Effects of Compaction Temperature on the Volumetric Properties and Compaction Energy Efforts of Polymer-Modified Asphalt Mixtures

In order to determine a proper compaction temperature that affects the workability and compactibility of the polymer-modified asphalt（PMA）, the effect of compaction temperature was examined on the volumetric properties and the compaction energy indices. Change in compaction temperature shows an important influence on the maximum specific gravity of mixture（G_（mm）） by internal volume change of PMA. The change in G_（mm） mainly affects the effective volume of the aggregate（V_（Eff））. Reduction in V_（Eff） from Zero shear viscosity（ZSV） to superpave temperature allows 0.1%-0.15% of the asphalt binder to occupy highly the external voids of aggregates. The volumetric properties for all compaction specimens meet superpave criteria, but the energy efforts were the lowest at ZSV temperature. Lower energy efforts at the ZSV temperature reflect easier compaction than those at excessively high temperature. Clearly, excessive compaction temperature may not be necessary to improve the compactibility and to reduce the compaction efforts.

Statistical Analysis of the Asphalt Mixtures Volumetric Properties

Based on statistics principle,random error and systematic error were considered and the volumetric properties of the two mixtures types,namely A and B,were statistically analyzed using different distribution methods.Seventy-two samples of mixture A and fifty-two of mixture B were fabricated using the Marshall method.The probability distributions were compared on the basis of goodness of fit.Weibull model was found to be most appropriate model for describing the asphalt mixtures volumetric properties distribution.The two-parameter Weibull distribution function applied well to model the bulk specific gravity and voids filled with asphalt data,whereas,the three-parameter Weibull distribution appeared to be more appropriate in the discussing of air voids and voids in mineral aggregate.The experimetal results is revealed that compared with the mean value,the peak value of Weibull distribution was suggested as an alternative and more powerful parameter for describing the test data distribution characteristic.The analysis of test results also revealed that there were significant differences in the volumetric properties of the two tested mixtures for the same confidence level.The confidence interval decreased with the decreasing in reliability.

Volum etric Properties of Sodium Chlorobenzoatein N,N-Dim ethylform am ide/waterMixtures at 298.15K

Densities of sodium chlorobenzoate( o , m , p ) were measured in solutions up to 80 weight percent dimethylformamide(DMF) at 298 15 K with an oscillating tube densimeter. From these densities, apparent molar volumes of sodium chlorobenzoate in DMF H 2O mixtures were calculated, and partial molar volumes at infinite dilution were evaluated. Substituent and solvent effects on transfer volumes of each isomer from water to DMF H 2O mixed solvents were obtained. The results are explained in terms of solvent structure and solute solvent interaction.

Automated,economical,and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO_(2)emissions during pavement construction and maintenance.Additionally,the laboratory mix design process,which involves selecting aggregate gradation and binder content,is time-consuming and labor-intensive.To accelerate the traditional mix design procedure,this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning(ML)and a meta-heuristic algorithm.Specifically,ML approaches were employed to model the relationship between volumetric properties(mixture bulk specific gravity(Gmb)and air void(VV))and both mixture component properties and mixture proportion,based on a dataset collected from literature with 660 mixture designs.Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization(MOGWO)algorithm,an automatic asphalt mix design was proposed to pursue three goals,including VV,cost,and CO_(2)emission.The results indicated that least squares support vector regression(LSSVR)and e Xtreme gradient boosting(XGBoost)achieved the highest prediction accuracies(correlation coefficient:0.92 for VV and 0.96 for Gmb).The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs.cost vs.CO_(2)emission.Compared to the traditional laboratory design,the optimal mixture with VV of4%achieves a cost saving of 2.46%and a reduction of 4.03%in carbon emission.The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.