Jet grouting column diameter prediction based on a data-driven approach
Autor: | Paulo Cortez, A. Gomes Correia, Joaquim Agostinho Barbosa Tinoco |
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Přispěvatelé: | Universidade do Minho |
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Engineering
Environmental Engineering 0211 other engineering and technologies 02 engineering and technology engineering.material Column (database) Data-driven Set (abstract data type) Engenharia e Tecnologia::Engenharia Civil 0202 electrical engineering electronic engineering information engineering Column diameter Data mining 021101 geological & geomatics engineering Civil and Structural Engineering Jet (fluid) Support vector machines Science & Technology Artificial neural network Jet grouting business.industry Grout Process (computing) Structural engineering Soil improvement Support vector machine Engenharia Civil [Engenharia e Tecnologia] 020201 artificial intelligence & image processing business Sensitivity analysis Algorithm Neural networks |
Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
Popis: | This study takes advantage of the high learning capabilities of data mining (DM) techniques towards to the development of a novel approach for jet grouting (JG) column diameter prediction. The high number of variables involved in JG technology as well as the complex phenomena related with the injection process make JG column diameter (D) prediction a difficult task. Therefore, in order to overcome it, the flexible learning capabilities of DM techniques were applied as an alternative approach of the traditional tools. The achieved results show that both artificial neural network and support vector machine algorithms can be trained to accurately predict D built in different soil types of clayey nature and using different JG systems. In both cases a coefficient of correlation () very close to the unity was achieved. For models training, a set of eight input variables were considered. Among them, the rod withdrawal speed, flow rate of the grout slurry and the JG system were identified as the most relevant ones, although the grout pressure and the dynamic impact of the grout also revealed an important influence on D prediction. Moreover, additionally to the identification of the key model variables, it was also measured their effects on D prediction based on a data-based sensitivity analysis. These achievements represent a novel contribution for JG technology, mainly at the design level. Furthermore, the obtained results also underline the potential and contribution of DM to solving complex problem in geotechnical engineering. The authors wish to thank to “Fundação para a Ciência e a Tecnologia” (FCT) for the financial support under the strategic project PEst-OE/ECI/UI4047/2011 as well as the Pos-Doc grant SFRH/BPD/94792/2013. Also, the authors would like to thank the interest of Tecnasol-FGE that supplied all data used in this study. info:eu-repo/semantics/publishedVersion |
Databáze: | OpenAIRE |
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