Real-Time Evaluation of Compaction Quality by Using Artificial Neural Networks
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/6617742 |
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| _version_ | 1832547771451703296 |
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| author | Weidong Cao Shutang Liu Xuechi Gao Fei Ren Peng Liu Qilun Wu |
| author_facet | Weidong Cao Shutang Liu Xuechi Gao Fei Ren Peng Liu Qilun Wu |
| author_sort | Weidong Cao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4092e3a149564f129667c2ed279fa805 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-4092e3a149564f129667c2ed279fa8052025-02-03T06:43:31ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422020-01-01202010.1155/2020/66177426617742Real-Time Evaluation of Compaction Quality by Using Artificial Neural NetworksWeidong Cao0Shutang Liu1Xuechi Gao2Fei Ren3Peng Liu4Qilun Wu5School of Qilu Transportation, Shandong University, Jinan 250061, Shandong Province, ChinaSchool of Qilu Transportation, Shandong University, Jinan 250061, Shandong Province, ChinaShandong Hi-Speed Group Co. Ltd., Jinan 250101, Shandong Province, ChinaSchool of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), No. 3501, Daxue Road, Changqing District, Jinan 250353, Shandong Province, ChinaShandong Hi-Speed Group Co. Ltd., Jinan 250101, Shandong Province, ChinaSchool of Qilu Transportation, Shandong University, Jinan 250061, Shandong Province, ChinaThe 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.http://dx.doi.org/10.1155/2020/6617742 |
| spellingShingle | Weidong Cao Shutang Liu Xuechi Gao Fei Ren Peng Liu Qilun Wu Real-Time Evaluation of Compaction Quality by Using Artificial Neural Networks Advances in Materials Science and Engineering |
| title | Real-Time Evaluation of Compaction Quality by Using Artificial Neural Networks |
| title_full | Real-Time Evaluation of Compaction Quality by Using Artificial Neural Networks |
| title_fullStr | Real-Time Evaluation of Compaction Quality by Using Artificial Neural Networks |
| title_full_unstemmed | Real-Time Evaluation of Compaction Quality by Using Artificial Neural Networks |
| title_short | Real-Time Evaluation of Compaction Quality by Using Artificial Neural Networks |
| title_sort | real time evaluation of compaction quality by using artificial neural networks |
| url | http://dx.doi.org/10.1155/2020/6617742 |
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