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Engineering with Computers

, Volume 35, Issue 2, pp 647–658 | Cite as

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

  • Ebrahim Noroozi Ghaleini
  • Mohammadreza KoopialipoorEmail author
  • Mohammadreza Momenzadeh
  • Mehdi Esfandi Sarafraz
  • Edy Tonnizam Mohamad
  • Behrouz Gordan
Original Article

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.

Keywords

Retaining wall Safety factor Artificial bee colony Artificial neural network Hybrid model 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Ebrahim Noroozi Ghaleini
    • 1
  • Mohammadreza Koopialipoor
    • 2
    Email author
  • Mohammadreza Momenzadeh
    • 3
  • Mehdi Esfandi Sarafraz
    • 4
  • Edy Tonnizam Mohamad
    • 5
  • Behrouz Gordan
    • 6
  1. 1.Faculty of Mining and MetallurgyAmirkabir University of TechnologyTehranIran
  2. 2.Faculty of Civil and Environmental EngineeringAmirkabir University of TechnologyTehranIran
  3. 3.Faculty of Civil and Environmental Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  4. 4.Department of Civil Engineering, West Tehran BranchIslamic Azad UniversityTehranIran
  5. 5.Geotropik- Centre of Tropical GeoengineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
  6. 6.Department of Geotechnics and Transportation, Faculty of Civil EngineeringUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia

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