Predicting Bond Strength between FRP Plates and Concrete Sub-strate: Applications of GMDH and MNLR Approaches

Hamze‐Ziabari, Seyed Mahmood; Yasavoli, Amir

doi:10.3151/jact.15.644

Cited by 17 publications

(12 citation statements)

References 34 publications

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“…The BPNN displayed a good potential usage. Hamze-Ziabari and Yasavoli 170 used group method of data handling and multivariate nonlinear regression to predict the bond strength between FRP plates and concrete substrate based on 342 experimental databases. The model reasonably predicted the bond strength, as it outperformed other existing statistical models.…”

Section: Heuristic Models Of Frp–concrete Bond Strengthmentioning

confidence: 99%

Recent developments in experimental and computational studies of hygrothermal effects on the bond between FRP and concrete

Mukhtar

Arowojolu

2020

Journal of Reinforced Plastics and Composites

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The performance of fiber reinforced polymer externally bonded to concrete is greatly influenced by the environmental conditions to which it is exposed during service. Temperature and humidity are the two common environmental factors that alter the bond behavior of externally bonded fiber reinforced polymer. This paper reviews the experimental and computational approaches used to evaluate the hygrothermal effects—that is, the effect of temperature and humidity—on the durability of the fiber reinforced polymer–concrete bond, as well as on the bond’s performance under loading conditions. Some experimental testing conducted in the laboratory and in situ are critically reviewed and presented. Implemented approaches for improving bond performance under hygrothermal conditions and their modeling techniques are also presented. The paper concludes by discussing the review’s salient issues. The ongoing wide application of externally bonded fiber reinforced polymer creates opportunities for new research on improving and predicting the bond strength of fiber reinforced polymer concrete under hygrothermal conditions.

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Section: Heuristic Models Of Frp–concrete Bond Strengthmentioning

confidence: 99%

Recent developments in experimental and computational studies of hygrothermal effects on the bond between FRP and concrete

Mukhtar

Arowojolu

2020

Journal of Reinforced Plastics and Composites

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“…The group method of data handling (GMDH) networks was investigated to measure the bond strength and perform a comparison with multiple linear and nonlinear regression models [21]. The GMDH model proved to outperform other models and improve accuracy [22]. An artificial neural network (ANN)-based model was built to predict the bond strength of concrete [23].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning-Based Approach for Predicting of FRP–Concrete Interfacial Bonding

Kim

Lee

et al. 2022

Mathematics

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Developments in fiber-reinforced polymer (FRP) composite materials have created a huge impact on civil engineering techniques. Bonding properties of FRP led to its wide usage with concrete structures for interfacial bonding. FRP materials show great promise for rehabilitation of existing infrastructure by strengthening concrete structures. Existing machine learning-based models for predicting the FRP–concrete bond strength have not attained maximum performance in evaluating the bond strength. This paper presents an ensemble machine learning approach capable of predicting the FRP–concrete interfacial bond strength. In this work, a dataset holding details of 855 single-lap shear tests on FRP–concrete interfacial bonds extracted from the literature is used to build a bond strength prediction model. Test results hold data of different material properties and geometrical parameters influencing the FRP–concrete interfacial bond. This study employs CatBoost algorithm, an improved ensemble machine learning approach used to accurately predict bond strength of FRP–concrete interface. The algorithm performance is compared with those of other ensemble methods (i.e., histogram gradient boosting algorithm, extreme gradient boosting algorithm, and random forest). The CatBoost algorithm outperforms other ensemble methods with various performance metrics (i.e., lower root mean square error (2.310), lower covariance (21.8%), lower integral absolute error (8.8%), and higher R-square (96.1%)). A comparative study is performed between the proposed model and best performing bond strength prediction models in the literature. The results show that FRP–concrete interfacial bonding can be effectively predicted using proposed ensemble method.

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“…Next, certain empirical prediction models were devised and incorporated in relevant design codes based on theoretical analysis and experimental validation. However, most of these models were developed using limited experiment datasets, which may make them exact within these data space but lack sufficient generalization capacity for other parameter settings [ 27 , 28 ]. An example is the standard empirical model reported in the American Concrete Institute (ACI) Committee 440 Guide for the Design and Construction of Structural Concrete Reinforced with FRP Bars that was used to traditionally estimate the BS of FRP (ACI 440.1 R-06 [ 29 ]).…”

Section: Introductionmentioning

confidence: 99%

“…With the advancement of computer science and the increasing volume of associated experimental datasets, data-driven approaches based on machine learning (ML) algorithms have recently emerged as alternative methods for establishing prediction models using comprehensive experimental data and information [ 35 , 36 , 37 , 38 , 39 ]. Some of the most commonly and successfully deployed ML algorithms for estimating the BS of FRP are artificial neural networks (ANNs), support vector machines (SVMs), multiple linear regression (MLR), genetic and evolutionary algorithms (GEAs), random forest (RF), and ensemble learning (gradient boosted regression trees [GBRT]) [ 18 , 27 , 28 , 35 , 40 , 41 , 42 , 43 , 44 , 45 ]. Thakur et al [ 13 ] proposed a bagged M5P tree regression model out of six different models for the prediction of the bonding strength of FRP bars embedded in concrete.…”

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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Predicting Bond Strength between FRP Plates and Concrete Sub-strate: Applications of GMDH and MNLR Approaches

Cited by 17 publications

References 34 publications

Recent developments in experimental and computational studies of hygrothermal effects on the bond between FRP and concrete

Recent developments in experimental and computational studies of hygrothermal effects on the bond between FRP and concrete

Ensemble Machine Learning-Based Approach for Predicting of FRP–Concrete Interfacial Bonding

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

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