Using a Hamming Neural Network to Predict Bond Strength of Welded Connections

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.

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

Applied Sciences ◽

10.3390/app11114889 ◽

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.

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