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https://hdl.handle.net/10316/108860
Título: | A Data-driven Approach for qu Prediction of Laboratory Soil-cement Mixtures | Autor: | Tinoco, Joaquim Correia, António Alberto Venda, Paulo da Correia, António Gomes Lemos, Luís |
Palavras-chave: | Soil-cement mixtures; Laboratory formulations; Uniaxial compressive strength; Data mining; Neuronal networks; Sensitivity analysis | Data: | 2016 | Editora: | Elsevier | Projeto: | Universities of Minho and Coimbra, ISISE, CIEPQPF and ACIV | Título da revista, periódico, livro ou evento: | Procedia Engineering | Volume: | 143 | Resumo: | 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. | URI: | https://hdl.handle.net/10316/108860 | ISSN: | 18777058 | DOI: | 10.1016/j.proeng.2016.06.073 | Direitos: | openAccess |
Aparece nas coleções: | FCTUC Eng.Civil - Artigos em Revistas Internacionais |
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Ficheiro | Descrição | Tamanho | Formato | |
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A Data-driven Approach for qu Prediction of Laboratory Soil-cement Mixtures.pdf | 395.53 kB | Adobe PDF | Ver/Abrir |
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