Prediction of Low‐Temperature Rheological Properties of SBS Modified Asphalt

Chen, Qian; Wang, Chaohui; Song, Liang

doi:10.1155/2020/8864766

Cited by 7 publications

(7 citation statements)

References 16 publications

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“…With the rapid development of computer science in recent years, neural networks have been widely used in the field of road engineering. A large number of domestic and foreign scholars use the BP neural network, RBF neural network, and general regression neural network for prediction Advances in Civil Engineering [25][26][27][28][29]. To solve the aforementioned problems in the multiple regression model, a neural network is used to establish a prediction model to further improve the accuracy of the prediction.…”

Section: Neural Network Prediction Modelmentioning

confidence: 99%

Dynamic Modulus Prediction of a High‐Modulus Asphalt Mixture

Wang

Tan

Chen

et al. 2021

Advances in Civil Engineering

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Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high-modulus asphalt mixture. Five high-temperature performance indexes of high-modulus asphalt and its mixture were selected. The correlation between the above five indexes and the dynamic modulus of the high-modulus asphalt mixture was analyzed. On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. According to parameter adjustment and cross-validation, the output stability and accuracy of different prediction models were compared and evaluated. The most effective prediction model was recommended. The results show that the SVM model has more significant prediction accuracy and output stability than the multiple regression model and the GRNN model. Its prediction error was 0.98–9.71%. Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. The SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture.

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Section: Neural Network Prediction Modelmentioning

confidence: 99%

Dynamic Modulus Prediction of a High‐Modulus Asphalt Mixture

Wang

Tan

Chen

et al. 2021

Advances in Civil Engineering

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“…In other study, the Extreme Learning Machine (ELM) algorithm, optimized by Genetic Algorithm (GA), was employed to rapidly predict the lowtemperature rheological properties of styrenic block copolymer modified asphalt based on the raw material properties. The GA-ELM model outperformed traditional models, reducing errors by 68.97-81.48% [17]. Another research introduced a data-driven Convolutional Neural Network (CNN) model to forecast the phase angle behavior of asphalt concrete mixtures.…”

Section: Introductionmentioning

confidence: 99%

Predicting flexural-creep stiffness in bending beam rheometer (BBR) experiments using advanced super learner machine learning techniques

Roshan,

Abdelrahman

2024

Res. Eng. Struct. Mat.

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BBR test is commonly used to assess the low-temperature performance grade (PG) of asphalt binders, with the flexural-creep stiffness being a critical parameter calculated through this test. However, it has notable limitations that demand attention. The significant amount of asphalt binder needed for test specimens increases costs and resource consumption. Additionally, the complex and time-consuming specimen preparation process hinders testing efficiency and introduces result variability, affecting the accuracy and reliability of PG determinations. In recent years, machine learning (ML) has emerged as a promising substitute for predicting various engineering values. In this study, the primary focus was on harnessing super learner (SL) techniques to predict the creep stiffness of asphalt binders. The SL approach combines multiple ML algorithms to enhance predictive accuracy and reduce individual model biases. Bagging, boosting, and stacking algorithms were employed in the construction of these prediction models. To conduct the investigation, data from 1350 samples sourced from the Long-Term Pavement Performance (LTPP) website were utilized to explore the influence of six crucial variables on the creep stiffness of asphalt binders. The proposed method demonstrated high accuracy, nearing 90% in the coefficient of determination. The Stacking model achieved a low Mean Absolute Percentage Error of 2.86% and robust Prediction Accuracy of 97.14% for randomly selected data points. Furthermore, the sensitivity analysis highlighted the significance of distinct input variables in influencing the creep stiffness of asphalt binders. Notably, the test temperature emerged as the most influential factor affecting creep stiffness, according to the conducted study

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“…Maltenes, which are composed of saturates and aromatics in asphalt binder, can play a significant role in the low-temperature cracking resistance. The addition of sulfur will form stronger molecular bonds with the asphalt molecules to form a three-dimensional lattice structure, thus enhancing the viscosity and storage stability of the modified asphalt [ 6 ]. Rubber processing oil is added due to its rich lightweight component, which can promote the swelling of SBS copolymers to Improve the low temperature crack resistance of SBS modified asphalt [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Rubber processing oil is added due to its rich lightweight component, which can promote the swelling of SBS copolymers to Improve the low temperature crack resistance of SBS modified asphalt [ 5 ]. Besides, SBS copolymers alter the microstructure and composition of the asphalt, leading to improved low-temperature properties [ 5 , 6 , 7 ]. Therefore, it is imperative to study the influence of SBS content, sulfur, and rubber processing oil on the low-temperature properties of SBS-modified asphalt.…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing the Low-Temperature Properties of Styrene-Butadiene-Styrene Modified Asphalt Based on Orthogonal Tests

Chen

Jin²,

et al. 2022

Polymers

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Styrene-butadiene-styrene (SBS) is widely used in asphalt modification to obtain superior high-temperature performance. Nevertheless, studies on the low-temperature properties of SBS-modified asphalt are not satisfactory. Orthogonal tests are valid in analysing the results. In this paper, the main factors (SBS content, sulfur content, and the addition of rubber processing oil) for improving the low-temperature performance of SBS-modified asphalt were analyzed base on the orthogonal tests. Firstly, the frequency sweep test, bending beam rheometer (BBR) test, and force-ductility test were conducted to evaluate the low-temperature properties of SBS-modified asphalt. Investigation of low-temperature parameters obtained through these tests was conducted base on the orthogonal analysis method. The G-R parameter was abandoned in the analysis of the orthogonal method for the result that the increase of SBS content was negative to the low-temperature properties by the Glover-Rowe (G-R) parameter, which were contrary to the results of BBR and force-ductility tests. Moreover, the other parameters (ΔTc and toughness) sorted according to the orthogonal analysis method indicated the effect on low-temperature performance of the SBS-modified asphalt as SBS content > rubber processing oil > sulfur. As shown above that both SBS and rubber processing oil play a critical role in improving the low-temperature properties of SBS-modified asphalt, for SBS could resist the generation and subsequent propagation of cracks while the rubber processing oil could supplement the maltene loss.

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Prediction of Low‐Temperature Rheological Properties of SBS Modified Asphalt

Cited by 7 publications

References 16 publications

Dynamic Modulus Prediction of a High‐Modulus Asphalt Mixture

Dynamic Modulus Prediction of a High‐Modulus Asphalt Mixture

Predicting flexural-creep stiffness in bending beam rheometer (BBR) experiments using advanced super learner machine learning techniques

Factors Influencing the Low-Temperature Properties of Styrene-Butadiene-Styrene Modified Asphalt Based on Orthogonal Tests

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