Abstract
The primary goal of this study is to find an easy and convenient way to estimate the degree of compaction in real time for compaction quality control. In this paper, an artificial neural network classifier is developed to identify the different characteristic patterns of drum vibration and classify them according to the different compaction levels. At first, a field compaction experiment is designed and performed in a construction site, and the degree of compaction and the vibration are measured. Then, the vibration signals collected from the experiment are processed to extract the features of vibration patterns and labeled with the compaction level to train the artificial neural network model. At last, the performance of the artificial neural network classifier is verified against the degree of compaction measured by using a nuclear density gauge. It can be found that artificial neural networks show good performance and huge potential for the problem of compaction quality control.
Highlights
An artificial intelligence-based intelligent compaction analyzer (ICA) was developed by Barman et al [17,18,19]. e frequency characteristics of drum vibration can be analyzed by the ICA, and amounts of field testing show that the results correlate well with subgrade modulus
Discussion on Roller Moving Direction during Compaction e analysis in this paper mainly focuses on the compaction of cement-stabilized gravel base. e main intention of this study is to find an easy way to estimate the degree of compaction in real time. e artificial neural networks (ANN) classifier developed in this paper partly achieves this goal by using vibration pattern recognition
Conclusions and Outlooks e primary goal of this paper is to find an easy and convenient way to estimate the degree of compaction in real time. e ANN classifier developed in this paper partly achieves this goal
Summary
An artificial intelligence-based intelligent compaction analyzer (ICA) was developed by Barman et al [17,18,19]. e frequency characteristics of drum vibration can be analyzed by the ICA, and amounts of field testing show that the results correlate well with subgrade modulus. E variations in the degree of compaction will affect the response and will lead to different patterns of vibrations of the drum. An artificial neural network (ANN) classifier is developed to identify the different characteristic patterns of drum vibration and classify them according to the different compaction levels. E vibration signals collected from the experiment are processed to extract the features of vibration patterns and labeled with the compaction level to train the ANN model. E variations in degree of compaction affect the coupled response and lead to different vibration patterns of the drum. To analyze and make use of the mapping relationship between the vibration pattern and degree of compaction, a field compaction experiment is designed and performed in a construction site to collect the data of the vibration signal and degree of compaction
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