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A platform for research: civil engineering, architecture and urbanism
Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates
Highlights In this study, an ANN method was developed to model the final strain of the asphalt concrete for varying aggregate types, additive type, additive content, temperature, and stress. Final strain of asphalt mixtures can be significantly improved through incorporating the maximum of 8.5% micro silica and Nano TiO2/SiO2 into mixtures. In samples with both aggregate types, the final strain of asphalt mixtures can be significantly improved through incorporating the maximum of 8.5% micro silica and Nano TiO2/SiO2 into mixtures. Increase in additives percentage over 8.5% does not help the reduction of permanent deformation in asphalt mixtures. As a result, the optimum additive content for both additive types was determined to be in ranges from 7.5% to 8.5%. Increasing the amount of Nano TiO2/SiO2 lead to reduce permanent deformation of asphalt samples.
Abstract Due to the complex behavior of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting the permanent deformation of asphalt pavement is difficult. To predict, it is required to find the mathematical relation between the input and output data by an accurate and simple method. In recent years, artificial neural networks (ANNs) have been used to model the properties and behavior of materials, and to find complex relations between different properties in many fields of civil engineering applications, because of their ability to learn and to adapt. This study discusses the application of ANN in predicting permanent deformation of asphalt concrete mixtures modified by Nano-additives. A total number of 270 asphalt mixtures were constructed from two different aggregate sources (natural and steel slag) and were modified by micro silica and Nano TiO2/SiO2. All samples were tested at three different testing temperatures of 40, 50, and 60°C and five stresses of 100–500kPa. An ANN model developed using five input parameters including: aggregate source, additive type, additive content, temperature, and stress. An ANN with 10 neurons in hidden layer was considered as the appropriate architecture for predicting final strain of asphalt mixtures, and an excellent conformity was observed between the predicted and the test data. The result indicates that the proposed model can be applied in predicting final strain of asphalt mixtures. The model is further applied to evaluate the effect of different percentages of Nano-additive on permanent asphalt deformation. Results show that an increase in percentage of Nano-additives is very effective in reducing the final strain of asphalt mixtures. However, an increase in percentage of additives over 8.5% does not help to reduce permanent deformation under dynamic loading in the asphalt mixtures.
Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates
Highlights In this study, an ANN method was developed to model the final strain of the asphalt concrete for varying aggregate types, additive type, additive content, temperature, and stress. Final strain of asphalt mixtures can be significantly improved through incorporating the maximum of 8.5% micro silica and Nano TiO2/SiO2 into mixtures. In samples with both aggregate types, the final strain of asphalt mixtures can be significantly improved through incorporating the maximum of 8.5% micro silica and Nano TiO2/SiO2 into mixtures. Increase in additives percentage over 8.5% does not help the reduction of permanent deformation in asphalt mixtures. As a result, the optimum additive content for both additive types was determined to be in ranges from 7.5% to 8.5%. Increasing the amount of Nano TiO2/SiO2 lead to reduce permanent deformation of asphalt samples.
Abstract Due to the complex behavior of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting the permanent deformation of asphalt pavement is difficult. To predict, it is required to find the mathematical relation between the input and output data by an accurate and simple method. In recent years, artificial neural networks (ANNs) have been used to model the properties and behavior of materials, and to find complex relations between different properties in many fields of civil engineering applications, because of their ability to learn and to adapt. This study discusses the application of ANN in predicting permanent deformation of asphalt concrete mixtures modified by Nano-additives. A total number of 270 asphalt mixtures were constructed from two different aggregate sources (natural and steel slag) and were modified by micro silica and Nano TiO2/SiO2. All samples were tested at three different testing temperatures of 40, 50, and 60°C and five stresses of 100–500kPa. An ANN model developed using five input parameters including: aggregate source, additive type, additive content, temperature, and stress. An ANN with 10 neurons in hidden layer was considered as the appropriate architecture for predicting final strain of asphalt mixtures, and an excellent conformity was observed between the predicted and the test data. The result indicates that the proposed model can be applied in predicting final strain of asphalt mixtures. The model is further applied to evaluate the effect of different percentages of Nano-additive on permanent asphalt deformation. Results show that an increase in percentage of Nano-additives is very effective in reducing the final strain of asphalt mixtures. However, an increase in percentage of additives over 8.5% does not help to reduce permanent deformation under dynamic loading in the asphalt mixtures.
Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates
Shafabakhsh, G.H. (author) / Ani, O. Jafari (author) / Talebsafa, M. (author)
Construction and Building Materials ; 85 ; 136-143
2015-03-08
8 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2015
| British Library Online Contents | 2015