Using genetic programming to model the bond strength of GFRP bars in concrete under the effects of design guidelines

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ying-Ji Chuang ◽  
Hsing-Chih Tsai
Keyword(s):  
Compressive Strength ◽  
Genetic Programming ◽  
Bond Strength ◽  
Design Guidelines ◽  
Concrete Compressive Strength ◽  
Design Guideline ◽  
Content Type ◽  
Gfrp Bars ◽  
Influence Parameter ◽  
Bar Diameter

Purpose This paper aims to use a derivative of genetic programming to predict the bond strength of glass fiber-reinforced polymer (GFRP) bars in concrete under the effects of design guidelines. In developing bond strength prediction models, this paper prioritized simplicity and meaningfulness over extreme accuracy. Design/methodology/approach Assessing the bond strength of GFRP bars in concrete is a critical issue in designing and building reinforced concrete structures. Findings Ultimately, the equation of a linear form of a particular design guideline was suggested as the optimal prediction model. Improvements to the current design guidelines suggested by this model include setting a 1.31 magnification and considering the effects of the three significant parameters of bar diameter (db), minimum cover-to-bar diameter (C/db) and development length to bar diameter (l/db) under an acceptable root mean square error accuracy of around 2 MPa. Furthermore, the model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations. Originality/value The model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations.

scholarly journals Experimental study on the effect of concrete strength and corrosion level on bond between steel bar and concrete

2021 ◽  
Vol 72 (4) ◽  
pp. 498-509
Author(s):  
Vuong Doan Dinh Thien ◽  
Hung Nguyen Thanh ◽  
Hung Nguyen Dinh
Keyword(s):  
Compressive Strength ◽  
Bond Strength ◽  
Concrete Strength ◽  
Concrete Block ◽  
Bond Stress ◽  
Concrete Compressive Strength ◽  
Pull Out ◽  
Steel Bar ◽  
Stress Degradation ◽  
Slip Displacement

Corrosion of the steel reinforcement bars reduces the area of the steel bar and the bond stress between the steel bars and around concrete that decreases the capacity of concrete structures. In this study, the bond stress between steel bar with a diameter of 12mm and concrete was examined with the effect of different corrosion levels and different concrete grades. A steel bar was inserted in a concrete block with a size of 20×20×20cm. The compressive strength of concrete was 25.6MPa, 35.1MPa, and 44.1MPa. These specimens were soaked into solution NaCl 3.5% to accelerate the corrosion process with different corrosion levels in the length of 60mm. The pull-out test was conducted. Results showed that the bond strength of the corroded steel bar was higher than that predicted from CEB-FIP. Slip displacement and the range of slip displacement at the bond strength were reduced when the concrete compressive strength was increased. The rate of bond stress degradation occurred faster with the increment of the corrosion level when the concrete compressive strength was increased.

scholarly journals Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Palika Chopra ◽  
Rajendra Kumar Sharma ◽  
Maneek Kumar
Keyword(s):  
Compressive Strength ◽  
Genetic Programming ◽  
Prediction Models ◽  
Model Development ◽  
Strength Prediction ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool.

Axial monotonic and cyclic compression behaviour of hollow-bar micropiles

2015 ◽  
Vol 52 (4) ◽  
pp. 426-441 ◽  
Author(s):  
Osama F. El Hadi Drbe ◽  
M. Hesham El Naggar
Keyword(s):  
Bond Strength ◽  
Ground Improvement ◽  
Design Guidelines ◽  
Diameter Ratio ◽  
Load Test ◽  
Drill Bit ◽  
Type B ◽  
Load Tests ◽  
Drilling Bit ◽  
Bar Diameter

Micropiles are used in various applications, including low-capacity micropile networks, underpinning, and seismic retrofitting of existing foundations and high-capacity foundations for new structures. Hollow-bar micropiles have an added advantage, as they provide fast installation with a high degree of ground improvement. The current Federal Highway Administration (FHWA) design guidelines designate hollow-bar micropiles as type B, even though the FHWA construction technique is different than the technique used for typical type B, which results in an overly conservative design. In addition, the current practice for construction of hollow-bar micropiles is limited to a drilling bit / hollow-bar diameter ratio of 2.5 or less. In this paper, full-scale load tests were conducted to evaluate the suitability of FHWA design guidelines to hollow-bar micropiles installed in cohesive soil and to evaluate the performance of hollow-bar micropiles constructed with a drilling bit / hollow-bar diameter ratio of 3. Eight micropiles were constructed using 76 mm (3 in.) hollow bars (76 mm outside diameter and 48 mm inside diameter) with the air–water flushing technique and advanced to a depth of 5.75 m: six micropiles were installed using a 228 mm (9 in.) drill bit and two micropiles were installed using a 178 mm (7 in.) drill bit. All micropiles were instrumented with vibrating wire strain gauges to measure the axial strain at three stations along the micropile shaft. The load tests included four axial monotonic and four cyclic axial loading tests. The results are presented and discussed in terms of load–displacement curves and load transfer mechanism. The load test results showed that the grout–ground bond strength values proposed by the FHWA (in 2005) for type B micropiles grossly underestimate the bond strength for calculating the ultimate capacity. In addition, the toe resistance can be significant for micropiles resting on sand due to the increased toe diameter. No tangent stiffness degradation was observed in the micropile capacity after applying 15 load cycles.

Plastic hinges mechano-reliability analysis in the beams of RC frames structures

2020 ◽  
Vol 17 (5) ◽  
pp. 719-732
Author(s):  
Leyla Bouzid ◽  
Mohand Hamizi ◽  
Naceur-Eddine Hannachi ◽  
Aghiles Nekmouche ◽  
Karim Akkouche
Keyword(s):  
Compressive Strength ◽  
Yield Stress ◽  
Failure Probability ◽  
Limit State ◽  
State Function ◽  
Concrete Compressive Strength ◽  
Plastic Hinges ◽  
Content Type ◽  
Steel Bars ◽  
The Impact

Purpose The purpose of this study is to establish a relationship between causes and effects, the respect of materials characteristics values [concrete compressive strength (fc) and steel yield stress (fy)] and the norms of the construction dispositions value (covers). This study is motivated by the post-seismic damages related to the plastification of the reinforced concrete (RC)/beams sections, named plastic hinges. The results are given by fragility curves representing the failure probability (Pf) of the plastic hinges versus covers value. Design/methodology/approach A mechanical-reliability coupling methodology is proposed and performed on three frames (three, six and nine storey). For each frame, seven covers the value of reinforcement steel bars has been taken into account in the beams. After definition of the limit state function G(x), a process of idea to twin-track; deterministic and probabilistic, is considered. Thus, numerical simulations are carried out under ETABS© software, to extract a soliciting moments Ms(x). Then, ultimate moments Mu(x), the result of reliability approach are calculated using Monte Carlo Simulations. In this step, two random variables; concrete compressive strength in 28 days of age (fc) and steel yield stress (fy), have been studied. Findings In the mechanical study, the results show that, the first plastic hinge appears at the beams for all frames. In the reliability study, the (fy) variation shows that all plastic hinges are in failure domain, nevertheless, the (fc) variation leads to have all sections in the safety domain, except A7 and B7 models. The failure probability (Pf) calculation according to (fc) and (fy) shows that an absolute error of 0.5 cm in the steel bars covers can switch the frame from the safety domain to the failure domain. Originality/value The plastic hinges reliability of the RC/ frame structures is independent on the high of the structure. The (fc) random variable according to the used distribution law does not affect the reliability (safety or failure). However, the impact of the steel yield stress variation (fy) is not negligible. The errors in covers affect considerably the strength of the elements.

Study on Strength and Bond Characteristics of GGBS Concrete

2006 ◽  
Vol 302-303 ◽  
pp. 561-566 ◽  
Author(s):  
Jun-Wu Xia ◽  
Shou Xiang Wang ◽  
Hong Fei Chang ◽  
Hua-Qun Peng ◽  
Long Jiang ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Bond Strength ◽  
Reinforcing Steel ◽  
Concrete Compressive Strength ◽  
Mixture Proportioning ◽  
Industry Waste ◽  
Slag Powder ◽  
Steel Bar ◽  
Blastfurnace Slag ◽  
Bond Characteristics

The influence of GGBS (ground granulated blastfurnace slag) on concrete compressive strength is experimentally investigated in this paper. By bond strength experiment of GGBS concrete with reinforcing steel bar, the influence of GGBS mixture proportioning on bond strength is studied. The result indicates that, with an increase replacement percentage of GGBS, the bond strength between concrete and steel bar will decrease, and the depressed magnitude relates to the dosage of slag powder and type of steel bar. The studies provide a reference for the exploitation and application of GGBS concrete. As known to all, blastfurnace slag is a kind of industry waste produced by blastfurnace puddling. A great deal of research has been conducted on the characteristics of slag powder. The improvements of GGBS on the concrete composites are widely accepted. Studies on GGBS concrete bring remarkable social and economical benefits such as: reducing cost of per cube concrete; improving concrete performance and making full use of industry waste residue. The influence of GGBS mixture proportioning on concrete compressive strength and the bond characteristics between concrete and steel bar is experimentally studied in this paper.

scholarly journals Performance of concrete beams partially/fully reinforced with glass fiber polymer bars

2021 ◽  
Vol 68 (1) ◽  
Author(s):  
Mohamed S. Moawad ◽  
Ahmed Fawzi
Keyword(s):  
Compressive Strength ◽  
Glass Fiber ◽  
Concrete Beams ◽  
Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Concrete Compressive Strength ◽  
Glass Fiber Reinforced Polymer ◽  
Gfrp Bars ◽  
Reinforced Polymer ◽  
Glass Fiber Reinforced

AbstractOne of the major advantages of using glass fiber-reinforced polymer bars as a replacement to the traditional steel-reinforced bars is its lightweight and high-resistant to corrosion. This research focuses on the performance of concrete beams partially/fully reinforced with glass fiber-reinforced polymer bars with 50% of GFRP bars were used to reinforce partially concrete beams at flexural zone. While 100% of GFRP bars were used to reinforce fully concrete beams at flexural and compression zones with different concrete compressive strength.This study reported the test results of 6 reinforced concrete beams with dimensions 150 × 200mm and a 1700-mm clear span length subjected to a four-point loading system. The tested beams were divided into three groups; the first one refers to the glass fiber-reinforced polymer bar effect. The second group is referring to the effect of concrete compressive strength, while the third group is referring to the effect of the GFRP bar volume ratio.Using longitudinal GFRP bars as a full or partial replacement of longitudinal steel bar reinforcement led to an increase in the failure load capacity and the average crack width, while a decrease in ductility was reported with a lower number of cracks. Increasing the concrete compressive strength is more compatible with GFRP bar reinforcement and enhanced the failure performance of beams compared with normal compressive strength concrete.

scholarly journals Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams

2020 ◽  
Vol 71 (7) ◽  
pp. 814-827
Author(s):  
Nguyen Thuy Anh ◽  
Ly Hai Bang
Keyword(s):  
Bond Strength ◽  
Concrete Structures ◽  
Diameter Ratio ◽  
Design Solution ◽  
Transverse Reinforcement ◽  
Critical Element ◽  
Ann Model ◽  
Gfrp Bars ◽  
Ann Models ◽  
Bar Diameter

The use of glass fiber-reinforced polymer (GFRP) has gained increasing attention over the past decades, aiming at replacing traditional steel rebar in concrete structures, especially in corrosion or magnetic conditions. Understanding the working mechanism between the reinforcements and concrete is crucial in many practical applications, in which the corresponding bond strength is considered as a critical element. In this study, a database including 159 experimental beam results gathered from the available literature was used for the development of an artificial neural network (ANN) model in an effort to predict the bond strength between GFRP bars and concrete. Two ANN models using BFGS quasi-Newton backpropagation and conjugate gradient backpropagation with Polak-Ribiére algorithms were constructed and evaluated in terms of bond strength prediction accuracy. The considered database consisted of five input parameters, including the bar diameter, concrete compressive strength, minimum cover to bar diameter ratio, bar development length to bar diameter ratio, the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameter. The evaluation of the models was conducted and compared using well-known statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results demonstrated that both ANN models could accurately predict the bond strength between GFRP bars and concrete, paving the way for engineers to possess a useful alternative design solution for reinforced concrete structures

Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams

2020 ◽  
Vol 71 (7) ◽  
pp. 814-827
Author(s):  
Nguyen Thuy-Anh ◽  
Ly Hai-Bang
Keyword(s):  
Bond Strength ◽  
Concrete Structures ◽  
Diameter Ratio ◽  
Design Solution ◽  
Transverse Reinforcement ◽  
Critical Element ◽  
Ann Model ◽  
Gfrp Bars ◽  
Ann Models ◽  
Bar Diameter

The use of glass fiber-reinforced polymer (GFRP) has gained increasing attention over the past decades, aiming at replacing traditional steel rebar in concrete structures, especially in corrosion or magnetic conditions. Understanding the working mechanism between the reinforcements and concrete is crucial in many practical applications, in which the corresponding bond strength is considered as a critical element. In this study, a database including 159 experimental beam results gathered from the available literature was used for the development of an artificial neural network (ANN) model in an effort to predict the bond strength between GFRP bars and concrete. Two ANN models using BFGS quasi-Newton backpropagation and conjugate gradient backpropagation with Polak-Ribiére algorithms were constructed and evaluated in terms of bond strength prediction accuracy. The considered database consisted of five input parameters, including the bar diameter, concrete compressive strength, minimum cover to bar diameter ratio, bar development length to bar diameter ratio, the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameter. The evaluation of the models was conducted and compared using well-known statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results demonstrated that both ANN models could accurately predict the bond strength between GFRP bars and concrete, paving the way for engineers to possess a useful alternative design solution for reinforced concrete structures

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