Applying a K-means model to TSD data to find categories for the structural assessment of flexible pavements
Since 2018, the German Federal Highway Research Institute (BASt) has been using a traffic speed deflectometer (TSD) for measurements on network level to assess the structural condition of asphalt pavements. TSD collects a variety of data, such as deflections, temperature and slope values at each mea...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Transportation Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666691X25000417 |
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| author | Mahdi Rahimi Nahoujy |
| author_facet | Mahdi Rahimi Nahoujy |
| author_sort | Mahdi Rahimi Nahoujy |
| collection | DOAJ |
| description | Since 2018, the German Federal Highway Research Institute (BASt) has been using a traffic speed deflectometer (TSD) for measurements on network level to assess the structural condition of asphalt pavements. TSD collects a variety of data, such as deflections, temperature and slope values at each measuring point. But for the evaluation of this data, there is no established methodological framework yet. The common methodological approach for falling weight deflectometer (FWD) data uses threshold values for different categories in order to assess the condition of the pavement. But as the load setup and properties of the TSD are different from FWD, the existing FWD- based thresholds are not directly applicable to TSD-based data.The objective of this study is to develop a new, data-driven approach for the analysis of TSD data in order to find categories for the structural status assessment of flexible pavements. A database with >113,000 data points of TSD measured data is used for K-means clustering slope values and SCI300 values in order to divide the data into different categories relevant for the assessment of the structural condition of pavements.The resulting threshold values of the categories found showed obvious correlations to the results of mechanistic models. The K-means model is thus a good supplement to mechanistic models and may even support their validation. The results support the practical applicability of the TSD as new measuring device for the pavement management system. |
| format | Article |
| id | doaj-art-06244967a43d45eb90016f68affe9c47 |
| institution | DOAJ |
| issn | 2666-691X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Engineering |
| spelling | doaj-art-06244967a43d45eb90016f68affe9c472025-08-20T03:10:31ZengElsevierTransportation Engineering2666-691X2025-06-012010034210.1016/j.treng.2025.100342Applying a K-means model to TSD data to find categories for the structural assessment of flexible pavementsMahdi Rahimi Nahoujy0Corresponding author.; Federal Highway and Transport Research Institute (BASt), Brüderstraße 53, Bergisch Gladbach, 51427, North Rhine-Westphalia, GermanySince 2018, the German Federal Highway Research Institute (BASt) has been using a traffic speed deflectometer (TSD) for measurements on network level to assess the structural condition of asphalt pavements. TSD collects a variety of data, such as deflections, temperature and slope values at each measuring point. But for the evaluation of this data, there is no established methodological framework yet. The common methodological approach for falling weight deflectometer (FWD) data uses threshold values for different categories in order to assess the condition of the pavement. But as the load setup and properties of the TSD are different from FWD, the existing FWD- based thresholds are not directly applicable to TSD-based data.The objective of this study is to develop a new, data-driven approach for the analysis of TSD data in order to find categories for the structural status assessment of flexible pavements. A database with >113,000 data points of TSD measured data is used for K-means clustering slope values and SCI300 values in order to divide the data into different categories relevant for the assessment of the structural condition of pavements.The resulting threshold values of the categories found showed obvious correlations to the results of mechanistic models. The K-means model is thus a good supplement to mechanistic models and may even support their validation. The results support the practical applicability of the TSD as new measuring device for the pavement management system.http://www.sciencedirect.com/science/article/pii/S2666691X25000417Traffic speed deflectometerPavement structural evaluationMachine learningK-means |
| spellingShingle | Mahdi Rahimi Nahoujy Applying a K-means model to TSD data to find categories for the structural assessment of flexible pavements Transportation Engineering Traffic speed deflectometer Pavement structural evaluation Machine learning K-means |
| title | Applying a K-means model to TSD data to find categories for the structural assessment of flexible pavements |
| title_full | Applying a K-means model to TSD data to find categories for the structural assessment of flexible pavements |
| title_fullStr | Applying a K-means model to TSD data to find categories for the structural assessment of flexible pavements |
| title_full_unstemmed | Applying a K-means model to TSD data to find categories for the structural assessment of flexible pavements |
| title_short | Applying a K-means model to TSD data to find categories for the structural assessment of flexible pavements |
| title_sort | applying a k means model to tsd data to find categories for the structural assessment of flexible pavements |
| topic | Traffic speed deflectometer Pavement structural evaluation Machine learning K-means |
| url | http://www.sciencedirect.com/science/article/pii/S2666691X25000417 |
| work_keys_str_mv | AT mahdirahiminahoujy applyingakmeansmodeltotsddatatofindcategoriesforthestructuralassessmentofflexiblepavements |