Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach

Abstract Fibre-reinforced polymer (FRP) provides an alternative reinforcement for concrete flat slabs. This research proposes a hybrid machine learning model for predicting the ultimate punching shear capacity of FRP-reinforced slabs. The model employs the least squares support vector machine (LS-SVM) to discover the mapping between the influencing factors and the slab punching capacity. Furthermore, the firefly algorithm (FA), a population-based metaheuristic, is utilised to facilitate the LS-SVM training. A data-set which contains actual tests of FRP-reinforced concrete slabs is utilised to construct and verify the proposed approach. The contribution of this research is to establish a hybrid machine method, based on the LS-SVM and FA algorithms, for meliorating the prediction accuracy of FRP-reinforced slabs’ ultimate punching shear capacity. Experimental results demonstrate that the new model has achieved roughly 55 and 15% reductions in terms of Root Mean Squared Error compared with the formula-based and Artificial Neural Network methods, respectively.

[1]  L. Hollaway.  A review of the present and future utilisation of FRP composites in the civil infrastructure with reference to their important in-service properties , 2010 .

[2]  Luc Taerwe,et al.  Concrete Slabs Reinforced with FRP Grids. II: Punching Resistance , 2000 .

[3]  Min-Yuan Cheng,et al.  A novel hybrid intelligent approach for contractor default status prediction , 2014, Knowl. Based Syst..

[4]  H. Marzouk,et al.  Prediction of punching shear strength of two-way slabs , 2011 .

[5]  Long Nguyen-Minh,et al.  Punching Shear Resistance of Interior GFRP Reinforced Slab-Column Connections , 2013 .

[6]  Kypros Pilakoutas,et al.  Punching Shear Behavior of Fiber Reinforced Polymers Reinforced Concrete Flat Slabs: Experimental Study , 2003 .

[7]  Carlos E. Ospina,et al.  PUNCHING OF TWO-WAY CONCRETE SLABS WITH FIBER-REINFORCED POLYMER REINFORCING BARS OR GRIDS , 2003 .

[8]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..

[9]  Kypros Pilakoutas,et al.  New Approach for Punching Shear Capacity Prediction of Fiber Reinforced Polymer Reinforced Concrete Flat Slabs , 1999, SP-188: 4th Intl Symposium - Fiber Reinforced Polymer Reinforcement for Reinforced Concrete Structures.

[10]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[11]  Brahim Benmokrane,et al.  Punching-Shear Strength of Normal and High-Strength Two-Way Concrete Slabs Reinforced with GFRP Bars , 2013 .

[12]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[13]  Luca Bertolini,et al.  Steel corrosion and service life of reinforced concrete structures , 2008 .

[14]  Ibrahim M. Metwally,et al.  Prediction of punching shear capacities of two-way concrete slabs reinforced with FRP bars , 2013 .

[15]  Hakan Guler,et al.  Prediction of railway track geometry deterioration using artificial neural networks: a case study for Turkish state railways , 2014 .

[16]  Min-Yuan Cheng,et al.  Interval estimation of construction cost at completion using least squares support vector machine , 2014 .

[17]  Min-Yuan Cheng,et al.  Hybrid intelligent inference model for enhancing prediction accuracy of scour depth around bridge piers , 2015 .

[18]  Jiping Bai,et al.  Advanced fibre-reinforced polymer (FRP) composites for structural applications , 2013 .

[19]  Nhat-Duc Hoang,et al.  Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression , 2016, J. Comput. Civ. Eng..

[20]  Ibrahim H. Guzelbey,et al.  Neural network modeling of strength enhancement for CFRP confined concrete cylinders , 2008 .