A statistical model for reinforced concrete bond prediction

Dahou, Zohra; Sbartaï, Zoubir Mehdi; Castel, Arnaud; Ghomari, Fouad

doi:10.1201/b10546-14

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…The backpropagation (BP) algorithm is commonly employed to optimize parameters in NN algorithms and BPNN has been widely adopted in civil engineering applications [35,36]. Using the BP algorithm, the signal experiences both forward propagation and backward error propagation.…”

Section: Ann Model Developmentmentioning

confidence: 99%

Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network

Hou

et al. 2021

PLoS ONE

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To address the problem of low accuracy and poor robustness of in situ testing of the compressive strength of high-performance self-compacting concrete (SCC), a genetic algorithm (GA)-optimized backpropagation neural network (BPNN) model was established to predict the compressive strength of SCC. Experiments based on two concrete nondestructive testing methods, i.e., ultrasonic pulse velocity and Schmidt rebound hammer, were designed and test sample data were obtained. A neural network topology with two input nodes, 19 hidden nodes, and one output node was constructed, and the initial weights and thresholds of the resulting traditional BPNN model were optimized using GA. The results showed a correlation coefficient of 0.967 between the values predicted by the established BPNN model and the test values, with an RMSE of 3.703, compared to a correlation coefficient of 0.979 between the values predicted by the GA-optimized BPNN model and the test values, with an RMSE of 2.972. The excellent agreement between the predicted and test values demonstrates the model can accurately predict the compressive strength of SCC and hence reduce the cost and time for SCC compressive strength testing.

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Section: Ann Model Developmentmentioning

confidence: 99%

Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network

Hou

et al. 2021

PLoS ONE

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“…It was indicated that ANN model and experimental results were in good agreement with the other authors' models. 25 Different from other studies, Tanyildizi used fuzzy logic method to estimate the adherence of reinforcement with lightweight concrete. In the models, concrete mixture properties and change of curing conditions were used as input data.…”

Section: Introductionmentioning

confidence: 99%

Pull‐out capacity prediction of sustainable cementitious composites with artificial intelligence and statistical methods

Ünsalan

Bankır

Cansız

2023

Structural Concrete

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Concrete is used with reinforcement in structures so adherence gains importance especially in fire scenarios. To contribute production of sustainable concrete, by-products like granulated blast furnace slag, fly ash, and silica fume were used, generally. In this study, to determine the elevated temperature resistance of by-product-added composites, samples were exposed to 250, 500, and 750 C and bonding behavior of composites was determined with pull-out test. Moreover, prediction models were developed to estimate concrete-reinforcement adherence values without experimentation and importantly before a fire. For the prediction models, considering the cross correlation, the mixture type, temperature, compressive strength, and flexural strength of concretes were selected as independent variables different from literature and pull-out capacity (POC) as dependent variable. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used for prediction models. As a statistical method, multiple linear regression was used. The performances of pull out prediction models were compared according to coefficient of correlation (R), mean square error (MSE), and mean percentage error (MPE). Among the statistical models, Quadratic and pure quadratic performance surpassed the ANFIS model, which is a combination of ANN and fuzzy techniques. The best performance was obtained in ANN model with 99.85%, 3.65 kg, 3.1% for R, MSE and MPE, respectively. Therefore, ANN can provide a new applicable model to effectively predict POC of by-product-added cementituous composites under fire effect.

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A statistical model for reinforced concrete bond prediction

Cited by 2 publications

References 2 publications

Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network

Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network

Pull‐out capacity prediction of sustainable cementitious composites with artificial intelligence and statistical methods

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