Using Application of an Artificial Neural Network System to Backcalculate Pavement Elastic Modulus

Elshamy, M. M. M.; Tiraturyan, Artem

doi:10.36622/vstu.2020.2.46.006

Cited by 4 publications

(2 citation statements)

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“…The backpropagating algorithm is characterized by its ability to change the neuron weights to reduce the differences between the goals and the output values of the algorithm using the error reduction technique [26]. The final set of node biases and connection weights is known when the error rate is reduced to permissible limits [27].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the elastic modulus of pavement layers using different types of neural networks models

Elshamy

Tiraturyan

Uglova

2022

Vestnik Donskogo gosudarstvennogo tehničeskogo universiteta

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Introduction. This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions. The falling weight deflectometer (FWD) was selected to simulate the dynamic traffic loads and measure the flexural bowls on the road surface to obtain the database of ANN models.Materials and Methods. Artificial networks types (the feedforward backpropagation, layer-recurrent, cascade back- propagation, and Elman backpropagation) are developed to define the optimal ANN model using Matlab software. To appreciate the efficiency of every model, we used the constructed ANN models for predicting the elastic modulus values for 25 new pavement sections that were not used in the process of training, validation, or testing to ensure its suitability. The efficiency measures such as mean absolute error (MAE), the coefficient of multiple determinations R2, Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) values were obtained for all models results.Results. Based on the performance parameters, it was concluded that among these algorithms, the feed-forward model has a better performance compared to the other three ANN types. The results of the best four models were compared to each other and to the actual data obtained to determine the best method.Discussion and Conclusions. The differences between the results of the four best models for the four types of algorithms used were very small, as they showed the closeness between them and the actual values. The research results confirm the possibility of ANN-based models to evaluate the elastic modulus of pavement layers speedily and reliably for using it in the structural assessment of (NDT) flexible pavement data at the appropriate time.

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Section: Methodsmentioning

confidence: 99%

Evaluation of the elastic modulus of pavement layers using different types of neural networks models

Elshamy

Tiraturyan

Uglova

2022

Vestnik Donskogo gosudarstvennogo tehničeskogo universiteta

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“…Works [ 19 , 20 , 21 ] are examples of determining the properties of pavement materials with the help of an ANN. Papers [ 22 , 23 ] propose a method for determining the chemical, physical and mechanical properties of polymers based on their molecular structure using machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Determining the Rheological Parameters of Polymers Using Artificial Neural Networks

Чепурненко

2022

Polymers

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Artificial neural networks have great prospects in solving the problems of predicting the properties of polymers. The purpose of this work was to study the possibility of using artificial neural networks to determine the rheological parameters of polymers from stress relaxation curves. The nonlinear Maxwell–Gurevich equation was used as the deformation law. The problem was solved in the MATLAB environment. The substantiation for the choice of the neural network input and output parameters was made. An algorithm for obtaining the data for neural network training was also proposed. Neural networks were trained on theoretical stress relaxation curves constructed with the Euler method. The value of the mean square error (MSE) was used as a criterion for the performance of the training. The constructed model of the artificial neural network was tested on the experimental relaxation curves of recycled polyvinyl chloride. The quality of the experimental curve approximation was quite good and was comparable with the standard methods for processing stress relaxation curves. Unlike the standard methods, when using artificial neural networks, no preliminary data smoothing was required. It is possible to use the proposed technique for processing not only relaxation curves, but also creep curves as well as processing creep tests not only in central tension, but also in bending, torsion and shear.

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Prediction of Multi-layered Pavement Moduli Based on Falling Weight Deflectometer Test Using Soft Computing Approaches

Phulsawat,

Senjuntichai,

Senjuntichai

2024

Transp. Infrastruct. Geotech.

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Using Application of an Artificial Neural Network System to Backcalculate Pavement Elastic Modulus

Cited by 4 publications

References 0 publications

Evaluation of the elastic modulus of pavement layers using different types of neural networks models

Evaluation of the elastic modulus of pavement layers using different types of neural networks models

Determining the Rheological Parameters of Polymers Using Artificial Neural Networks

Prediction of Multi-layered Pavement Moduli Based on Falling Weight Deflectometer Test Using Soft Computing Approaches

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