Prediction of pavement BPN value based on RIOHTrack full-scale test track
The pavement skid resistance performance index is one of the important indexes to ensure driving safety.Based on the test data of RIOHTrack full-scale test track,this paper selects 357 sets of test data of four structures as input variables,including STR2,STR4,STR9 and STR16.The three influence factors of temperature at pavement surface,equivalent single axle load times(ESALs)and pavement abrasion frequency as input variables,while the british pendulum number(BPN)was taken as output variable.The visualized and implicit prediction model of BPN value are constructed by using group data as samples.The 300 groups of data is taken as training samples,while the 57 groups of data is taken as the verifying samples.Based on the assumption that temperature has a certain influence range on the skid resistance performance of asphalt pavement,the visualized prediction model of BPN value is constructed.The correlation coefficient(R2)of this model is 0.625,while the average relative error is 10.227％.At the same time,different hidden layer neurons and training functions are adopted to construct BP neural network prediction model of BPN value.The predicted BPN value is basically consistent with the test value while the average relative error is 4.484％.Different prediction models have different application prospects.It improves the effectiveness and accuracy of pavement skid resistance performance index prediction,which provides reference for pavement skid resistance performance detection and analysis.
pavement skid resistance performanceBP neural networkRIOHTrack full-scale test trackprediction model
万方数据
维普
