Kernel machines and firefly algorithm based dynamic modulus prediction model for asphalt mixes considering aggregate morphology

2018 ◽  
Vol 159 ◽  
pp. 408-416 ◽  
Author(s):  
Dharamveer Singh ◽  
Saurabh Maheshwari ◽  
Musharraf Zaman ◽  
Sesh Commuri
Keyword(s):  
Prediction Model ◽  
Dynamic Modulus ◽  
Firefly Algorithm ◽  
Kernel Machines ◽  
Asphalt Mixes ◽  
Aggregate Morphology

Laboratory Investigation of Pavement Backfilling Materials for Micro-Trenching in Cold Regions

2018 ◽  
Vol 2672 (12) ◽  
pp. 62-71
Author(s):  
Leila Hashemian ◽  
Vinicius Afonso Velasco Rios ◽  
Alireza Bayat
Keyword(s):  
Laboratory Investigation ◽  
Dynamic Modulus ◽  
Laboratory Tests ◽  
Hot Mix Asphalt ◽  
Cement Grout ◽  
Host Material ◽  
High Quality ◽  
Cold Temperatures ◽  
Asphalt Mixes ◽  
Freeze Thaw

This study investigated the performance of different materials in a micro-trench composite backfilling design. Laboratory tests were conducted to evaluate the effect of cold temperatures and freeze/thaw cycles on a cement grout and seven preparatory cold asphalt mixes. To compare the performance of cold mix asphalt and epoxy grout with hot mix asphalt as the host material, rutting tests and dynamic modulus tests at different loading frequencies and temperatures were conducted. Finally, laboratory scale micro-trench samples were prepared using different backfilling materials and were loaded using a wheel tracker after freeze/thaw conditioning. The results showed that cement grout could effectively be used to secure the conduit inside the trench. It was also concluded that using high-quality cold mix asphalt, a compatible material with hot mix asphalt, could improve micro-trench durability compared with epoxy grout.

scholarly journals Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chaohui Wang ◽  
Songyuan Tan ◽  
Qian Chen ◽  
Jiguo Han ◽  
Liang Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Multiple Regression ◽  
Dynamic Modulus ◽  
Prediction Error ◽  
Prediction Models ◽  
Asphalt Mixture ◽  
High Modulus ◽  
Output Stability ◽  
Svm Model

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.

Effect of Coarse Aggregate Morphology on the Resilient Modulus of Hot-Mix Asphalt

2005 ◽  
Vol 1929 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Tongyan Pan ◽  
Erol Tutumluer ◽  
Samuel H. Carpenter
Keyword(s):  
Surface Texture ◽  
Coarse Aggregate ◽  
Resilient Modulus ◽  
Asphalt Binder ◽  
Aggregate Size ◽  
Asphalt Mixtures ◽  
Maximum Aggregate Size ◽  
Asphalt Mixes ◽  
Aggregate Morphology ◽  
The Relationship

The resilient modulus measured in the indirect tensile mode according to ASTM D 4123 reflects effectively the elastic properties of asphalt mixtures under repeated load. The coarse aggregate morphology quantified by angularity and surface texture properties affects resilient modulus of asphalt mixes; however, the relationship is not yet well understood because of the lack of quantitative measurement of coarse aggregate morphology. This paper presents findings of a laboratory study aimed at investigating the effects of the material properties of the major component on the resilient modulus of asphalt mixes, with the coarse aggregate morphology considered as the principal factor. With modulus tests performed at a temperature of 25°C, using coarse aggregates with more irregular morphologies substantially improved the resilient modulus of asphalt mixtures. An imaging-based angularity index was found to be more closely related to the resilient modulus than an imaging-based surface texture index, as indicated by a higher value of the correlation coefficient. The stiffness of the asphalt binder also had a strong influence on modulus. When the resilient modulus data were grouped on the basis of binder stiffnesses, the agreement between the coarse aggregate morphology and the resilient modulus was significantly improved in each group. Although the changes in aggregate gradation did not significantly affect the relationship between the coarse aggregate morphology and the resilient modulus, decreasing the nominal maximum aggregate size from 19 mm to 9.5 mm indicated an increasing positive influence of aggregate morphology on the resilient modulus of asphalt mixes.

scholarly journals Advanced approaches to hot-mix asphalt dynamic modulus prediction

2008 ◽  
Vol 35 (7) ◽  
pp. 699-707 ◽  
Author(s):  
Halil Ceylan ◽  
Kasthurirangan Gopalakrishnan ◽  
Sunghwan Kim
Keyword(s):  
Prediction Model ◽  
Dynamic Modulus ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Logical Structure ◽  
Ann Model ◽  
Model Predictions ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
National Cooperative

The dynamic modulus (|E*|) is one of the primary hot-mix asphalt (HMA) material property inputs at all three hierarchical levels in the new Mechanistic–empirical pavement design guide (MEPDG). The existing |E*| prediction models were developed mainly from regression analysis of an |E*| database obtained from laboratory testing over many years and, in general, lack the necessary accuracy for making reliable predictions. This paper describes the development of a simplified HMA |E*| prediction model employing artificial neural network (ANN) methodology. The intelligent |E*| prediction models were developed using the latest comprehensive |E*| database that is available to researchers (from National Cooperative Highway Research Program Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the Hirsch |E*| prediction model, which has a logical structure and a relatively simple prediction model in terms of the number of input parameters needed with respect to the existing |E*| models. The ANN-based |E*| predictions showed significantly higher accuracy compared with the Hirsch model predictions. The sensitivity of input variables to the ANN model predictions were also examined and discussed.

Inclusion of aggregate angularity, texture, and form in estimating dynamic modulus of asphalt mixes

2012 ◽  
Vol 13 (2) ◽  
pp. 327-344 ◽  
Author(s):  
Dharamveer Singh ◽  
Musharraf Zaman ◽  
Sesh Commuri
Keyword(s):  
Dynamic Modulus ◽  
Asphalt Mixes

scholarly journals Measurement of Dynamic Modulus and Master Curve Modeling of Hot Mix Asphalt from Senegal (West Africa)

2015 ◽  
Vol 2 (1) ◽  
pp. 124 ◽  
Author(s):  
Mouhamed Lamine Chérif Aidara ◽  
Makhaly Ba ◽  
Alan Carter
Keyword(s):  
Dynamic Modulus ◽  
Master Curve ◽  
Hot Mix Asphalt ◽  
Complex Modulus ◽  
Test Results ◽  
Asphalt Mixes ◽  
Design Guide ◽  
Asphalt Mix ◽  
Sigmoidal Model ◽  
Curve Modeling

The main purpose of this paper is to model the master curve of dynamic modulus |E*| for Hot Mix Asphalt mix designed with aggregate from Senegal named basalt of Diack and quartzite of Bakel. The prediction model used is the Witczak model, used in the Mechanistic-Empirical Pavement Design Guide. A study has been conducted in the Laboratory of Pavements and Bituminous Materials. Six different HMA (BBSG 0/14 mm) were subjected to complex modulus test by tension-compression according to the European or Canadian procedure using the same range of temperatures and frequencies. For each mixture studied the uniqueness of modulus curves in the Cole-Cole or in Black diagrams have shown that the asphalt mixes are thermorheologically simple materials and the Canadian test process is suitable for determining the HMA complex modulus mix designed with the aggregates from Senegal. This implies their tender with the principle of time-temperature equivalence. The test results were used to model the master curves of HMA studied. A correlation with the results of dynamic modulus measured have shown an accuracy of R2 = 0,99 and p = 0,00 in STATISTICA software, which allows to conclude that the sigmoidal model has good modeling of the dynamic modulus.

scholarly journals Study on the Optimum Steel Slag Content of SMA-13 Asphalt Mixes Based on Road Performance

Coatings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1436
Author(s):  
Wei Chen ◽  
Jincheng Wei ◽  
Xizhong Xu ◽  
Xiaomeng Zhang ◽  
Wenyang Han ◽  
...  
Keyword(s):  
Dynamic Modulus ◽  
Steel Slag ◽  
Temperature Stability ◽  
Shear Resistance ◽  
High Temperature Stability ◽  
Asphalt Mixes ◽  
Asphalt Mix ◽  
Wheel Tracking ◽  
Temperature Crack ◽  
Slag Content

To reduce the use of aggregates such as limestone and basalt, this paper used steel slag to replace some of the limestone aggregates in the production of SMA-13 asphalt mixes. The optimum content of steel slag in the SMA-13 asphalt mixes was investigated, and the performance of these mixes was evaluated. Five SMA-13 asphalt mixes with varying steel slag content (0%, 25%, 50%, 75%, and 100%) were designed and prepared experimentally. The high-temperature stability, low-temperature crack resistance, water stability, dynamic modulus, shear resistance, and volumetric stability of the mixes were investigated using the wheel tracking, Hamburg wheel tracking, three-point bending, freeze–thaw splitting, dynamic modulus, uniaxial penetration, and asphalt mix expansion tests. The results showed that compared to normal SMA-13 asphalt mixes, the high-temperature stability, water stability, and shear resistance of the SMA-13 asphalt mixes increased and then decreased as the steel slag content increased. All three performance indicators peaked at 75% steel slag content, and the dynamic stability, freeze–thaw splitting ratio, and uniaxial penetration strength increased by 90.48%, 7.39%, and 88.08%, respectively; however, the maximum bending tensile strain, which represents the low-temperature crack resistance of the asphalt mix, decreased by 5.98%. The dynamic modulus of the SMA-13 asphalt mixes increased with increasing steel slag content, but the volume expansion at a 75% steel slag content was 0.446% higher than at a 0% steel slag content. Based on the experimental results, the optimum content of steel slag for SMA-13 asphalt mixes was determined to be 75%.

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