Application of data mining techniques to jet grouting columns design

Tese de doutoramento em Civil Engineering (ramo de conhecimento em Geotechnics)Jet Grouting (JG) is actually a reference method on soil improvement technologies, allowing to improve the strength, stiffness and permeability of soft soils. However, even after several years of practice and notable technology advances, there are still some limitations to overcome. In particular, the main limitation is the absence of efficient approaches for its design. Indeed, the actual design approaches are essentially based on empirically and less accurate methods that are often too conservatives. As a results, the economy and the quality of the treatment can be affected. Therefore, it is fundamental to develop new approaches able to accurately predict JG columns mechanical properties as well as its diameter. However, due to the high number of variables involved in JG process and the heterogeneity of the soils treated, the accomplishment of such complex task represents a major challenge. This challenge relies in the fact that a JG model design should be able to incorporate simultaneously the effect of different variables (e.g. soil and cement slurry properties). So far, the traditional statistical approaches were unable to deal with the complexity of JG data. However, in the past few years powerful tools have emerged for extracting useful information from large and complex data. These tools are currently known as Data Mining (DM) techniques and have been successfully applied in different application domains.. In the present research work, some of the most well known DM algorithms were applied in the prediction of the mechanical properties of JG mixtures as well as JG columns diameter. Therefore, and as a first step, a multiple regression, artificial neural network, support vector machine and functional network algorithms were trained to predict JG laboratory formulations stiffness and uniaxial compressive strength. Moreover, the analytical expressions proposed by Eurocode 2 and CEB-FIP Model Code 1990 for strength and stiffness prediction of concrete were adapted to JG mixtures. After that, the same methodologies were applied in the prediction of strength, stiffness and column diameter of real JG columns. As the main outcomes of this work, high quality predictive models were achieved, as well as a better understanding of the JG mixtures behaviour (given by a global sensitivity analysis). Such results are quite useful for JG design, being expecting an economic and technical improvement through a better optimization of the available resources and efficient designJet Grouting (JG) surge atualmente como um método de referência entre as tecnologias de melhoramento de solos, permitindo o aumento da resistência e deformabilidade bem como a diminuição da permeabilidade de solos moles. No entanto, mesmo após vários anos de prática e de notáveis avanços tecnológicos, existem ainda algumas limitações a vencer. Uma das mais relevantes prende-se com a ausência de abordagens eficientes de dimensionamento. De facto, as atuais abordagens de cálculo são essencialmente suportadas por métodos empíricos e pouco precisos, por vezes até demasiado conservativos. Em consequência, a eficiência técnica e económica do tratamento pode ficar comprometida. Neste sentido, é fundamental desenvolver novas abordagens capazes de prever com maior precisão as propriedades mecânicas e respectivo diâmetro das colunas de JG. Contudo, devido ao elevado número de variáveis envolvidas e à heterogeneidade dos solos tratados, tal tarefa representa um enorme desafio. Este desafio prende-se com o facto de um modelo de dimensionamento da tecnologia de JG dever ser capaz de incorporar simultaneamente o efeito de diferente variáveis (e.g. propriedades do solo e da calda injetada e o tipo de jet). Até aos dias de hoje, as ferramentas estatísticas tradicionais foram incapazes de lidar com a complexidade caracteristica de dados JG. No entanto, nos últimos anos têm emergido ferramentas com enorme potencial, capazes de analisar e extrair informação útil de grandes volumes de dados complexos. Estas ferramentas são correntemente conhecidas como técnicas de Data Mining (DM) e têm sido aplicadas com sucesso em diferentes áreas do conhecimento. No presente trabalho de investigação, alguns dos mais conhecidos algoritmos de DM foram aplicados na previsão das propriedades mecânicas de misturas de JG bem como na previsão do diâmetro das respetivas colunas. Assim, numa primeira fase, os algoritmos de regressão múltipla, redes neuronais artificiais, máquina de vetores de suporte e redes funcionais foram treinados para prever a deformabilidade e a resistência à compressão uniaxial de formulações laboratoriais de JG. Além disso, as expressões analíticas propostas pelo Eurocódigo 2 e pelo CEB-FIP Model Code 1990 usadas na previsão da resistência e deformabilidade do betão, foram também adaptadas a misturas de JG. Posteriormente, as mesmas metodologias foram aplicadas na previsão da resistência, deformabilidade e diâmetro de colunas reais de JG. Como principais resultados do presente trabalho, destaca-se a elevada qualidade previsional dos modelos obtidos, bem como uma melhor compreensão do comportamento de misturas de JG (conseguida através da aplicação de análises de sensibilidade globais). Estes resultados são um claro contributo para o dimensionamento de colunas de JG, antevendo-se uma maior eficiência técnica e económica, através de uma melhor otimização dos recursos disponíveis e eficiência no dimensionamento

EC2 model applied to the prediction of mechanical properties of soil cement based on test results at early ages

O modelo analítico proposto pelo Eurocódigo 2 (EC2) para a previsão das propriedades mecânicas do betão ao longo do tempo tem mostrado resultados bastante satisfatórios quando adaptado a formulações laboratoriais de Jet Grouting (JG) e de Cutter Soil Mixing (CSM). No entanto, apresenta com principal limitação o facto de estar dependente da realização de ensaios experimentais aos 28 dias de cura para a quantificação das respetivas propriedades, o que limita a sua aplicabilidade em fases mais avançadas do projeto, nomeadamente para fins de controlo de qualidade. No presente artigo o modelo analítico proposto pelo EC2 para a previsão da resistência e rigidez do betão é adaptado a formulações laboratoriais de JG e CSM. Em particular, a abordagem do EC2 é adaptada no sentido de considerar resultados laboratoriais a idades jovens, nomeadamente aos 3, 7 e 14 dias de cura, em substituição dos convencionais 28 dias. Os resultados obtidos mostram que o desempenho do modelo do EC2 aumenta proporcionalmente à idade dos resultados experimentais considerados. Contudo, observou-se também apenas uma ligeira diferença entre o desempenho do modelo do EC2 considerando resultados experimentais aos 14 e aos 28 dias, o que permite fazer um balanceamento entre a precisão do modelo e o tempo/custos totais do projeto.The Eurocode 2 (EC2) approach for strength and stiffness prediction of concrete has been successful adapted to soil-cement laboratory formulations for Jet Grouting (JG) and Cutter Soil Mixing (CSM) technologies. However, its dependence of 28 days test result represents an important limitation. Accordingly, in the present work EC2 approach is modified in order to use laboratory reference data at early ages (e.g. 3, 7 or 14 days) and the achieved results are compared with the conventional 28 days time of cure. As expected, the achieved results show a decrease in EC2 approach performance when reference data at early ages are used. However, it is also observed just a slightly difference in EC2 approach performance when test data at 14 days or 28 days are used. This observation allows us to balance the model prediction accuracy and time consuming in the final project and construction work costs

Application of data mining techniques in the estimation of the uniaxial compressive strength of jet grouting columns over time

Jet grouting (JG) is a soil treatment technique which is the best solution for several soil improvement problems. However, JG lacks design rules and quality controls. As a result, the main JG works are planned from empirical rules that are too conservative. The development of rational models to simulate the effects of the different parameters involved in the JG process is of primary importance in order to satisfy the binomial safety-economy that is required in any engineering project. In this paper, we present a new approach to predict the uniaxial compressive strength (UCS) of JG materials based on data mining techniques. This model was developed and verified using data from a JG laboratory formulation that involves the measurement of UCS. The results of the proposed approach are compared with the EC2 analytical model adapted to the JG material, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the UCS of JG material and its contributing factors.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/ 45781/2008Tecnasol-FG

Support vector machines in mechanical properties prediction of jet grouting columns

Strength and stiffness are the mechanical properties currently used in geotechnical works design, namely in jet grouting (JG) treatments. However, when working with this soil improvement technology, due to its inherent geological complexity and high number of variables involved, such design is a hard, perhaps very hard task. To help in such task, support vector machine (SVM), which is a data mining algorithm especially adequate to explore high number of complex data, can be used to learn the complex relationship between mechanical properties of JG samples extracted from real JG columns (JGS) and its contributing factors. In the present paper, the high capabilities of SVM in Uniaxial Compressive Strength (UCS) and Elastic Young Modulus estimation of JG laboratory formulations are summarized. After that, the performance reached by the same algorithm in the study of JGS are presented and discussed. It is shown, by performing a detailed sensitivity analysis, that the relation between mixture porosity and the volumetric content of cement, as well as the JG system are the key variables in UCS prediction of JGS. Furthermore, it is underlined the exponential effect of the age of the mixture in UCS estimation as well as the high iteration between these two key variables.Fundação para a Ciência e a Tecnologia (FCT

Application of a sensitivity analysis procedure to interpret uniaxial compressive strength prediction of jet grouting laboratory formulations performed by SVM model

Jet Grouting (JG) technology, one of the most efficient soft soils improvement methods, has been widely applied in important geotechnical works due to its versatility. However, there is still an important limitation to overcome related with the absence of rational approaches for its design. In the present work, three different Data Mining (DM) techniques, i.e., Artificial Neuronal Networks (ANN), Support Vector Machines (SVM) and multiple regression are trained in order to predict elastic young modulus (E0) of JG mixtures. It is shown that the complex relationships between E0 and its contribut- ing factors can be learned using DM tools, particularly by SVM and ANN algorithms. By performing a detailed sensitivity analysis, understandable knowledge is extracted from the trained models, in terms of the relative importance of the attributes, as well as its effect in E0 prediction. In addition, the mathemati- cal expression proposed by Eurocode 2 to estimate concrete stiffness, is adapted to JG material. Its low performance is assessed and compared with those achieved by DM models.The authors wish to thank to “Fundação para a Ciência e a Tecnologia” (FCT) for the finan- cial support under the strategic project PEst-OE/ ECI/UI4047/2011 and the doctoral Grant SFRH/ BD/45781/2008Tecnasol-FG

Uniaxial compressive strength prediction of jet grouting columns using support vector machines

Uniaxial compressive strength (UCS) is the mechanical properties currently used in geotechnical works design, namely in jet grouting (JG) treatments. However, when working with this soil improvement technology, due to its inherent geological complexity and high number of variables involved, such design is a hard, perhaps very hard task. To help in such task, a support vector machine (SVM), which is a data mining algorithm particularly adequate to explore high number of complex data, was trained to estimate UCS of JG samples extracted from real JG columns. In the present paper, the performance reached by SVM algorithm in UCS estimation is shown and discussed. Furthermore, the relation between mixture porosity and volumetric content of cement and the JG system were identified as key parameters by performing a 1-D sensitivity analysis. In addition, the effect and the interaction between the key variables in UCS estimation was measured and analyzed.Tecnasol-FGEFundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/45781/200

Support vector machines on mechanical behaviour prediction of soil-cement laboratory formulations to jet grouting columns

Fundação Para a Ciência e a Tecnologia (FCT) pelo apoio financeiro no âmbito do projeto PEst-OE/ECI/UI4047/2011 e pela bolsa de doutoramento SFRH/BD/45781/200

Data-driven models for uniaxial compressive strength prediction applied to unseen data

Data Mining (DM) techniques have been successfully applied to solve a wide range of real-world problems in different real-world domains, particularly in the field of geotechnical civil engineering. A remarkable example is their use in Jet Grouting (JG) technology. Due to the high number of parameters involved and to the heterogeneity of the soil, JG mechanical properties prediction, as well as columns diameter, are complex tasks. Accordingly, the high learning capabilities of DM, namely of the Support Vector Machine (SVM), were applied in the development of new approaches to accurately perform such tasks. This paper aims to assess the SVM model performance trained to predict Uniaxial Compressive Strength (UCS) of JG samples extracted directly from JG columns, when applied to a new set of records collected from a new JG work not contemplated in the database used during the model learning phase. The achieved results highlight the importance of the model domain applicability, as well as the restrictions and recommendations for its generalization when applied to new JG work data not contemplated in the training dataset.The authors wish to thank to Fundacao para a Cienciae a Tecnologia (FCT) for the financial support under the Pos-Doc grant of strategic project PEstOE/ECI/UI4047/2011. Also, the authors would like to thank the interest Tecnasol-FGE company for providing all data needed

A data mining approach for jet grouting uniaxial compressive strength prediction

Jet Grouting (JG) is a Geotechnical Engineering technique that is characterized by a great versatility, being the best solution for several soil treatment improvement problems. However, JG lacks design rules and quality control. As the result, the main JG works are planned from empirical rules that are often too conservative. The development of rational models to simulate the effect of the different parameters involved in the JG process is of primary importance in order to satisfy the binomial safety-economy that is required in any engineering project. In this work, three data mining models, i.e. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN), were adapted to predict the Uniaxial Compressive Strength (UCS) of JG laboratory formulations. A comparative study was held, by using a dataset used that was obtained from several studies previously accomplished in University of Minho. We show that the novel data-driven models are able to learn with high accuracy the complex relationships between the UCS of JG laboratory formulations and its contributing factors.Tecnasol-FG

Previsão do comportamento mecânico de formulações laboratoriais de solo-cimento para colunas de jet grouting com recurso a máquina de vetores de suporte

Atualmente, no âmbito dos métodos de tratamentos de solos, o jet grouting (JG) é uma das tecnologias mais utilizadas, nomeadamente em importantes obras geotécnicas, caracterizando-se pela sua grande versatilidade. No entanto, no que respeita à previsão das propriedades mecânicas do novo material resultante do tratamento, a heterogeneidade dos solos e o elevado número de parâmetros envolvidos são os fatores que mais condicionam a existência de modelos racionais e precisos. O presente trabalho visa contribuir para o desenvolvimento de abordagens racionais e precisas com vista à previsão da resistência à compressão uniaxial e respetivo módulo de deformabilidade de formulações laboratoriais de JG. Para o efeito, recorreu-se à aplicação de técnicas de data mining, particularmente do algoritmo máquinas de vetores de suporte. Foi ainda realizada uma análise de sensibilidade detalhada, visando identificar as variáveis chave e qual o seu efeito no estudo das propriedades mecânicas de formulações laboratoriais de JG