Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete

Su, Miao; Zhong, Qingyu; Peng, Hui; Li, Shaofan

doi:10.1016/j.conbuildmat.2020.121456

Cited by 131 publications

(45 citation statements)

References 43 publications

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“…The artificial neural network (ANN) is a computational technique used for predicting and fitting complex datasets [61,67]. The ANN is unique from other methods due to its ability to learn like a human brain [64,68]. ANN predictions are thus considered to provide logical and intelligent solutions due to their self-learning capacity [61,65,68].…”

Section: Bond Strength Prediction Using An Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The ANN is unique from other methods due to its ability to learn like a human brain [64,68]. ANN predictions are thus considered to provide logical and intelligent solutions due to their self-learning capacity [61,65,68]. The architectural framework of an ANN consists of three main parts-namely, the input layer, hidden layer, and output layer [61,64,67].…”

Section: Bond Strength Prediction Using An Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The input layer consists of an input dataset where the different parameters for prediction are entered. In the hidden layer, there is a collection of nodes known as neurons, which represents the interrelationship between each of the input parameters [66,69]; this is the layer where mathematical computations are executed in order to provide the desired or targeted output in the output layer [61,68,70]. The neurons in the hidden layer can be assigned weights or bias in order to modify the targeted output, and are decided by learning and memorizing each input with the most desired output [71][72][73].…”

Section: Bond Strength Prediction Using An Artificial Neural Network (Ann)mentioning

confidence: 99%

“…This was obtained by training the network continually using the (nntraintool), which updates the weights until the mean squared error is small. However, repetitive training may cause the model to memorize the target values and result in overfitting of the model, and hence it is necessary to carry out regularization and identify the early fit during the training process[61,68,74]. Figure27below shows the best validation performance of the ANN model at epoch 4.Appl.…”

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confidence: 99%

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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

Rahman

Al-Ameri

2021

Applied Sciences

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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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Section: Bond Strength Prediction Using An Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Bond Strength Prediction Using An Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Bond Strength Prediction Using An Artificial Neural Network (Ann)mentioning

confidence: 99%

mentioning

confidence: 99%

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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

Rahman

Al-Ameri

2021

Applied Sciences

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“…It has the ability to avoid overtraining and has better generalization capability than artificial neural network models. Some applications of SVM in civil engineering problems include concrete workability prediction [33], slope reliability analysis [34], settlement studies of shallow foundations [35], seismic liquefaction assessment [36], and prediction of tunnel water gushing [37]. In addition, relevance vector machine (RVM) is a nonlinear relation machine learning method which is popular in recent years [38]- [40].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hydration Heat of Mass Concrete Based on the SVR Model

et al. 2021

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Thermal cracking in pile caps caused by concrete hydration heat will affect the safety and durability of long-span cable-stayed bridges. Therefore, effective prediction and control of concrete bridges hydration heat has been a challenging problem. In this study, the temperature of hydration heat in mass concrete pile caps belonging to a long-span cable-stayed bridge in China were monitored. Then, we adopt support vector machine regression (SVR) to establish the correlation between influencing variables and the temperature of hydration heat. The monitoring data are used to train to realize the short-term prediction of concrete temperature. The predicted results show that the SVR has a high accuracy, and the deviation between the prediction results and the measured values is quite small. The prediction performance of SVR for temperature of hydration heat of mass concrete is obviously better than that of BP neural network. The SVR prediction model can predict the temperature of 2-3 days with high accuracy. Based on the prediction results, temperature control method can be taken in advance to reduce the possibility of thermal cracks, which is of great significance for the safety and durability of actual engineering construction.INDEX TERMS Mass concrete, heat of hydration, support vector machine, regression model, temperature prediction.

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Machine Learning Models in Prediction of Strength Parameters of FRP‐Wrapped RC Beams

Kumar¹,

Arora²,

Kapoor³

et al. 2023

Machine Intelligence, Big Data Analytics, and IoT in Image Processing

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Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete

Cited by 131 publications

References 43 publications

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

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

Prediction of Hydration Heat of Mass Concrete Based on the SVR Model

Machine Learning Models in Prediction of Strength Parameters of FRP‐Wrapped RC Beams

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