scholarly journals Experimental Investigation and Artificial Neural Network Based Prediction of Bond Strength in Self-Compacting Geopolymer Concrete Reinforced with Basalt FRP Bars

2021 ◽  
Vol 11 (11) ◽  
pp. 4889
Author(s):  
Sherin Khadeeja Rahman ◽  
Riyadh Al-Ameri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bond Strength ◽  
Carbon Emissions ◽  
Geopolymer Concrete ◽  
Corrosion Attack ◽  
Pull Out ◽  
Artificial Neural ◽  
Bar Diameter ◽  
Frp Bars

The current research on concrete and cementitious materials focuses on finding sustainable solutions to address critical issues, such as increased carbon emissions, or corrosion attack associated with reinforced concrete structures. Geopolymer concrete is considered to be an eco-friendly alternative due to its superior properties in terms of reduced carbon emissions and durability. Similarly, the use of fibre-reinforced polymer (FRP) bars to address corrosion attack in steel-reinforced structures is also gaining momentum. This paper investigates the bond performance of a newly developed self-compacting geopolymer concrete (SCGC) reinforced with basalt FRP (BFRP) bars. This study examines the bond behaviour of BFRP-reinforced SCGC specimens with variables such as bar diameter (6 mm and 10 mm) and embedment lengths. The embedment lengths adopted are 5, 10, and 15 times the bar diameter (db), and are denoted as 5 db, 10 db, and 15 db throughout the study. A total of 21 specimens, inclusive of the variable parameters, are subjected to direct pull-out tests in order to assess the bond between the rebar and the concrete. The result is then compared with the SCGC reinforced with traditional steel bars, in accordance with the ACI 440.3R-04 and CAN/CSA-S806-02 guidelines. A prediction model for bond strength has been proposed using artificial neural network (ANN) tools, which contributes to the new knowledge on the use of Basalt FRP bars as internal reinforcement in an ambient-cured self-compacting geopolymer concrete.

scholarly journals Artificial neural network for predicting the flexural bond strength of FRP bars in concrete

2019 ◽  
Vol 26 (1) ◽  
pp. 12-29 ◽  
Author(s):  
Mehmet Alpaslan Köroğlu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bond Strength ◽  
Diameter Ratio ◽  
Transverse Reinforcement ◽  
Ann Model ◽  
Type Specimens ◽  
Artificial Neural ◽  
Bar Diameter ◽  
Frp Bars

AbstractThe bond strength between fibre-reinforced polymer (FRP) rebars and concrete is one of the most significant aspects of composite behaviour for rebars and concrete. In this study, a database of 408 beam type specimens consisting of beam end specimens, beam anchorage specimens and splice beam specimens was compiled from the current literature and used to develop a simple prediction using the artificial neural network (ANN). The data used for modelling were organised in a format of eight input parameters that include FRP type, cover bar surface, confinement, bar diameter (db), concrete compressive strength $(\sqrt {{f_c}} )$, minimum cover-to-bar-diameter ratio (c/db), bar-development-length-to-bar-diameter ratio (l/db), and the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameters (Atr/sndb). Additionally, a simple prediction formula by regression analysis was developed. The root mean square error and R2 values of the testing data were found in order to compare the results of both ANN and the proposed model with existing regulations. The new ANN model predicts the bond strength of FRP bars in reinforced concrete with 0.8989 R2, thus yielding better results when compared with existing regulations.

Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic

2012 ◽  
Vol 36 ◽  
pp. 411-418 ◽  
Author(s):  
Emadaldin Mohammadi Golafshani ◽  
Alireza Rahai ◽  
Mohammad Hassan Sebt ◽  
Hamed Akbarpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Bond Strength ◽  
Steel Bars ◽  
Artificial Neural

scholarly journals Bond Behaviour Between Reinforcing Bars and Geopolymer Concrete By Using Pull-out Test

2019 ◽  
Vol 280 ◽  
pp. 04008
Author(s):  
Ratni Nurwidayati ◽  
Januarti Ekaputri ◽  
Triwulan ◽  
Priyo Suprobo
Keyword(s):  
Bond Strength ◽  
Sodium Hydroxide ◽  
Concrete Cover ◽  
Raw Material ◽  
Maximum Diameter ◽  
Geopolymer Concrete ◽  
Bond Behaviour ◽  
Reinforcing Bar ◽  
Pull Out ◽  
Bar Diameter

This paper presents the effect of the reinforcing bar diameter (db) and concrete cover thickness to reinforcing bar diameter ratio (c/db) to the bond strength between reinforcing bar and geopolymer concrete by using the experimental pull-out test. The mass ratio of sodium hydroxide (NaOH) to sodium silicate (Na2SiO3) was 2.5 with an 8 M concentration of sodium hydroxide were used in this research. Class F fly ash from Suralaya Power Station, Banten, Indonesia was used as raw material to produce geopolymer concrete. The maximum diameter of coarse aggregate was 10 mm. The result indicated that the maximum pull-out load on reinforcing bar diameter of 16 mm was higher than the diameter of 13 mm. The pull-out failure occurred on the ratio of c/db more than equal of 4.3. The bond strength increased as the ratio of c/db increased, up to 4.3. However, more than 4.3 was the insignificant effect.

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