FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3201770868> ?p ?o ?g. }

• W3201770868 endingPage "11" @default.
• W3201770868 startingPage "1" @default.
• W3201770868 abstract "Corrosion is one of the main concerns in the field of structural engineering due to its effect on steel buried in soil. Currently, there is no clearly established method that allows its calculation with precision and ensures the durability of this type of structures. Qualitative methods are commonly used rather than quantitative methods. The objective of this research is the development of a multivariate quantitative predictive model for estimating the loss of thickness that will occur in buried hot-dip galvanized steel as a function of time. The technique used in the modelling is the Adaptive Regression of Multivariate Splines (MARS). The main drawback of this kind of studies is the lack of data since it is not possible to have a priori the corrosive behaviour that the buried material will have as a function of time. To solve this issue, a solid and reliable database was built from the analysis and treatment of the existing literature and with the results obtained from a predictive model to estimate the thickness loss of ungalvanized steel. The input variables of the model are 5 characteristics of the soil, the useful life of the structure, and the loss of corroded ungalvanized steel in the soil. This last data is the output variable of another previous predictive model to estimate the loss of thickness of bare steel in a soil. The objective variable of the model is the loss of thickness that hot-dip galvanized steel will experience buried in the ground and expressed in g/m2. To evaluate the performance and applicability of the proposed model, the statistical metrics RMSE, R2, MAE, and RAE and the graphs of standardized residuals were used. The results indicated that the model offers a very high prediction performance. Specifically, the mean square error was 290.6 g/m2 (range of the objective variable is from 51.787 g/m2 to 5950.5 g/m2), R2 was 0.96, and from a relative error of 0.14, the success of the estimate was 100%. Therefore, the use of the proposed predictive model optimizes the relationship between the amount of hot-dip galvanized steel and the useful life of the buried metal structure." @default.
• W3201770868 created "2021-10-11" @default.
• W3201770868 creator A5001284489 @default.
• W3201770868 creator A5030674403 @default.
• W3201770868 creator A5072117490 @default.
• W3201770868 creator A5076792504 @default.
• W3201770868 date "2021-09-29" @default.
• W3201770868 modified "2023-10-17" @default.
• W3201770868 title "Corrosion Predictive Model in Hot-Dip Galvanized Steel Buried in Soil" @default.
• W3201770868 cites W1966979457 @default.
• W3201770868 cites W1974583112 @default.
• W3201770868 cites W1982850185 @default.
• W3201770868 cites W1991650832 @default.
• W3201770868 cites W2005207103 @default.
• W3201770868 cites W2016083957 @default.
• W3201770868 cites W2024588805 @default.
• W3201770868 cites W2060527569 @default.
• W3201770868 cites W2075634159 @default.
• W3201770868 cites W2092269141 @default.
• W3201770868 cites W2102201073 @default.
• W3201770868 cites W2167623551 @default.
• W3201770868 cites W2201366100 @default.
• W3201770868 cites W2257739456 @default.
• W3201770868 cites W2325611075 @default.
• W3201770868 cites W2522612621 @default.
• W3201770868 cites W2738396933 @default.
• W3201770868 cites W2792416274 @default.
• W3201770868 cites W2798133435 @default.
• W3201770868 cites W2800346842 @default.
• W3201770868 cites W2951871563 @default.
• W3201770868 cites W2987477136 @default.
• W3201770868 cites W2997527504 @default.
• W3201770868 cites W3109019274 @default.
• W3201770868 cites W3117599315 @default.
• W3201770868 cites W3131678448 @default.
• W3201770868 cites W429766147 @default.
• W3201770868 doi "https://doi.org/10.1155/2021/9275779" @default.
• W3201770868 hasPublicationYear "2021" @default.
• W3201770868 type Work @default.
• W3201770868 sameAs 3201770868 @default.
• W3201770868 citedByCount "3" @default.
• W3201770868 countsByYear W32017708682022 @default.
• W3201770868 crossrefType "journal-article" @default.
• W3201770868 hasAuthorship W3201770868A5001284489 @default.
• W3201770868 hasAuthorship W3201770868A5030674403 @default.
• W3201770868 hasAuthorship W3201770868A5072117490 @default.
• W3201770868 hasAuthorship W3201770868A5076792504 @default.
• W3201770868 hasBestOaLocation W32017708681 @default.
• W3201770868 hasConcept C104304963 @default.
• W3201770868 hasConcept C119857082 @default.
• W3201770868 hasConcept C127413603 @default.
• W3201770868 hasConcept C134306372 @default.
• W3201770868 hasConcept C152877465 @default.
• W3201770868 hasConcept C159985019 @default.
• W3201770868 hasConcept C161584116 @default.
• W3201770868 hasConcept C182365436 @default.
• W3201770868 hasConcept C191897082 @default.
• W3201770868 hasConcept C192562407 @default.
• W3201770868 hasConcept C20625102 @default.
• W3201770868 hasConcept C2779227376 @default.
• W3201770868 hasConcept C33923547 @default.
• W3201770868 hasConcept C39432304 @default.
• W3201770868 hasConcept C41008148 @default.
• W3201770868 hasConcept C44882253 @default.
• W3201770868 hasConcept C64946054 @default.
• W3201770868 hasConcept C66938386 @default.
• W3201770868 hasConcept C8846229 @default.
• W3201770868 hasConceptScore W3201770868C104304963 @default.
• W3201770868 hasConceptScore W3201770868C119857082 @default.
• W3201770868 hasConceptScore W3201770868C127413603 @default.
• W3201770868 hasConceptScore W3201770868C134306372 @default.
• W3201770868 hasConceptScore W3201770868C152877465 @default.
• W3201770868 hasConceptScore W3201770868C159985019 @default.
• W3201770868 hasConceptScore W3201770868C161584116 @default.
• W3201770868 hasConceptScore W3201770868C182365436 @default.
• W3201770868 hasConceptScore W3201770868C191897082 @default.
• W3201770868 hasConceptScore W3201770868C192562407 @default.
• W3201770868 hasConceptScore W3201770868C20625102 @default.
• W3201770868 hasConceptScore W3201770868C2779227376 @default.
• W3201770868 hasConceptScore W3201770868C33923547 @default.
• W3201770868 hasConceptScore W3201770868C39432304 @default.
• W3201770868 hasConceptScore W3201770868C41008148 @default.
• W3201770868 hasConceptScore W3201770868C44882253 @default.
• W3201770868 hasConceptScore W3201770868C64946054 @default.
• W3201770868 hasConceptScore W3201770868C66938386 @default.
• W3201770868 hasConceptScore W3201770868C8846229 @default.
• W3201770868 hasLocation W32017708681 @default.
• W3201770868 hasOpenAccess W3201770868 @default.
• W3201770868 hasPrimaryLocation W32017708681 @default.
• W3201770868 hasRelatedWork W1529675224 @default.
• W3201770868 hasRelatedWork W1991715599 @default.
• W3201770868 hasRelatedWork W225997466 @default.
• W3201770868 hasRelatedWork W2347630940 @default.
• W3201770868 hasRelatedWork W2763130398 @default.
• W3201770868 hasRelatedWork W2791899021 @default.
• W3201770868 hasRelatedWork W2901599058 @default.
• W3201770868 hasRelatedWork W39561353 @default.
• W3201770868 hasRelatedWork W4288535524 @default.

• next