New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering

  • Ameer A. JeburEmail author
  • Dhiya Al-Jumeily
  • Khalid R. Aljanabi
  • Rafid M. Al Khaddar
  • William Atherton
  • Zeinab I. Alattar
  • Adel H. Majeed
  • Jamila Mustafina
Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)


In this study, artificial neural network (ANN) techniques are used in an attempt to predict the nonlinear hyperbolic soil stress–strain relationship parameters (k and Rf). Two ANN models are developed and trained to achieve the planned target, in an attempt at making the experimental test (unconsolidated undrained triaxial test) unnecessary. The first is logarithm of modulus number (log k), and the second is failure ratio (Rf). A database of laboratory measurements comprises a total of (83) case records for modulus number (k) and failure ratio (Rf). Four parameters are considered to have the most significant impact on the nonlinear soil stress–strain relationship parameters, which are used as an independent input variables (IIVs) to the developed the proposed ANNs models. These comprise of: Plasticity index (PI), Dry unit weight (γdry), Water content (ωo), and Confining stress (σ3), the output models are respectively, (log k), and (Rf). Multilayer perceptron trainings using back-propagation algorithm are used in this work. The effect of a number of issues in relation to ANN construction such as ANN geometry and internal parameters on the performance of ANN models is investigated. Information on the relative importance of the factors affecting the (log k), and (Rf) is presented, and practical equations for their prediction are proposed.


Artificial neural network (ANN) Soil stress–strain parameters Unconsolidated undrained triaxial test Supervised computational intelligence 



The authors would like to acknowledge the Iraqi Ministry of Higher Education and Scientific Research and Wasit University for the grant provided to carry out this research under the grant agreement number 162575, dated 28/05/2013, with the Liverpool John Moores University, university reference number (744221).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ameer A. Jebur
    • 1
    • 2
    Email author
  • Dhiya Al-Jumeily
    • 3
  • Khalid R. Aljanabi
    • 4
  • Rafid M. Al Khaddar
    • 1
  • William Atherton
    • 1
  • Zeinab I. Alattar
    • 2
  • Adel H. Majeed
    • 2
  • Jamila Mustafina
    • 5
  1. 1.Department of Civil EngineeringLiverpool John Moores UniversityLiverpoolUK
  2. 2.Department of Civil EngineeringAl-Mustansiriya UniversityBaghdadIraq
  3. 3.Department of Computer ScienceLiverpool John Moores UniversityLiverpoolUK
  4. 4.Department of Civil EngineeringUniversity of AnbarRamadiIraq
  5. 5.Kazan Federal UniversityKazanRussia

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