Workability of rubberized asphalt from a perspective of particle effect

Yu, Huayang; Deng, Guansen; Zhang, Zeyu; Zhu, M.; Gong, Minghui; Oeser, Markus

doi:10.1016/j.trd.2021.102712

Cited by 62 publications

(29 citation statements)

References 36 publications

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“…The skid resistance force has been considered a critical indicator of the concrete pavement performance by the Portland Cement Association (PCA) and American Association of State Highway and Transportation Officials (AASHTO) [ 28 , 29 , 30 , 31 , 32 ]. Related studies have proven that the skid-resistance is influenced by many factors, including the pavement surface texture, tire type, tire–pavement friction, or other factors such as the speed, water film depth, and temperature, among which the tire–pavement friction is a primary factor [ 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Texturing Parameters of Concrete Pavement by Balancing Skid-Resistance Performance and Driving Stability

Zhang

Long³

et al. 2021

Materials

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Curved texturing is an effective technique to improve the skid-resistance performance of concrete pavements, which relies on the suitable combination of the groove parameters. This study aims to optimize these parameters with the consideration of skid-resistance performance and driving stability. A pressure film was adopted to obtain the contact stress distribution at the tire–pavement interface. The evaluated indicator of the stress concentration coefficient was established, and the calculation method for the stationary steering resistance torque was optimized based on actual tire–pavement contact characteristics. Test samples with various groove parameters were prepared use self-design molds to evaluate the influence degree of each groove parameter at different levels on the skid-resistance performance through orthogonal and abrasion resistance tests. The results showed that the groove depth and groove spacing had the most significant influence on the stress concentration coefficient and stationary steering resistance torque, respectively, with the groove depth having the most significant influence on the texture depth. Moreover, the driving stability and durability of the skid-resistance performance could be balanced by optimizing the width of the groove group. After analyzing and comprehensively comparing the influences of various parameters, it was found the parameter combination with width, depth, spacing, and the groove group width, respectively, in 8 mm, 3 mm, 15 mm, and 50 mm can balance the skid-resistance performance and driving stability. The actual engineering results showed that the R2 of the fitting between the stress concentration coefficient and SFC (measured at 60 km/h) was 0.871, which proved the effectiveness of the evaluation index proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

Optimizing the Texturing Parameters of Concrete Pavement by Balancing Skid-Resistance Performance and Driving Stability

Zhang

Long³

et al. 2021

Materials

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“…Tailings have the characteristics of fine particles, small weight, large surface area, being easy to run away in contact with water, and being easy to fly when exposed to wind. erefore, those solid mining wastes are a potential hazard to the air, water bodies, farmland, and villages [4,13,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Liu

Zhang

et al. 2021

Advances in Civil Engineering

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To estimate the compressive strength of cement-based materials with mining waste, the dataset based on a series of experimental studies was constructed. The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. The beetle antennae search (BAS) algorithm was employed to tune the hyperparameters of the developed machine learning models. The predictive performances of the three models were compared by the evaluation of the values of correlation coefficient (R) and root mean square error (RMSE). The results showed that the BAS algorithm can effectively tune these artificial intelligence models. The SVM model can obtain the minimum RMSE, while the BAS algorithm is inefficient in DT and RF models. The SVM, DT, and RF models can be used to predict the compressive strength of cement-based materials using solid mining waste as aggregate effectively and accurately, with high R values and lower RMSE values. The RF algorithm can obtain the highest value of R and the lowest value of RMSE, demonstrating the highest accuracy. The solid mining waste to cement ratio is the most important variable to affect the compressive strength. Curing time was also an important parameter in the compressive strength of cemented materials, followed by the water-solid ratio of mining waste and fine sand ratio.

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“…Large-scale rock mass-engineering disasters are mainly caused by the expansion, evolution, and penetration of existing joints [4,12,21]. The instability of the engineering rock mass is closely related to the development degree, development location, occurrence, combination characteristics, and engineering properties of the joint fissures [7,[22][23][24][25][26][27][28]. Regardless of the hardness of the rock, as long as there is a weak geological interface formed by unfavorable joint cracks in the rock mass, the rock mass may deform and fail along these joint cracks [3,17].…”

Section: Introductionmentioning

confidence: 99%

Intelligently Predict the Rock Joint Shear Strength Using the Support Vector Regression and Firefly Algorithm

Huang¹,

Gao²,

Liu³

2021

Lithosphere

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To propose an effective and reasonable excavation plan for rock joints to control the overall stability of the surrounding rock mass and predict and prevent engineering disasters, this study is aimed at predicting the rock joint shear strength using the combined algorithm by the support vector regression (SVR) and firefly algorithm (FA). The dataset of rock joint shear strength collected was employed as the output of the prediction, using the joint roughness coefficient (JRC), uniaxial compressive strength (σc), normal stress (σn), and basic friction angle (φb) as the input for the machine learning. Based on the database of rock joint shear strength, the training subset and test subset for machine learning processes are developed to realize the prediction and evaluation processes. The results showed that the FA algorithm can adjust the hyperparameters effectively and accurately, obtaining the optimized SVR model to complete the prediction of rock joint shear strength. For the testing results, the developed model was able to obtain values of 0.9825 and 0.2334 for the coefficient of determination and root-mean-square error, showing the good applicability of the SVR-FA model to establish the nonlinear relationship between the input variables and the rock joint shear strength. Results of the importance scores showed that σn is the most important factor that affects the rock joint shear strength while σc has the least significant effect. As a factor influencing the shear stiffness from the perspective of physical appearance, the change of the JRC value has a significant impact on the rock joint shear strength.

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Workability of rubberized asphalt from a perspective of particle effect

Cited by 62 publications

References 36 publications

Optimizing the Texturing Parameters of Concrete Pavement by Balancing Skid-Resistance Performance and Driving Stability

Optimizing the Texturing Parameters of Concrete Pavement by Balancing Skid-Resistance Performance and Driving Stability

Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Intelligently Predict the Rock Joint Shear Strength Using the Support Vector Regression and Firefly Algorithm

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