Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/44929
Título: | A 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-chave: | Soil-cement mixtures Laboratory formulations Uniaxial compressive strength Data mining Neuronal networks Sensitivity analysis |
Data: | Jul-2016 |
Editora: | Elsevier 1 |
Revista: | Procedia Engineering |
Citação: | Tinoco, 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/44929 |
DOI: | 10.1016/j.proeng.2016.06.073 |
ISSN: | 1877-7058 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | ISISE - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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A Data-driven Approach for qu Prediction of Laboratory Soil-cement Mixtures [Tinoco et al. 2016].pdf | 395,53 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons