An artificial neural network model for virtual Superpave asphalt mixture design

Öztürk, Hande İşık; Kutay, M. Emin

doi:10.1080/10298436.2013.808341

Cited by 43 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The low-temperature ultimate flexural-tensile strain of small beams is affected by many complex factors. Among them, the asphalt property and mineral gradation are two key determinants of the magnitude of the strain [12]. Hence, these two factors were selected as the main research targets to ensure the computing accuracy and efficiency of the BPNN-based prediction model.…”

Section: Input and Output Indicesmentioning

confidence: 99%

A Backpropagation Neural Network-based Flexural-tensile Strength Prediction Model for Asphalt Mixture in Cold Regions under Cyclic Thermal Stress

Qin¹,

Zheng²

2019

MMEP

View full text Add to dashboard Cite

This paper attempts to disclose how the varying cold season temperature affects the performance of asphalt mixture i=n cold regions, and create a model to predict the flexuraltensile strength under cyclic thermal stress. For this purpose, the author investigated the influencing factors of asphalt mixture performance in cold regions, such as temperature level and variation in temperature difference, and employed the backpropagation neural network (BPNN) to learn, train and verify 120 samples of SBS AC-13 database. On this basis, a BPNN-based prediction model was established for the flexural-tensile strength of asphalt mixture under cyclic thermal stress. Next, the predicted results were compared with the actual results through regression analysis. The comparison shows that our model output a correlation coefficient (R) of 0.9706, an evidence for good prediction accuracy. This means our model can effectively predict the flexural-tensile strengths of asphalt mixture under cyclic thermal stress in cold regions. The research findings provide a good reference for similar studies on asphalt mixture.

show abstract

Section: Input and Output Indicesmentioning

confidence: 99%

A Backpropagation Neural Network-based Flexural-tensile Strength Prediction Model for Asphalt Mixture in Cold Regions under Cyclic Thermal Stress

Qin¹,

Zheng²

2019

MMEP

View full text Add to dashboard Cite

show abstract

“…Sigmoid and linear functions were used as activation functions for the hidden and output nodes, respectively. Further details regarding the ANN structure are explained elsewhere ( 24, 25 ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Soft Computing Tools to Predict Progression of Percent Embedment of Aggregates in Chip Seals

Seitllari

Kutay

2018

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

In this study, soft computing and multilinear regression techniques were employed to develop models for prediction of progression of chip seal percent embedment depth ( Pe). The model uses inputs such as cumulative equivalent traffic volume, Vialit test results, dust content of aggregates, and initial embedment depth. Multilinear regression, adaptive neuro-fuzzy system, and artificial neural network techniques were used to estimate the Pe. The contribution of the variables affecting Pe was evaluated through a sensitivity analysis. The results indicate that while most of the proposed models were able to predict the Pe reasonably, the artificial neural network model performed the best.

show abstract

“…In 2014, Ozturk and Emin (2014) presented an ANN model for predicting HMA volumetric at Superpave gyrations levels. They concluded that the application of the ANN model superpave mix design can take approximately 1.5 to 4.5 days.…”

Section: Application Of Artificial Neural Network In the Predicting Process Of The Asphalt MIX Propertiesmentioning

confidence: 99%

Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

Androjić

Marović

2017

Can. J. Civ. Eng.

View full text Add to dashboard Cite

The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.

show abstract

An artificial neural network model for virtual Superpave asphalt mixture design

Cited by 43 publications

References 14 publications

A Backpropagation Neural Network-based Flexural-tensile Strength Prediction Model for Asphalt Mixture in Cold Regions under Cyclic Thermal Stress

A Backpropagation Neural Network-based Flexural-tensile Strength Prediction Model for Asphalt Mixture in Cold Regions under Cyclic Thermal Stress

Soft Computing Tools to Predict Progression of Percent Embedment of Aggregates in Chip Seals

Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

Contact Info

Product

Resources

About