FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3215757687> ?p ?o ?g. }

• W3215757687 endingPage "116283" @default.
• W3215757687 startingPage "116283" @default.
• W3215757687 abstract "A framework was proposed to find optimal maintenance policies in a road network. The framework included: identifying factors that contribute to policy-making; clustering the network based on these factors; identifying criteria that impact optimal policies; and determining optimal policies and periods using these criteria. To test the framework applicability, it was applied to Iran roads network step-by-step. First, road functional class and climate were identified as factors that contribute to policy-making. Second, the network was clustered into six sub-networks based on two road functional classes and three climates. Third; maintenance cost, network condition, and maintenance period were identified as criteria that impact optimal policies. Fourth, 96 maintenance policies were applied to each sub-network considering two-year, four-year, six-year, and twelve-year maintenance periods. To quantify policies cost, seven machine-learning algorithms including Gradient Boosting Regression, Lasso, Ridge, Random Forest Regression, Elastic Net, Neural Network, and Multiple Linear Regression were tested. Using the coefficient of determination (R2) as the accuracy metric, it was found that in all sub-networks the Gradient Boosting Regression had the highest accuracy on testing set (greater than 90%) while that of other algorithms was between 50% and 90%. Sub-networks condition was modeled using the Markov Chain model and was measured by the average Pavement Condition Index (PCI). Having policies cost and sub-networks PCI, the optimal policy was selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). It was concluded that in all sub-networks, the four-year maintenance period was optimal. Roads in warm zone demanded the most intense policies, followed by those in cold and humid zones. The same applied to arterial roads followed by local ones." @default.
• W3215757687 created "2021-12-06" @default.
• W3215757687 creator A5054390609 @default.
• W3215757687 creator A5085387707 @default.
• W3215757687 date "2022-04-01" @default.
• W3215757687 modified "2023-10-15" @default.
• W3215757687 title "Application of Machine-Learning in Network-Level Road Maintenance Policy-Making: The Case of Iran" @default.
• W3215757687 cites W1908813088 @default.
• W3215757687 cites W1969408324 @default.
• W3215757687 cites W1981456570 @default.
• W3215757687 cites W1983059765 @default.
• W3215757687 cites W2011312118 @default.
• W3215757687 cites W2018788229 @default.
• W3215757687 cites W2021810715 @default.
• W3215757687 cites W2024426272 @default.
• W3215757687 cites W2029184015 @default.
• W3215757687 cites W2046996051 @default.
• W3215757687 cites W2047277421 @default.
• W3215757687 cites W2048688082 @default.
• W3215757687 cites W2073175998 @default.
• W3215757687 cites W2076505664 @default.
• W3215757687 cites W2088794999 @default.
• W3215757687 cites W2098207414 @default.
• W3215757687 cites W2113676209 @default.
• W3215757687 cites W2136589719 @default.
• W3215757687 cites W2315038347 @default.
• W3215757687 cites W2321671980 @default.
• W3215757687 cites W2596033302 @default.
• W3215757687 cites W2731541337 @default.
• W3215757687 cites W2737774739 @default.
• W3215757687 cites W2770110644 @default.
• W3215757687 cites W2791564844 @default.
• W3215757687 cites W2791817377 @default.
• W3215757687 cites W2794658055 @default.
• W3215757687 cites W2794940393 @default.
• W3215757687 cites W2909597659 @default.
• W3215757687 cites W2911873782 @default.
• W3215757687 cites W2912057070 @default.
• W3215757687 cites W2916299521 @default.
• W3215757687 cites W2922673383 @default.
• W3215757687 cites W2944398166 @default.
• W3215757687 cites W2951420198 @default.
• W3215757687 cites W2951956003 @default.
• W3215757687 cites W2953526251 @default.
• W3215757687 cites W2965334037 @default.
• W3215757687 cites W2989576489 @default.
• W3215757687 cites W2996338922 @default.
• W3215757687 cites W3017039712 @default.
• W3215757687 cites W3039432969 @default.
• W3215757687 cites W3112099171 @default.
• W3215757687 cites W3158648516 @default.
• W3215757687 cites W4244035502 @default.
• W3215757687 cites W4251709618 @default.
• W3215757687 doi "https://doi.org/10.1016/j.eswa.2021.116283" @default.
• W3215757687 hasPublicationYear "2022" @default.
• W3215757687 type Work @default.
• W3215757687 sameAs 3215757687 @default.
• W3215757687 citedByCount "4" @default.
• W3215757687 countsByYear W32157576872022 @default.
• W3215757687 countsByYear W32157576872023 @default.
• W3215757687 crossrefType "journal-article" @default.
• W3215757687 hasAuthorship W3215757687A5054390609 @default.
• W3215757687 hasAuthorship W3215757687A5085387707 @default.
• W3215757687 hasConcept C105795698 @default.
• W3215757687 hasConcept C119857082 @default.
• W3215757687 hasConcept C124101348 @default.
• W3215757687 hasConcept C127413603 @default.
• W3215757687 hasConcept C148483581 @default.
• W3215757687 hasConcept C154945302 @default.
• W3215757687 hasConcept C169258074 @default.
• W3215757687 hasConcept C176217482 @default.
• W3215757687 hasConcept C203868755 @default.
• W3215757687 hasConcept C21547014 @default.
• W3215757687 hasConcept C33923547 @default.
• W3215757687 hasConcept C41008148 @default.
• W3215757687 hasConcept C42475967 @default.
• W3215757687 hasConcept C46686674 @default.
• W3215757687 hasConcept C50644808 @default.
• W3215757687 hasConcept C51566761 @default.
• W3215757687 hasConcept C70153297 @default.
• W3215757687 hasConcept C73555534 @default.
• W3215757687 hasConcept C83546350 @default.
• W3215757687 hasConceptScore W3215757687C105795698 @default.
• W3215757687 hasConceptScore W3215757687C119857082 @default.
• W3215757687 hasConceptScore W3215757687C124101348 @default.
• W3215757687 hasConceptScore W3215757687C127413603 @default.
• W3215757687 hasConceptScore W3215757687C148483581 @default.
• W3215757687 hasConceptScore W3215757687C154945302 @default.
• W3215757687 hasConceptScore W3215757687C169258074 @default.
• W3215757687 hasConceptScore W3215757687C176217482 @default.
• W3215757687 hasConceptScore W3215757687C203868755 @default.
• W3215757687 hasConceptScore W3215757687C21547014 @default.
• W3215757687 hasConceptScore W3215757687C33923547 @default.
• W3215757687 hasConceptScore W3215757687C41008148 @default.
• W3215757687 hasConceptScore W3215757687C42475967 @default.
• W3215757687 hasConceptScore W3215757687C46686674 @default.
• W3215757687 hasConceptScore W3215757687C50644808 @default.
• W3215757687 hasConceptScore W3215757687C51566761 @default.

• next