Polyurethane mixture is a new type of cold mixing and cold paving green road pavement material formed by polyurethane instead of asphalt as binder independently developed by the research group.This material has the advantages of energy saving,emission reduction and excellent performance.However,the strength formation of the material is closely related to the curing reaction of polyurethane,it is difficult to ensure the compaction effect and road performance of the material when compacted too early or too late.Therefore,this paper aims to determine the judgment index and standard of compaction timing of polyurethane mixture through experimental research.According to the above research,the prediction model of compaction timing is established.Based on the micro analysis of curing reaction,the compaction timing prediction model is optimized and its fitting accuracy is improved.Firstly,in order to explore the influence of gradation type and void ratio on compaction timing of polyurethane mixture.According to the gradation Curve of AC-13 and PA-13,the mix proportion of polyurethane concrete-13(PC-13)and porous polyurethane mixture(PPM-13)is designed.Secondly,the representative test conditions(25℃,50% relative humidity and 4% catalyst dosage)are selected to study the method of determining the compaction timing of polyurethane mixture.According to the test results,the fitting equations of void ratio,splitting tensile strength,high temperature,low temperature and water stability of polyurethane mixture with forming time after curing are established,And the compaction timing of two kinds of polyurethane mixture is determined on the premise that various properties meet the technical requirements.On this basis,combined with correlation analysis,the judgment index and standard of compaction timing of polyurethane mixture are determined,that is,the earliest compaction timing is determined by void ratio,and the latest compaction timing is determined by splitting tensile strength.In addition,under the condition of comprehensively considering different road performance,the optimum compaction timing of polyurethane mixture is determined by entropy method.Thirdly,combined with the characteristics of polyurethane materials and the climate conditions in China.The orthogonal design is carried out with temperature,relative humidity and catalyst dosage as influencing factors.According to the results of orthogonal test,the regression equations of void ratio and splitting tensile strength of PC-13 and PPM-13 with forming time under different conditions are established,and combined with the proposed compaction time determination method,the allowable compaction timing of polyurethane mixture under different conditions are determined.On this basis,the primary and secondary relationship of the influence of each factor on compaction timing is determined by the analysis of range and variance.According to the test and analysis results and based on multiple nonlinear regression analysis,the earliest and latest compaction timing prediction models are preliminarily established.The coefficients of determination by the earliest and latest compaction timing prediction models of PC-13 are 0.92 and 0.95.The coefficients of determination by the earliest and latest compaction timing prediction models of PPM-13 are 0.86 and 0.88.Finally,in order to analyze the curing reaction of polyurethane mixture from the micro point of view.Taking the polyurethane mortar system formed by the mixture of polyurethane adhesive and limestone powder as the research object,though DSC test,it was found that the early curing reaction of polyurethane is significantly affected by temperature.Based on the Kamal reaction model,the curing kinetics fitting equation of polyurethane mortar system is established,and the coefficient of determination is more than 0.98.This shows that the curing reaction of polyurethane has significant autocatalytic characteristics.In addition,combined with the micro analysis results,the test conditions for the polyurethane mixture to reach the maximum curing rate are determined.Based on this,the invalid test conditions and test results in orthogonal design are eliminated,and the prediction model is optimized. |