Prediction of mechanical and durability characteristics of concrete including slag and recycled aggregate concrete with artificial neural networks (ANNs)

Amiri, Mostafa; Hatami, Farzad

doi:10.1016/j.conbuildmat.2022.126839

Cited by 47 publications

(25 citation statements)

References 26 publications

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“…The ratio of the difference between O 1 and O 2 to the original output O is defined as the relative impact value matrix (RIVM). RIVM is finally averaged according to the number of samples to obtain the ARIV of each input parameter, as shown in Equations ( 11) and (12).…”

Section: Average Relative Impact Value (Ariv)mentioning

confidence: 99%

“…Although BPNN has primarily been used to predict concrete strength [8][9][10][11] and durability properties [12,13], several studies [14][15][16][17][18][19][20][21] on fatigue life prediction of concrete did demonstrate its rationality and effectiveness. Fatigue life prediction generally fits closer to the experimental results, and the model performs statistically better than the code equations [14].…”

Section: Introductionmentioning

confidence: 99%

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Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network

Chen

Sun

Zhong³

et al. 2022

Materials

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Concrete tensile properties usually govern the fatigue cracking of structural components such as bridge decks under repetitive loading. A fatigue life reliability analysis of commonly used ordinary cement concrete is desirable. As fatigue is affected by many interlinked factors whose effect is nonlinear, a unanimous consensus on the quantitative measurement of these factors has not yet been achieved. Benefiting from its unique self-learning ability and strong generalization capability, the Bayesian regularized backpropagation neural network (BR-BPNN) was proposed to predict concrete behavior in tensile fatigue. A total of 432 effective data points were collected from the literature, and an optimal model was determined with various combinations of network parameters. The average relative impact value (ARIV) was constructed to evaluate the correlation between fatigue life and its influencing parameters (maximum stress level Smax, stress ratio R, static strength f, failure probability P). ARIV results were compared with other factor assessment methods (weight equation and multiple linear regression analyses). Using BR-BPNN, S-N curves were obtained for the combinations of R = 0.1, 0.2, 0.5; f = 5, 6, 7 MPa; P = 5%, 50%, 95%. The tensile fatigue results under different testing conditions were finally compared for compatibility. It was concluded that Smax had the most significant negative effect on fatigue life; and the degree of influence of R, P, and f, which positively correlated with fatigue life, decreased successively. ARIV was confirmed as a feasible way to analyze the importance of parameters and could be recommended for future applications. It was found that the predicted logarithmic fatigue life agreed well with the test results and conventional data fitting curves, indicating the reliability of the BR-BPNN model in predicting concrete tensile fatigue behavior. These probabilistic fatigue curves could provide insights into fatigue test program design and fatigue evaluation. Since the overall correlation coefficient between the prediction and experimental results reached 0.99, the experimental results of plain concrete under flexural tension, axial tension, and splitting tension could be combined in future analyses. Besides utilizing the valuable fatigue test data available in the literature, this work provided evidence of the successful application of BR-BPNN on concrete fatigue prediction. Although a more accurate and comprehensive method was derived in the current study, caution should still be exercised when utilizing this method.

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Section: Average Relative Impact Value (Ariv)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network

Chen

Sun

Zhong³

et al. 2022

Materials

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“…In civil engineering, AI technique is one of the most effective tools in machine learning over the past decades to develop predication models that deal with highly nonlinear problems. Previous studies reveal that artificial neural network (ANN) is an effective tool for assessing the concrete performance while considering the effect of multiple parameters such as composition of ingredients, water cement ratio, and quantity of additives [47][48][49][50][51]. Recently, studies have utilized artificial intelligence (AI) and machine learning (ML) methods for the assessment and prediction of radiation shielding performance of concrete mixtures [52,53].…”

Section: Introductionmentioning

confidence: 99%

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

et al. 2022

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Concrete is an economical and efficient material for attenuating radiation. The potential of concrete in attenuating radiation is attributed to its density, which in turn depends on the mix design of concrete. This paper presents the findings of a study conducted to evaluate the radiation attenuation with varying water-cement ratio (w/c), thickness, density, and compressive strength of concrete. Three different types of concrete, i.e., normal concrete, barite, and magnetite containing concrete, were prepared to investigate this study. The radiation attenuation was calculated by studying the dose absorbed by the concrete and the linear attenuation coefficient. Additionally, artificial neural network (ANN) and gene expression programming (GEP) models were developed for predicting the radiation shielding capacity of concrete. A correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) were calculated as 0.999, 1.474 mGy, 2.154 mGy and 0.994, 5.07 mGy, 5.772 mGy for the training and validation sets of the ANN model, respectively. Similarly, for the GEP model, these values were recorded as 0.981, 13.17 mGy, and 20.20 mGy for the training set, whereas the validation data yielded R = 0.985, MAE = 12.2 mGy, and RMSE = 14.96 mGy. The statistical evaluation reflects that the developed models manifested close agreement between experimental and predicted results. In comparison, the ANN model surpassed the accuracy of the GEP models, yielding the highest R and the lowest MAE and RMSE. The parametric and sensitivity analysis revealed the thickness and density of concrete as the most influential parameters in contributing towards radiation shielding. The mathematical equation derived from the GEP models signifies its importance such that the equation can be easily used for future prediction of radiation shielding of high-density concrete.

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“…ANNs are mathematical models that simulate the neural frame of a human to process complex information. They are widely used for data prediction [ 39 , 40 ]. Liu used an ANN and a swarm intelligence algorithm to predict the carbonation depth of recycled concrete through nine parameters, including temperature, recycled aggregate replacement rate, water absorption, and exposure time, and the results showed that the ANN performed better than the conventional formula [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

et al. 2022

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The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (D/t) and the tensile strength of the FRP (ffrp) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.

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Prediction of mechanical and durability characteristics of concrete including slag and recycled aggregate concrete with artificial neural networks (ANNs)

Cited by 47 publications

References 26 publications

Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network

Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

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