Neural Computing and Applications

, Volume 31, Issue 11, pp 7837–7865 | Cite as

Fuzzy logic model for pullout capacity of near-surface-mounted FRP reinforcement bonded to concrete

  • Kourosh NasrollahzadehEmail author
  • Solmaz Afzali
Original Article


The application of fiber-reinforced polymer (FRP) strips or rods in the form of near-surface-mounted (NSM) reinforcement has become an attractive solution to strengthen the existing buildings and bridges. It is of interest to engineers to have an accurate estimate of the bond capacity of this technique. In this paper, fuzzy logic approach is utilized to propose an alternative method of determining the pullout strength of NSM FRP strips/rods which are bonded to the concrete block. Two types of fuzzy logic models, namely Mamdani and Takagi–Sugeno, are developed. With the aim of enhancing the interpretability of the fuzzy model, the rule base of Mamdani model is extracted from the classification decision tree, and the membership functions corresponding to the linguistic concepts are built by uniform partitioning the range of variables. On the other hand, in order to arrive at closed-form equations for pullout capacity, the subtractive clustering algorithm is employed to deduce the rule base and membership functions of Takagi–Sugeno model (first order), and its consequent part is tuned by the least square optimization using training dataset. Several fuzzy logic models of both types with different numbers of rules are developed and compared in terms of different error measures. To train and validate the fuzzy models, a large database of 384 direct pullout tests on NSM FRP bonded to concrete is assembled from the literature. The results reveal that both of the proposed Mamdani and Takagi–Sugeno models demonstrate good accuracy against the experimental data and outperform the published models. A parametric study indicates that the proposed fuzzy models can predict the maximum effective bond length, and thus, they are able to capture the underlying mechanics of the problem.


Fuzzy logic Data mining Decision tree Bond strength Fiber-reinforced polymer Concrete 



This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringK. N. Toosi University of TechnologyTehranIran

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