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

Chuang, Ying-Ji; Tsai, Hsing‐Chih

doi:10.1108/ec-05-2020-0258

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…A new branch of genetic programming called multigene genetic programming (MGGP) was also proposed, relying on its remarkable prediction capabilities to estimate the BS of FRP bars. Considering its successful implementation and lofty performance in different studies, gene expression programming (GEP) was chosen in this study to estimate the BS of FRP [ 31 , 41 , 42 , 46 , 47 ]. Free from computational issues of slow convergence rates and local minimum convergence, GEP uses a linear constant-length expression tree (ET), a mathematical expression representation arranged in a tree-like the structure of data.…”

Section: Introductionmentioning

confidence: 99%

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

et al. 2022

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Rebars made of fiber-reinforced plastic (FRP) might be the future reinforcing material, replacing mild steel rebars, which are prone to corrosion. The bond characteristics of FRP rebars differ from those of mild steel rebars due to their different stress-strain behavior than mild steel. As a result, determining the bond strength (BS) qualities of FRP rebars is critical. In this work, BS data for FRP rebars was investigated, utilizing non-linear capabilities of gene expression programming (GEP) on 273 samples. The BS of FRP and concrete was considered a function of bar surface (Bs), bar diameter (db), concrete compressive strength (fc′), concrete-cover-bar-diameter ratio (c/d), and embedment-length-bar-diameter ratio (l/d). The investigation of the variable number of genetic parameters such as number of chromosomes, head size, and number of genes was undertaken such that 11 different models (M1–M11) were created. The results of accuracy evaluation parameters, namely coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) imply that the M11 model outperforms other created models for the training and testing stages, with values of (0.925, 0.751, 1.08) and (0.9285, 0.802, 1.11), respectively. The values of R2 and error indices showed that there is very close agreement between the experimental and predicted results. 30 number chromosomes, 9 head size, and 5 genes yielded the optimum model. The parametric analysis revealed that db, c/d, and l/d significantly affected the BS. The FRP rebar diameter size is greater than 10 mm, whereas a l/d ratio of more than 12 showed a considerable decrease in BS. In contrast, the rise in c/d ratio revealed second-degree increasing trend of BS.

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Section: Introductionmentioning

confidence: 99%

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

et al. 2022

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Estimating the Bond Strength of FRP Bars Using a Hybrid Machine Learning Model

Liu

Cheng³

2022

Buildings

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Although the use of fiber-reinforced plastic (FRP) rebars instead of mild steel can effectively avoid rebar corrosion, the bonding performance gets weakened. To accurately estimate the bond strength of FRP bars, this paper proposes a particle swarm optimization-based extreme learning machine model based on 222 samples. The model used six variables including the bar position (P), bar surface condition (SC), bar diameter (D), concrete compressive strength (fc), the ratio of the bar depth to the bar diameter (L/D), and the ratio of the concrete protective layer thickness to the bar diameter (C/D) as input features, and the relative importance of the input parameters was quantified using a sensitivity analysis. The results showed that the proposed model can effectively and accurately estimate the bond strength of the FRP bar with R2 = 0.945 compared with the R2 = 0.926 of the original ELM model, which shows that the model can be used as an auxiliary tool for the bond performance analysis of FRP bars. The results of the sensitivity analysis indicate that the parameter L/D is of the greatest importance to the output bond strength.

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

Cited by 2 publications

References 28 publications

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

Estimating the Bond Strength of FRP Bars Using a Hybrid Machine Learning Model

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