Study on Quantitative Characterization of Morphological Characteristics and High Temperature Performance Evaluation of Coarse Aggregate Based on Computer Vision
The morphological characteristics of aggregate include outline shape, angularity, and surface texture, which determine the mutual extrusion and friction between aggregates, and significantly affect the performance of asphalt pavement. At present, the research on the morphological characteristics of...
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| Format: | Article |
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Frontiers Media S.A.
2021-02-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2020.607105/full |
| _version_ | 1818732579675701248 |
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| author | Zhanliang Liu Zhanliang Liu Chen Zhang Chen Zhang Linlong Shao Jiangfeng Wang |
| author_facet | Zhanliang Liu Zhanliang Liu Chen Zhang Chen Zhang Linlong Shao Jiangfeng Wang |
| author_sort | Zhanliang Liu |
| collection | DOAJ |
| description | The morphological characteristics of aggregate include outline shape, angularity, and surface texture, which determine the mutual extrusion and friction between aggregates, and significantly affect the performance of asphalt pavement. At present, the research on the morphological characteristics of coarse aggregate is mainly focused on indoor visual identification technology (AIMS, XCT, etc.), in which the applicability of the proposed aggregate shape characterization index is weak, and these instruments could not serve the practical engineering well. In this article, the Coarse Aggregate Morphological Identification System (CAMIS) is developed based on computer vision technology, and the system can recognize the shape features of aggregates above 2.36 mm particle size and carry out uninterrupted feeding and removal based on the mechanical arm system, which can realize large sample detection. Based on CAMIS aggregate identification system and laboratory tests (rutting test, dynamic modulus test, and penetration shear test), the shape identification and performance test of aggregate samples from construction site are carried out, and an aggregate performance evaluation index, CEI, suitable for high-temperature areas is proposed in combination with the improved response surface method. The processing parameters of vertical shaft impact aggregate crusher are optimized based on the CEI index, and the recommended processing parameters are verified by laboratory tests. The results show that the morphological characteristics of coarse aggregate affect the high temperature performance in order angularity, needle flake, axial coefficient, and convexity. The combination of processing parameters of vertical axis impact crusher is recommended to be of 45 m/s rotational speed, 3 t/h feed quantity, and 30% air intake. Verified by laboratory tests, the aggregate identification system CAMIS developed in this article and the proposed aggregate performance evaluation index, CEI, are highly reliable. |
| first_indexed | 2024-12-17T23:35:49Z |
| format | Article |
| id | doaj.art-619a15c982ea40b3b5dc6a9c9cf1f1f4 |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-12-17T23:35:49Z |
| publishDate | 2021-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-619a15c982ea40b3b5dc6a9c9cf1f1f42022-12-21T21:28:33ZengFrontiers Media S.A.Frontiers in Materials2296-80162021-02-01710.3389/fmats.2020.607105607105Study on Quantitative Characterization of Morphological Characteristics and High Temperature Performance Evaluation of Coarse Aggregate Based on Computer VisionZhanliang Liu0Zhanliang Liu1Chen Zhang2Chen Zhang3Linlong Shao4Jiangfeng Wang5Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an, ChinaDepartment of Railway Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang, ChinaKey Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an, ChinaSchool of Energy and Architecture, Xi’an Aeronautical University, Xi’an, ChinaKey Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an, ChinaKey Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an, ChinaThe morphological characteristics of aggregate include outline shape, angularity, and surface texture, which determine the mutual extrusion and friction between aggregates, and significantly affect the performance of asphalt pavement. At present, the research on the morphological characteristics of coarse aggregate is mainly focused on indoor visual identification technology (AIMS, XCT, etc.), in which the applicability of the proposed aggregate shape characterization index is weak, and these instruments could not serve the practical engineering well. In this article, the Coarse Aggregate Morphological Identification System (CAMIS) is developed based on computer vision technology, and the system can recognize the shape features of aggregates above 2.36 mm particle size and carry out uninterrupted feeding and removal based on the mechanical arm system, which can realize large sample detection. Based on CAMIS aggregate identification system and laboratory tests (rutting test, dynamic modulus test, and penetration shear test), the shape identification and performance test of aggregate samples from construction site are carried out, and an aggregate performance evaluation index, CEI, suitable for high-temperature areas is proposed in combination with the improved response surface method. The processing parameters of vertical shaft impact aggregate crusher are optimized based on the CEI index, and the recommended processing parameters are verified by laboratory tests. The results show that the morphological characteristics of coarse aggregate affect the high temperature performance in order angularity, needle flake, axial coefficient, and convexity. The combination of processing parameters of vertical axis impact crusher is recommended to be of 45 m/s rotational speed, 3 t/h feed quantity, and 30% air intake. Verified by laboratory tests, the aggregate identification system CAMIS developed in this article and the proposed aggregate performance evaluation index, CEI, are highly reliable.https://www.frontiersin.org/articles/10.3389/fmats.2020.607105/fullasphalt pavementcoarse aggregatecomputer visiontechnologymesomorphologyquantitative characterization |
| spellingShingle | Zhanliang Liu Zhanliang Liu Chen Zhang Chen Zhang Linlong Shao Jiangfeng Wang Study on Quantitative Characterization of Morphological Characteristics and High Temperature Performance Evaluation of Coarse Aggregate Based on Computer Vision Frontiers in Materials asphalt pavement coarse aggregate computer vision technology mesomorphology quantitative characterization |
| title | Study on Quantitative Characterization of Morphological Characteristics and High Temperature Performance Evaluation of Coarse Aggregate Based on Computer Vision |
| title_full | Study on Quantitative Characterization of Morphological Characteristics and High Temperature Performance Evaluation of Coarse Aggregate Based on Computer Vision |
| title_fullStr | Study on Quantitative Characterization of Morphological Characteristics and High Temperature Performance Evaluation of Coarse Aggregate Based on Computer Vision |
| title_full_unstemmed | Study on Quantitative Characterization of Morphological Characteristics and High Temperature Performance Evaluation of Coarse Aggregate Based on Computer Vision |
| title_short | Study on Quantitative Characterization of Morphological Characteristics and High Temperature Performance Evaluation of Coarse Aggregate Based on Computer Vision |
| title_sort | study on quantitative characterization of morphological characteristics and high temperature performance evaluation of coarse aggregate based on computer vision |
| topic | asphalt pavement coarse aggregate computer vision technology mesomorphology quantitative characterization |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2020.607105/full |
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