Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering
This study explores the application of K-means clustering to optimize the selection of sampling locations for suspended silt loading (sL) on asphalt pavements, addressing the limitations of traditional random sampling methods in the EPA method. The objective was to identify reliable sampling points...
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MDPI AG
2025-04-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/5/530 |
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| author | Ho-jun Yoo In-tai Kim |
| author_facet | Ho-jun Yoo In-tai Kim |
| author_sort | Ho-jun Yoo |
| collection | DOAJ |
| description | This study explores the application of K-means clustering to optimize the selection of sampling locations for suspended silt loading (sL) on asphalt pavements, addressing the limitations of traditional random sampling methods in the EPA method. The objective was to identify reliable sampling points for road dust concentration measurement, with a focus on improving the accuracy of data collection using the vacuum sweep method. The elbow method was used to determine the optimal number of clusters, revealing that three clusters were ideal for 25 m intervals and five for 100 m intervals. The clustering analysis identified specific sampling locations within the 25 m and 100 m road sections, such as 1.5–4.5 m and 12–18 m, and 15–18 m, 39–42 m, 57 m, 69 m, and 87 m, respectively, which adequately captured sL characteristics. The silhouette score of 0.6247 confirmed the effectiveness of the clustering method in distinguishing distinct groups with similar sL characteristics. The comparison of clustered versus non-clustered sections across 15 pavement segments showed an error rate of approximately 6%. Properly selecting sampling points ensures more accurate dust concentration data, which is crucial for effective road maintenance and environmental management. The findings highlight that optimizing the sampling process can significantly enhance the precision of dust monitoring, especially in areas with varying sL characteristics. |
| format | Article |
| id | doaj-art-41cd917baa5745fba9e0946de8fa6a56 |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-41cd917baa5745fba9e0946de8fa6a562025-08-20T03:14:45ZengMDPI AGAtmosphere2073-44332025-04-0116553010.3390/atmos16050530Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means ClusteringHo-jun Yoo0In-tai Kim1Research Institute, RoadKorea Inc., Gyeonggido 18471, Republic of KoreaDepartment of Transportation Engineering, Myongji University, Gyeonggido 17058, Republic of KoreaThis study explores the application of K-means clustering to optimize the selection of sampling locations for suspended silt loading (sL) on asphalt pavements, addressing the limitations of traditional random sampling methods in the EPA method. The objective was to identify reliable sampling points for road dust concentration measurement, with a focus on improving the accuracy of data collection using the vacuum sweep method. The elbow method was used to determine the optimal number of clusters, revealing that three clusters were ideal for 25 m intervals and five for 100 m intervals. The clustering analysis identified specific sampling locations within the 25 m and 100 m road sections, such as 1.5–4.5 m and 12–18 m, and 15–18 m, 39–42 m, 57 m, 69 m, and 87 m, respectively, which adequately captured sL characteristics. The silhouette score of 0.6247 confirmed the effectiveness of the clustering method in distinguishing distinct groups with similar sL characteristics. The comparison of clustered versus non-clustered sections across 15 pavement segments showed an error rate of approximately 6%. Properly selecting sampling points ensures more accurate dust concentration data, which is crucial for effective road maintenance and environmental management. The findings highlight that optimizing the sampling process can significantly enhance the precision of dust monitoring, especially in areas with varying sL characteristics.https://www.mdpi.com/2073-4433/16/5/530K-means clusteringsilt loadingsampling optimizationroad suspended dustvacuum sweep method |
| spellingShingle | Ho-jun Yoo In-tai Kim Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering Atmosphere K-means clustering silt loading sampling optimization road suspended dust vacuum sweep method |
| title | Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering |
| title_full | Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering |
| title_fullStr | Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering |
| title_full_unstemmed | Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering |
| title_short | Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering |
| title_sort | analytical approach to enhancing efficiency of silt loading collection in epa vacuum sweep method using k means clustering |
| topic | K-means clustering silt loading sampling optimization road suspended dust vacuum sweep method |
| url | https://www.mdpi.com/2073-4433/16/5/530 |
| work_keys_str_mv | AT hojunyoo analyticalapproachtoenhancingefficiencyofsiltloadingcollectioninepavacuumsweepmethodusingkmeansclustering AT intaikim analyticalapproachtoenhancingefficiencyofsiltloadingcollectioninepavacuumsweepmethodusingkmeansclustering |