Cold-recycled Mixture Compaction State Sensing Based on SmartRock
To better reveal the interaction among mixture particles in the compaction process,and realize the purpose of optimizing compaction process and mixture design,the gyratory compaction test of cold-recycled asphalt mixture was carried out.The SmartRock sensing device was used to monitor the dynamic response rule of the force,rotation angle and acceleration of particles in the gyratory compaction process.The correlation between SmartRock dynamic response and relative compaction degree of mixture was studied.The interlocking mechanism between among particles during the compaction process was reveal,and the mixture compaction state was evaluated.The gyratory compaction test of cold-recycled mixture with different coarse aggregate contents was carried out to study the influence of different coarse aggregate contents on the compaction process.The test result indicates that there is a good correlation between the variation rule of relative compaction degree and the dynamic response of SmartRock during the compaction process.The intelligent perception of mixture compaction state during the gyratory compaction process was initially realized.Compared with the mixture with 45％and 65％coarse aggregate contents,the mixture with 55％coarse aggregate content is easier to achieve a better compaction degree,and has a higher stability during the compaction process.In addition,the prediction model of compactness based on improved artificial neural network was established.The relative rotation angle and acceleration in x and y directions,the contact force in z direction measured by SmartRock,and the passage rate of aggregate in each sieve hole were selected as the input variables of model.The Tensorflow2.0 framework was used to construct the network and the cyclic neural network structure.The sequential feature data and continuous feature data were processed respectively,and the data fusion training was carried out through the splicing operation.The loss function and accuracy of network reach the convergence threshold after 600 training iterations.The linear variance of measured value and predicted value of the relative mixture compaction was 0.93,indicating that the model has a good prediction ability for the relative mixture compactness.
intelligent transportcompaction state sensingSmartRockcold-recycled asphalt mixturegyratory compaction
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NSTL
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