Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

Recently, the use of fiber-reinforced polymers (FRP)-confinement has increased due to its various favorable effects on concrete structures, such as an increase in strength and ductility. Therefore, researchers have been attracted to exploring the behavior and efficiency of FRP-confinement for concrete structural elements further. The current study investigates improved strength and strain models for FRP confined concrete cylindrical elements. Two new physical methods are proposed for use on a large preliminary evaluated database of 708 specimens for strength and 572 specimens for strain from previous experiments. The first approach is employing artificial neural networks (ANNs), and the second is using the general regression analysis technique for both axial strength and strain of FRP-confined concrete. The accuracy of the newly proposed strain models is quite satisfactory in comparison with previous experimental results. Moreover, the predictions of the proposed ANN models are better than the predictions of previously proposed models based on various statistical indices, such as the correlation coefficient (R) and mean square error (MSE), and can be used to assess the members at the ultimate limit state.

Download Full-text

Experimental Study on Concrete under Combined FRP–Steel Confinement

Materials ◽

10.3390/ma13204467 ◽

2020 ◽

Vol 13 (20) ◽

pp. 4467 ◽

Cited By ~ 1

Author(s):

Stefan Kaeseberg ◽

Dennis Messerer ◽

Klaus Holschemacher

Keyword(s):

Experimental Study ◽

Cross Sections ◽

Limit State ◽

Plain Concrete ◽

Confined Concrete ◽

Fiber Reinforced Polymers ◽

Ultimate Limit State ◽

Stress Strain ◽

Effective Measure ◽

Strain Behavior

The confinement of reinforced concrete (RC) compression members by fiber-reinforced polymers (FRPs) is an effective measure for the strengthening and retrofitting of existing structures. Thus far, extensive research on the stress–strain behavior and ultimate limit state design of FRP-confined concrete has been conducted, leading to various design models. However, these models are significantly different when compared to one another. In particular, the use of certain empirical efficiency and reduction factors results in various predictions of load-bearing behavior. Furthermore, most experimental programs solely focus on plain concrete specimens or demonstrate insufficient variation in the material properties. Therefore, this paper presents a comprehensive experimental study on plain and reinforced FRP-confined concrete, limited to circular cross sections. The program included 63 carbon FRP (CFRP)-confined plain and 60 CFRP-confined RC specimens with a variation in the geometries and in the applied materials. The analysis showed a significant influence of the compressive strength of the confined concrete on the confinement efficiency in the design methodology, as well as the importance of the proper determination of individual reduction values for different FRP composites. Finally, applicable experimental test results from the literature were included, enabling the development of a modified stress–strain and ultimate condition design model.

Download Full-text

Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

Science and Engineering of Composite Materials ◽

10.1515/secm-2013-0002 ◽

2014 ◽

Vol 21 (2) ◽

pp. 239-255 ◽

Cited By ~ 2

Author(s):

Gunnur Yavuz ◽

Musa Hakan Arslan ◽

Omer Kaan Baykan

Keyword(s):

Neural Networks ◽

Shear Strength ◽

Artificial Neural Networks ◽

Back Propagation ◽

Fiber Reinforced Polymers ◽

Rc Beams ◽

Ann Model ◽

Ultimate Shear Strength ◽

Existing Building ◽

Artificial Neural

AbstractIn this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R2≈0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.

Download Full-text

An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns

Engineering Structures ◽

10.1016/j.engstruct.2017.02.047 ◽

2017 ◽

Vol 140 ◽

pp. 199-208 ◽

Cited By ~ 51

Author(s):

Alessio Cascardi ◽

Francesco Micelli ◽

Maria Antonietta Aiello

Keyword(s):

Neural Networks ◽

Compressive Strength ◽

Artificial Neural Networks ◽

Confined Concrete ◽

Artificial Neural

Download Full-text

Structural Reliability Analysis Using Adaptive Artificial Neural Networks

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4044040 ◽

2019 ◽

Vol 5 (4) ◽

Author(s):

Wellison José de Santana Gomes

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Reliability Analysis ◽

Structural Reliability ◽

Limit State ◽

Surrogate Models ◽

Primary Objective ◽

Computational Effort ◽

State Function ◽

Artificial Neural

Abstract Structural reliability theory has been applied to many engineering problems in the last decades, with the primary objective of quantifying the safety of such structures. Although in some cases approximated methods may be used, many times the only alternatives are those involving more demanding approaches, such as Monte Carlo simulation (MCS). In this context, surrogate models have been widely employed as an attempt to keep the computational effort acceptable. In this paper, an adaptive approach for reliability analysis using surrogate models, proposed in the literature in the context of Kriging and polynomial chaos expansions (PCEs), is adapted for the case of multilayer perceptron (MLP) artificial neural networks (ANNs). The methodology is employed in the solution of three benchmark reliability problems and compared to MCS and other methods from the literature. In all cases, the ANNs led to results very close to those obtained by MCS and required much less limit state function evaluations. Also, the performance of the ANNs was found comparable, in terms of accuracy and efficiency, to the performance of the other methods.

Download Full-text