FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4280581977> ?p ?o ?g. }

• W4280581977 abstract "Purpose The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods. Design/methodology/approach Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations. Findings According to the study results, several regression models were developed with a big variation in the coefficients of determination ( R 2 ). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R 2 equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses. Practical implications The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options. Originality/value To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved." @default.
• W4280581977 created "2022-05-22" @default.
• W4280581977 creator A5034148034 @default.
• W4280581977 creator A5065411913 @default.
• W4280581977 date "2022-05-24" @default.
• W4280581977 modified "2023-09-29" @default.
• W4280581977 title "Development of pavement roughness regression models based on smartphone measurements" @default.
• W4280581977 cites W1979137945 @default.
• W4280581977 cites W1996951941 @default.
• W4280581977 cites W2031169143 @default.
• W4280581977 cites W2044923862 @default.
• W4280581977 cites W2084425001 @default.
• W4280581977 cites W2085921433 @default.
• W4280581977 cites W2104107943 @default.
• W4280581977 cites W2111997054 @default.
• W4280581977 cites W2126979422 @default.
• W4280581977 cites W2128320031 @default.
• W4280581977 cites W2148304743 @default.
• W4280581977 cites W2397746749 @default.
• W4280581977 cites W2538931973 @default.
• W4280581977 cites W2584586614 @default.
• W4280581977 cites W2755357646 @default.
• W4280581977 cites W2769972173 @default.
• W4280581977 cites W2793748566 @default.
• W4280581977 cites W2905376801 @default.
• W4280581977 cites W2929133625 @default.
• W4280581977 cites W2932440871 @default.
• W4280581977 cites W2954697253 @default.
• W4280581977 cites W2969085785 @default.
• W4280581977 cites W3003017134 @default.
• W4280581977 cites W3088821534 @default.
• W4280581977 cites W3186360865 @default.
• W4280581977 cites W3197178112 @default.
• W4280581977 doi "https://doi.org/10.1108/jedt-12-2021-0723" @default.
• W4280581977 hasPublicationYear "2022" @default.
• W4280581977 type Work @default.
• W4280581977 citedByCount "1" @default.
• W4280581977 countsByYear W42805819772022 @default.
• W4280581977 crossrefType "journal-article" @default.
• W4280581977 hasAuthorship W4280581977A5034148034 @default.
• W4280581977 hasAuthorship W4280581977A5065411913 @default.
• W4280581977 hasConcept C105795698 @default.
• W4280581977 hasConcept C107365816 @default.
• W4280581977 hasConcept C119857082 @default.
• W4280581977 hasConcept C127413603 @default.
• W4280581977 hasConcept C152877465 @default.
• W4280581977 hasConcept C159985019 @default.
• W4280581977 hasConcept C171146098 @default.
• W4280581977 hasConcept C192562407 @default.
• W4280581977 hasConcept C22212356 @default.
• W4280581977 hasConcept C27574286 @default.
• W4280581977 hasConcept C2776652587 @default.
• W4280581977 hasConcept C2780996376 @default.
• W4280581977 hasConcept C2781212230 @default.
• W4280581977 hasConcept C33923547 @default.
• W4280581977 hasConcept C41008148 @default.
• W4280581977 hasConcept C45804977 @default.
• W4280581977 hasConcept C48921125 @default.
• W4280581977 hasConcept C71039073 @default.
• W4280581977 hasConcept C78519656 @default.
• W4280581977 hasConcept C83546350 @default.
• W4280581977 hasConceptScore W4280581977C105795698 @default.
• W4280581977 hasConceptScore W4280581977C107365816 @default.
• W4280581977 hasConceptScore W4280581977C119857082 @default.
• W4280581977 hasConceptScore W4280581977C127413603 @default.
• W4280581977 hasConceptScore W4280581977C152877465 @default.
• W4280581977 hasConceptScore W4280581977C159985019 @default.
• W4280581977 hasConceptScore W4280581977C171146098 @default.
• W4280581977 hasConceptScore W4280581977C192562407 @default.
• W4280581977 hasConceptScore W4280581977C22212356 @default.
• W4280581977 hasConceptScore W4280581977C27574286 @default.
• W4280581977 hasConceptScore W4280581977C2776652587 @default.
• W4280581977 hasConceptScore W4280581977C2780996376 @default.
• W4280581977 hasConceptScore W4280581977C2781212230 @default.
• W4280581977 hasConceptScore W4280581977C33923547 @default.
• W4280581977 hasConceptScore W4280581977C41008148 @default.
• W4280581977 hasConceptScore W4280581977C45804977 @default.
• W4280581977 hasConceptScore W4280581977C48921125 @default.
• W4280581977 hasConceptScore W4280581977C71039073 @default.
• W4280581977 hasConceptScore W4280581977C78519656 @default.
• W4280581977 hasConceptScore W4280581977C83546350 @default.
• W4280581977 hasLocation W42805819771 @default.
• W4280581977 hasOpenAccess W4280581977 @default.
• W4280581977 hasPrimaryLocation W42805819771 @default.
• W4280581977 hasRelatedWork W1541739370 @default.
• W4280581977 hasRelatedWork W2001258722 @default.
• W4280581977 hasRelatedWork W2003085719 @default.
• W4280581977 hasRelatedWork W2083647837 @default.
• W4280581977 hasRelatedWork W2167660240 @default.
• W4280581977 hasRelatedWork W3188410990 @default.
• W4280581977 hasRelatedWork W4212972958 @default.
• W4280581977 hasRelatedWork W4244187931 @default.
• W4280581977 hasRelatedWork W4280581977 @default.
• W4280581977 hasRelatedWork W4321847880 @default.
• W4280581977 isParatext "false" @default.
• W4280581977 isRetracted "false" @default.
• W4280581977 workType "article" @default.