A new and robust hybrid artificial bee colony algorithm – ANN model for FRP-concrete bond strength evaluation

Jahangir, Hashem; Eidgahee, Danial Rezazadeh

doi:10.1016/j.compstruct.2020.113160

Cited by 80 publications

(32 citation statements)

References 55 publications

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“…The validation process evaluates the accuracy of the trained model. According to the literature survey, the feed forward and the feed forward back propagation (FFBP) neural networks are the most commonly used when solving engineering problems [70]. These types of ANN consist of input, hidden and output layers that contain neurons [71].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

et al. 2021

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Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

et al. 2021

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“…Furthermore, two thicknesses of the concrete core were considered: 80 mm and 100 mm. The width and length of the specimens were 300 mm and 250 mm, respectively, according to the standards [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]. The concrete mixes’ characteristics are represented in Table 3 .…”

Section: Materials and Specimens’ Specificationsmentioning

confidence: 99%

Experimental Investigation on the Shear Behaviour of Stud-Bolt Connectors of Steel-Concrete-Steel Fibre-Reinforced Recycled Aggregates Sandwich Panels

et al. 2021

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Steel-concrete-steel (SCS) sandwich panels are manufactured with two thin high-strength steel plates and a moderately low-density and low-strength thick concrete core. In this study, 24 specimens were produced and tested. In these specimens, a new stud-bolt connector was used to regulate its shear behaviour in sandwich panels. The bolts’ diameter, concrete core’s thickness and bolts’ spacing were the parameters under analysis. Furthermore, the concrete core was manufactured with normal-strength concrete and steel fibres concrete (SFC). Steel fibres were added at 1% by volume. In addition, the recycled coarse aggregate was used at 100% in terms of mass instead of natural coarse aggregate. Therefore, the ultimate bearing capability and slip of the sandwich panels were recorded, and the failure mode and ductility index of the specimens were evaluated. A new formula was also established to determine the shear strength of SCS panels with this kind of connectors. According to this study, increasing the diameter of the stud-bolts or using SFC in sandwich panels improve their shear strength and ductility ratio.

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“…In the paper [1] hybrid ANN and bee colony algorithm were applied to predict the FRP-concrete bond strength evaluation. The database contains 656 cases collected from literature where it were experimentally determined.…”

Section: Introductionmentioning

confidence: 99%

“…The authors analysed the papers in which algorithms based on ANNs were mostly applied to predict strength in models where: experiments are expensive, time consuming or complicated to calculate by computer simulation. The analysed literature shows that ANNs are especially useful and efficient when are applied in combination with other software algorithms like bee colony [1] or the gene expression algorithm [3].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Jet Engine Compressor Disc with Application of Artificial Neural Networks for Calculations Related to Time and Mass Criteria

Kieszek¹,

Kozakiewicz²,

Rogólski³

2021

Adv. Sci. Technol. Res. J.

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The paper presents the results of a series of numerical research on the possibility of applying Artificial Neural Networks (ANNs) for the ultimate strength calculations of selected parts of rotating machines. The layout and the principle of the algorithm operation were described, beginning from the general assumptions and then moving on to the detailed description of the subsequent modules. The effects of applying the algorithm were presented on the example of the compressor disc analysis. The significant benefits of its application were the reduction of optimization time by about 40% and the disc weight reduction by 0.5 kg. The accuracy of ANNs was different in each iteration of the presented algorithm. Finally, high accuracy of neural networks was achieved with the following mean values of relevant indices reached in the last iteration: RMSE = 0.5983, MAPA = 0.0733 and R 2 = 0.99895. The further perspectives of the undertaken research were defined at the end.

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A new and robust hybrid artificial bee colony algorithm – ANN model for FRP-concrete bond strength evaluation

Cited by 80 publications

References 55 publications

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

Experimental Investigation on the Shear Behaviour of Stud-Bolt Connectors of Steel-Concrete-Steel Fibre-Reinforced Recycled Aggregates Sandwich Panels

Optimization of a Jet Engine Compressor Disc with Application of Artificial Neural Networks for Calculations Related to Time and Mass Criteria

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