Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/44929

TítuloA data-driven approach for qᵤ prediction of laboratory soil-cement mixtures
Autor(es)Tinoco, Joaquim Agostinho Barbosa
Correia, António Alberto Santo
Paulo da Venda
Correia, A. Gomes
Luís Lemos
Palavras-chaveSoil-cement mixtures
Laboratory formulations
Uniaxial compressive strength
Data mining
Neuronal networks
Sensitivity analysis
DataJul-2016
EditoraElsevier
RevistaProcedia Engineering
CitaçãoTinoco, J., Alberto, A., Da Venda, P., Correia, A. G., & Lemos, L. (2016). A data-driven approach for qu prediction of laboratory soil-cement mixtures. Procedia Engineering, 143:566-573, July 2016. ISSN 1877-7058. doi: 10.1016/j.proeng.2016.06.073
Resumo(s)In this paper a new data-driven approach is proposed for uniaxial compressive strength (qu) prediction of laboratory soil-cement mixtures. The proposed model is able to predict qu over time under different conditions, e.g. different cement contents or soil types, and can be applied at the pre-design stage. This means that the model can be applied previously to the preparation of any laboratory formulation. The designer only needs to collect information about the main geotechnical soil properties (grain size, organic matter content, among other) and select the binder composition to prepare the mixture. Based on a sensitivity analysis, the key model variables were identified and its effect quantified. Thus, it was caught by the model the most relevant variables in qu prediction over time and very high prediction capacity with an overall regression coefficient higher than 0.95.
TipoconferencePaper
URIhttp://hdl.handle.net/1822/44929
DOI10.1016/j.proeng.2016.06.073
ISSN1877-7058
Arbitragem científicayes
AcessoopenAccess
Aparece nas coleções:ISISE - Artigos em Revistas Internacionais

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