Asphalt 4 initializing save data manual#

Furthermore, it is of great importance to regulate the supply market of the asphalt binder and guarantee the reliability of asphalt pavement. Therefore, an effective method should be proposed for quantifying the content of SBS in modified asphalt. However, due to the high cost of SBS modifiers, adequate content of SBS in modified asphalt is not always guaranteed during the production process. It has been found that the properties of the modified asphalt are related to the amount of SBS, considering that it has a close relation with the microstructure of the SBS-modified asphalt. Additionally, it also enables the improvement of antifatigue and antiaging features of the pavement. As a triblock copolymer, styrene-butadiene-styrene (SBS) is the most popular polymer used for modifying asphalt, which is of great significance for improving the toughness of asphalt pavement against rutting deformation and cracking in high and low temperatures, respectively. However, several factors, such as increased traffic and extreme weather conditions, poses great threat to pavement longevity, which gives rise to pavement distress, such as permanent deformation, fatigue cracking, and thermal cracking. IntroductionДЄsphalt pavement is widely used owing to its remarkable durability. In comparison with the standard curve method and the random forest method, the precision of DNN is greater than 98% for the same test conditions, achieving the best predicting performance. The DNN-based prediction model showed that the correlation coefficient between the target value and the mean predicted value is 0.9978 and 0.9992 for training and testing samples, respectively, indicating its remarkable accuracy and applicability after training. Results show that the mean square error value decreased by 68% for DNN with noise and dimension reduction. Furthermore, comparative studies are conducted to verify the accuracy of the proposed model. After designing DNN, preprocessed feature data are utilized as training and testing data and are fed into the DNN via a feature matrix. Feature data of SBS content are derived from the spectra, which are obtained by the Fourier-transform infrared spectroscopy test.

Asphalt 4 initializing save data manual#

Traditional methods used for evaluating the SBS content are inaccurate and complicated because they are prone to produce errors by manual computation. This study proposes a prediction model for accurately detecting styrene-butadiene-styrene (SBS) content in modified asphalt using the deep neural network (DNN).