  Google Scholar 

  67. 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, Iran

    Ebrahim Noroozi Ghaleini

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

    Mohammadreza Koopialipoor

  3. Faculty of Civil and Environmental Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mohammadreza Momenzadeh

  4. Department of Civil Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran

    Mehdi Esfandi Sarafraz

  5. Geotropik- Centre of Tropical Geoengineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Edy Tonnizam Mohamad

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

    Behrouz Gordan

Authors
  1. Ebrahim Noroozi Ghaleini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammadreza Koopialipoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammadreza Momenzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mehdi Esfandi Sarafraz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Edy Tonnizam Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Behrouz Gordan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammadreza Koopialipoor.

Additional information

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

Ghaleini, E.N., Koopialipoor, M., Momenzadeh, M. et al. A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Engineering with Computers 35, 647–658 (2019). https://doi.org/10.1007/s00366-018-0625-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-018-0625-3

Keywords

Search

Navigation