Asphalt and Aggregate Fluorescence Tracing Based on Sensors and Ambient Parameter Optimization
Fluorescence tracing effectively identifies asphalt stripping on aggregate surfaces, showing promise for characterizing asphalt–aggregate adhesion in pavement performance detection. However, this method’s effectiveness depends on sensor parameters and ambient conditions. This study developed a fluor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/12/1978 |
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| author | Kexi Zong Hongxi Zhu Sinan Wu Donglin Wu Shuo Pang Junhao Zhai Huiying Mao Yixi Ding |
| author_facet | Kexi Zong Hongxi Zhu Sinan Wu Donglin Wu Shuo Pang Junhao Zhai Huiying Mao Yixi Ding |
| author_sort | Kexi Zong |
| collection | DOAJ |
| description | Fluorescence tracing effectively identifies asphalt stripping on aggregate surfaces, showing promise for characterizing asphalt–aggregate adhesion in pavement performance detection. However, this method’s effectiveness depends on sensor parameters and ambient conditions. This study developed a fluorescence tracing image acquisition system and employed a five-factor, six-level orthogonal experiment to optimize sensor parameters. We compared multilayer perceptron (MLP) regression, Kolmogorov–Arnold networks regression, and Laplacian sharpening for image quality assessment, with MLP proving superior. The results indicate that (1) image quality is primarily influenced by camera aperture, followed by focal length, exposure time, UV light–camera distance, and object–camera distance; (2) the optimal parameters were 100,000 ms exposure time, 8 mm focal length, 44 cm object–camera distance, aperture of 6, and 30 cm UV light–camera distance; (3) a green background with combined UV and daylight illumination in a glass box yielded the highest image quality score (0.7084); and (4) images acquired under these optimized conditions displayed fluorescence tracing and asphalt regions with superior clarity. This study optimizes the fluorescence tracing method for quantifying the adhesion between asphalt and aggregate and promotes an intellectual approach to material performance detection in pavement engineering. |
| format | Article |
| id | doaj-art-333ab623e28e439db878fc36fdd3ef4e |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-333ab623e28e439db878fc36fdd3ef4e2025-08-20T03:27:22ZengMDPI AGBuildings2075-53092025-06-011512197810.3390/buildings15121978Asphalt and Aggregate Fluorescence Tracing Based on Sensors and Ambient Parameter OptimizationKexi Zong0Hongxi Zhu1Sinan Wu2Donglin Wu3Shuo Pang4Junhao Zhai5Huiying Mao6Yixi Ding7College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaFluorescence tracing effectively identifies asphalt stripping on aggregate surfaces, showing promise for characterizing asphalt–aggregate adhesion in pavement performance detection. However, this method’s effectiveness depends on sensor parameters and ambient conditions. This study developed a fluorescence tracing image acquisition system and employed a five-factor, six-level orthogonal experiment to optimize sensor parameters. We compared multilayer perceptron (MLP) regression, Kolmogorov–Arnold networks regression, and Laplacian sharpening for image quality assessment, with MLP proving superior. The results indicate that (1) image quality is primarily influenced by camera aperture, followed by focal length, exposure time, UV light–camera distance, and object–camera distance; (2) the optimal parameters were 100,000 ms exposure time, 8 mm focal length, 44 cm object–camera distance, aperture of 6, and 30 cm UV light–camera distance; (3) a green background with combined UV and daylight illumination in a glass box yielded the highest image quality score (0.7084); and (4) images acquired under these optimized conditions displayed fluorescence tracing and asphalt regions with superior clarity. This study optimizes the fluorescence tracing method for quantifying the adhesion between asphalt and aggregate and promotes an intellectual approach to material performance detection in pavement engineering.https://www.mdpi.com/2075-5309/15/12/1978pavementfluorescence tracingoptimizationimage quality assessmentsensor parameters |
| spellingShingle | Kexi Zong Hongxi Zhu Sinan Wu Donglin Wu Shuo Pang Junhao Zhai Huiying Mao Yixi Ding Asphalt and Aggregate Fluorescence Tracing Based on Sensors and Ambient Parameter Optimization Buildings pavement fluorescence tracing optimization image quality assessment sensor parameters |
| title | Asphalt and Aggregate Fluorescence Tracing Based on Sensors and Ambient Parameter Optimization |
| title_full | Asphalt and Aggregate Fluorescence Tracing Based on Sensors and Ambient Parameter Optimization |
| title_fullStr | Asphalt and Aggregate Fluorescence Tracing Based on Sensors and Ambient Parameter Optimization |
| title_full_unstemmed | Asphalt and Aggregate Fluorescence Tracing Based on Sensors and Ambient Parameter Optimization |
| title_short | Asphalt and Aggregate Fluorescence Tracing Based on Sensors and Ambient Parameter Optimization |
| title_sort | asphalt and aggregate fluorescence tracing based on sensors and ambient parameter optimization |
| topic | pavement fluorescence tracing optimization image quality assessment sensor parameters |
| url | https://www.mdpi.com/2075-5309/15/12/1978 |
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