Journal of Intelligent Manufacturing

, Volume 30, Issue 3, pp 1247–1257 | Cite as

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

  • I. Mansouri
  • M. Shariati
  • M. SafaEmail author
  • Z. Ibrahim
  • M. M. Tahir
  • D. Petković


The V-shaped angle shear connector is recognized as to expand certain mechanical properties to the shear connectors, contains adequate ductility, elevate resistance, power degradation resistance under cyclic charging, and high shear transmission, more economical than other shear connectors, for instance, the L-shaped and C-shaped shear connectors. The performance of this shear connector had been investigated by previous researchers (Shariati et al. in Mater Struct 49(9):1–18, 2015), but the strength prediction was not clearly explained. In this investigation, the shear strength prediction of this connector was analyzed based on several factors. The ultimate purpose was to investigate the variations of different factors that were affecting the shear strength of this connector. To achieve this aim, the data (concrete compression strength, thickness, length, height, slope of inclination, and shear strength) were collected from the parametric studies using finite element analysis results for this purpose were input using the ANFIS method (neuro-fuzzy inference system). The finite element analysis results were verified by experimental test results. All variables from the predominant factors that were affected the shear strength of the shear connector (V-shaped angle) were also selected by using the ANFIS process. The results exhibited that the proposed shear connector (V-shaped angle) contained the potentiality to be used practically after several improvements. One option might be the improvement of the testing process for different predictive models with more input variables that will improve the predictive power of the created models.


ANFIS Forecasting Shear strength, Shear connector Composite V-shaped angle Push-out test Monotonic load 

List of symbols


Silica sand


Silica sand weight


Sieve weight

Cum. Ret

Cumulative retained

\(\hbox {E}_{\mathrm{s}}\)

The elasticity modulus

\(\upgamma \)


\(\upupsilon \)

Poisson ratio


The concrete strength of cylinder specimen


The concrete strength of cubic specimen

\(\upvarepsilon _{\mathrm{c1}}\)



The reduction factor

\(\upvarepsilon _{\mathrm{cu}}\)

The ultimate strain at failure

\(\hbox {E}_{\mathrm{cm}}\)

The elasticity module

\({\varvec{\upvarepsilon }}\)

The eccentricity

\(\Psi \)

The material dilation angle

\(\hbox {f}_{\mathrm{b}0}\)

The biaxial compressive strength

\(\hbox {f}_{\mathrm{c}0}\)

The uniaxial compressive strength


Penetration measure in the contact region


Fraction of the minimum element length

\(\hbox {L}_{\mathrm{elem}}\).

Element length

\(K\hbox {i}\)

Initial stiffness


The segment member


Geometric scale factor

\(\hbox {k}_{\mathrm{dflt}}\)

Default stiffness


Overclosure factor


Overclosure measure

\(\hbox {S}_{0}\)

The initial scale factor;

\(\mu _{AB} \left( x \right) , \mu _{CD} \left( x \right) \)

The membership function

\(\left\{ {a_i, b_i, c_i, d_i } \right\} \)

The set of parameters

x” and “y

The values of inputs from the nodes

\(\left\{ {p_i, q_i, r} \right\} \)

The variable set designated as consequent parameters

\(P_{i }\)

The experimental value


Signifies the forecast value


The total number of test data



This research was supported by Birjand University of Technology grant (Project No. RP/95/1003). The authors would like to acknowledge this support.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • I. Mansouri
    • 1
  • M. Shariati
    • 2
  • M. Safa
    • 2
    Email author
  • Z. Ibrahim
    • 2
  • M. M. Tahir
    • 3
  • D. Petković
    • 4
  1. 1.Department of Civil EngineeringBirjand University of TechnologyBirjandIran
  2. 2.Department of Civil EngineeringUniversity of MalayaKuala LumpurMalaysia
  3. 3.UTM CRC, Institute for Smart Infrastructure and Innovative ConstructionUTMJohor BahruMalaysia
  4. 4.University of Niš, Pedagogical Faculty in VranjeVranjeSerbia

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