Abstract
With the advancement of intelligent compaction technology, real-time quality control has been widely investigated on the subgrade, while it is insufficient on asphalt pavement. This paper aims to estimate the real-time compaction quality of hot mix asphalt (HMA) using an artificial neural network (ANN) classifier. A field experiment of HMA compaction was designed. The vibration patterns of the drum were identified by using the ANN classifier and classified based on the compaction levels. The vibration signals were collected and the degree of compaction was measured in the field experiment. The collected signals were processed and the features of vibration patterns were extracted. The processed signals were tagged with their corresponding compaction level to form the sample dataset to train the ANN models. Four ANN models with different hidden layer setups were considered to investigate the effect of hidden layer structure on performance. To test the performance of the ANN classifier, the predictions made by ANN were compared with the measuring results from a non-nuclear density gauge (NNDG). The testing results show that the ANN classifier has good performance and huge potential for estimating the compaction quality of HMA in real-time.
Highlights
The compaction of hot mix asphalt (HMA) is the last and most critical process during asphalt layer construction, which is critical for the safety and durability of an asphalt pavement
Over-compaction will lead to a low percentage of air voids, which is the main cause of asphalt bleeding in high-temperature weather [3]
The predictions made by artificial neural network (ANN) were compared with the measuring results from a non-nuclear density gauge (NNDG) to test the performance of the ANN classifier
Summary
The compaction of hot mix asphalt (HMA) is the last and most critical process during asphalt layer construction, which is critical for the safety and durability of an asphalt pavement. The most reliable method of measuring pavement density is to evaluate air voids of the extraction gained by field cores at limited locations. Alternative ways to obtain the density of HMA layers in the field include nuclear density gauges and non-nuclear density gauges These methods provide only point-wise measurements of density and cannot reflect the overall quality of the HMA layer in real time during compaction. A novel IC system based on the artificial neural network (ANN), named the IC analyzer (ICA), was proposed It provides another way to estimate the stiffness of subgrade in real-time and forms an extension for estimating the quality of asphalt pavement during construction [16,20,21,22]. The predictions made by ANN were compared with the measuring results from a non-nuclear density gauge (NNDG) to test the performance of the ANN classifier
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