Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386835834> ?p ?o ?g. }
Showing items 1 to 97 of
97
with 100 items per page.
- W4386835834 endingPage "409" @default.
- W4386835834 startingPage "409" @default.
- W4386835834 abstract "Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface friction metrics such as mean profile depth (MPD) or friction number. These metrics assess chip seal quality by targeting issues such as aggregate loss or excessive bleeding, which may yield low friction numbers or texture depths. However, aggregate loss, particularly due to snowplow operations, does not always result in slippery conditions and may lead to uneven surfaces. The correlation between higher MPD or friction number and superior chip seal quality is not straightforward. This research introduces an innovative machine learning-based approach to enhance chip seal QC. Using a hybrid DBSCAN-Isolation Forest model, anomaly detection was conducted on a dataset comprising 183,794 20 m MPD measurements from actual chip seal projects across six districts in Indiana. This resulted in typical 20 m segment MPD ranges of [0.9 mm, 1.9 mm], [0.6 mm, 2.1 mm], [0.3 mm, 1.3 mm], [1.0 mm, 1.7 mm], [0.6 mm, 1.9 mm], and [1.0 mm, 2.3 mm] for the respective six districts in Indiana. A two-step QC procedure tailored for chip seal evaluation was proposed. The first step calculated outlier percentages across 1-mile segments, with an established limit of 25% outlier segments per wheel track. The second step assessed unqualified rates across projects, setting a threshold of 50% for unqualified 1-mile wheel track segments. Through validation analysis of four chip seal projects, both field inspection and friction measurements closely aligned with the proposed methodology’s results. The methodology presented establishes a foundational QC standard for chip seal projects, enhancing both acceptance efficiency and safety by using a quantitative method and minimizing the extended presence of practitioners on roadways." @default.
- W4386835834 created "2023-09-19" @default.
- W4386835834 creator A5042511041 @default.
- W4386835834 creator A5043296622 @default.
- W4386835834 creator A5057560325 @default.
- W4386835834 creator A5086662856 @default.
- W4386835834 date "2023-09-18" @default.
- W4386835834 modified "2023-09-26" @default.
- W4386835834 title "Enhancing Quality Control of Chip Seal Construction through Machine Learning-Based Analysis of Surface Macrotexture Metrics" @default.
- W4386835834 cites W1836205432 @default.
- W4386835834 cites W1901616594 @default.
- W4386835834 cites W1918074162 @default.
- W4386835834 cites W2010958209 @default.
- W4386835834 cites W2025196156 @default.
- W4386835834 cites W2034539987 @default.
- W4386835834 cites W2058074054 @default.
- W4386835834 cites W2059658038 @default.
- W4386835834 cites W2060712603 @default.
- W4386835834 cites W2152022550 @default.
- W4386835834 cites W2296719434 @default.
- W4386835834 cites W2804554613 @default.
- W4386835834 cites W2807617467 @default.
- W4386835834 cites W2911201033 @default.
- W4386835834 cites W2919115771 @default.
- W4386835834 cites W2967456789 @default.
- W4386835834 cites W3034143135 @default.
- W4386835834 cites W3152879181 @default.
- W4386835834 cites W4214904081 @default.
- W4386835834 cites W4245177100 @default.
- W4386835834 cites W4366421352 @default.
- W4386835834 cites W4378803747 @default.
- W4386835834 doi "https://doi.org/10.3390/lubricants11090409" @default.
- W4386835834 hasPublicationYear "2023" @default.
- W4386835834 type Work @default.
- W4386835834 citedByCount "0" @default.
- W4386835834 crossrefType "journal-article" @default.
- W4386835834 hasAuthorship W4386835834A5042511041 @default.
- W4386835834 hasAuthorship W4386835834A5043296622 @default.
- W4386835834 hasAuthorship W4386835834A5057560325 @default.
- W4386835834 hasAuthorship W4386835834A5086662856 @default.
- W4386835834 hasBestOaLocation W43868358341 @default.
- W4386835834 hasConcept C111472728 @default.
- W4386835834 hasConcept C119599485 @default.
- W4386835834 hasConcept C127413603 @default.
- W4386835834 hasConcept C138885662 @default.
- W4386835834 hasConcept C142362112 @default.
- W4386835834 hasConcept C153349607 @default.
- W4386835834 hasConcept C154945302 @default.
- W4386835834 hasConcept C159985019 @default.
- W4386835834 hasConcept C165005293 @default.
- W4386835834 hasConcept C192562407 @default.
- W4386835834 hasConcept C2776652587 @default.
- W4386835834 hasConcept C2777755289 @default.
- W4386835834 hasConcept C2779530757 @default.
- W4386835834 hasConcept C41008148 @default.
- W4386835834 hasConcept C4679612 @default.
- W4386835834 hasConcept C66938386 @default.
- W4386835834 hasConcept C78519656 @default.
- W4386835834 hasConcept C79337645 @default.
- W4386835834 hasConceptScore W4386835834C111472728 @default.
- W4386835834 hasConceptScore W4386835834C119599485 @default.
- W4386835834 hasConceptScore W4386835834C127413603 @default.
- W4386835834 hasConceptScore W4386835834C138885662 @default.
- W4386835834 hasConceptScore W4386835834C142362112 @default.
- W4386835834 hasConceptScore W4386835834C153349607 @default.
- W4386835834 hasConceptScore W4386835834C154945302 @default.
- W4386835834 hasConceptScore W4386835834C159985019 @default.
- W4386835834 hasConceptScore W4386835834C165005293 @default.
- W4386835834 hasConceptScore W4386835834C192562407 @default.
- W4386835834 hasConceptScore W4386835834C2776652587 @default.
- W4386835834 hasConceptScore W4386835834C2777755289 @default.
- W4386835834 hasConceptScore W4386835834C2779530757 @default.
- W4386835834 hasConceptScore W4386835834C41008148 @default.
- W4386835834 hasConceptScore W4386835834C4679612 @default.
- W4386835834 hasConceptScore W4386835834C66938386 @default.
- W4386835834 hasConceptScore W4386835834C78519656 @default.
- W4386835834 hasConceptScore W4386835834C79337645 @default.
- W4386835834 hasIssue "9" @default.
- W4386835834 hasLocation W43868358341 @default.
- W4386835834 hasOpenAccess W4386835834 @default.
- W4386835834 hasPrimaryLocation W43868358341 @default.
- W4386835834 hasRelatedWork W2021310682 @default.
- W4386835834 hasRelatedWork W2027701351 @default.
- W4386835834 hasRelatedWork W2029512801 @default.
- W4386835834 hasRelatedWork W2047229921 @default.
- W4386835834 hasRelatedWork W2922413363 @default.
- W4386835834 hasRelatedWork W2948985893 @default.
- W4386835834 hasRelatedWork W2953221735 @default.
- W4386835834 hasRelatedWork W3000409872 @default.
- W4386835834 hasRelatedWork W4386627299 @default.
- W4386835834 hasRelatedWork W644719928 @default.
- W4386835834 hasVolume "11" @default.
- W4386835834 isParatext "false" @default.
- W4386835834 isRetracted "false" @default.
- W4386835834 workType "article" @default.