• Computers and Concrete
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• v.14 no.3
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• pp.233-245
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• 2014

In this paper, a rule based Mamdani type fuzzy logic model for prediction of slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes were discussed. In the model steel rebar diameters and development lengths were used as inputs. The FL model and experimental results, the coefficient of determination R2, the Root Mean Square Error were used as evaluation criteria for comparison. It was concluded that FL was practical method for predicting slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• Computers and Concrete
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• v.23 no.4
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• pp.273-279
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• 2019

Corrosion of the rebar embedded in concrete has a fundamental role in the determination of life and durability of the concrete structures. Researches have demonstrated that artificial neural networks (ANNs) can effectively predict issues such as expected damage in concrete structures in marine environment caused by chloride penetration, the potential of steel embedded in concrete under the influence of chloride, the corrosion of the steel embedded in concrete and corrosion current density in steel reinforced concrete. In this study, data from different kind of concrete under the influence of chloride ion, are analyzed using the neural network and it is concluded that this method is able to predict the bond strength between the concrete and the steel reinforcement in mentioned condition with high reliability.

• Computers and Concrete
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• v.19 no.6
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• pp.631-639
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• 2017

This study experimentally investigated the flexural capacity of a concrete beam reinforced with a newly developed GFRP bar that overcomes the lower modulus of elasticity and bond strength compared to a steel bar. The GFRP bar was fabricated by thermosetting a braided pultrusion process to form the outer fiber ribs. The mechanical properties of the modulus of elasticity and bond strength were enhanced compared with those of commercial GFRP bars. In the four-point bending test results, all specimens failed according to the intended failure mode due to flexural design in compliance with ACI 440.1R-15. The effects of the reinforcement ratio and concrete compressive strength were investigated. Equations from the code were used to predict the deflection, and they overestimated the deflection compared with the experimental results. A modified model using two coefficients was developed to provide much better predictive ability, even when the effective moment of inertia was less than the theoretical $I_{cr}$. The deformability of the test beams satisfied the specified value of 4.0 in compliance with CSA S6-10. A modified effective moment of inertia with two correction factors was proposed and it could provide much better predictability in prediction even at the effective moment of inertia less than that of theoretical cracked moment of inertia.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.903-910
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• 2005

In recent years, fiber reinforced polymer(FRP) plates have shown a great promise as an alternative to steel plates for reinforced concrete beam rehabilitation. Reinforced concrete beams strengthened with externally bonded FRP sheets to the tension face can exhibit ultimate flexural strengths several times greater than their original strength if their bond strength is enough. Debonding failure, however, may occur before the strengthened beam can achieve its enhanced flexural strength. The purpose of this paper is to investigate the debonding failure strength of FRP-strengthened reinforced concrete beams. An analytical procedure for calculating debonding load between concrete and strengthening FRP is presented. Based on the local bond stress-slip relationship in the previous studies, uniform bond stress is assumed on the effective bond length. The analytical expressions are developed from linear elastic theory and statistical analyses of experimantal results reported in the literature. The proposed method is verified by comparisons with experimental results reported in the previous researches.

• Journal of Korean Association for Spatial Structures
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• v.18 no.4
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• pp.61-68
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• 2018

Tensile load tests were conducted on High-Shear Ring Anchors (HRAs) after shear load had been applied to the HRAs, which had been developed to reduce the number of the anchors. Test variables include the embedment length of the rod and the width of the specimens and a total of 12 specimens were tested. Test results show that the HRAs pulled out due to bond failure or steel failure occurred in case that the HRAs were installed to the members with 300mm or greater width and the embedment length of 160mm (the actual embedment of rod is 140mm) or deeper. Except 4 HRAs showing steel failure of rod, the minimum and average of test-to-prediction by ACI 318-14 ratios are 1.18 and 1.79, respectively. The tensile strength of HRAs, after shear load was applied to the HRAs, can be safely evaluated by the minimum among the concrete breakout strength and bond strength with the actual embedment length of the rod.

• Structural Engineering and Mechanics
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• v.26 no.2
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• pp.211-229
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• 2007

An experimental investigation to determine the transfer length of a seven-wire prestressing strand in different concretes is presented in this paper. A testing technique based on the analysis of bond behaviour by means of measuring the force supported by the prestressing strand on a series of specimens with different embedment lengths has been used. An analytical bond model to calculate the transfer length from an inelastic bond stress distribution along the transfer length has been obtained. A relationship between the plastic bond stress for transfer length and the concrete compressive strength at the time of prestress transfer has been found. An equation to predict the average and both the lower bound and the upper bound values of transfer length is proposed. The experimental results have not only been compared with the theoretical prediction from proposed equations in the literature, but also with experimental results obtained by several researchers.

• Smart Structures and Systems
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• v.17 no.4
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• pp.593-610
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• 2016

In this study, an innovative and smart glass fiber-reinforced polymer (GFRP) hybrid bar was developed for stronger durability of concrete structures. As comparing with the conventional GFRP bar, the smart GFRP Hybrid bar can promise to enhance the modulus of elasticity so that it makes the cracking reduced than the case when the conventional GFRP bar is used. Besides, the GFRP Hybrid bar can effectively resist the corrosion of conventional steel bar by the GFRP outer surface on the steel bar. In order to verify the bond performance of the GFRP hybrid bar for structural reinforcement, uniaxial pull-out test was conducted. The variables were the bar diameter and the number of strands and pitch of the fiber ribs. Tensile tests showed a excellent increase in the modulus of elasticity, 152.1 GPa, as compared to that of the pure GFRP bar (50 GPa). The stress-strain curve was bi-linear, so that the ductile performance could be obtained. For the bond test, the entire GFRP hybrid bar test specimens failed in concrete splitting due to higher shear strength resulting in concrete crushing as a function of bar deformation. Investigation revealed that an increase in the number of strands of fiber ribs enhanced the bond strength, and the pitch guaranteed the bond strength of 19.1 mm diameter hybrid bar with 15.9 mm diameter of core section of deformed steel the ACI 440 1R-15 equation is regarded as more suitable for predicting the bond strength of GFRP hybrid bars, whereas the CSA S806-12 prediction is considered too conservative and is largely influenced by the bar diameter. For further study, various geometrical and material properties such as concrete cover, cross-sectional ratio, and surface treatment should be considered.

• Smart Structures and Systems
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• v.26 no.4
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• pp.403-418
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• 2020

In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

• Steel and Composite Structures
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• v.51 no.5
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.