Development of Predictive Models for Cement Stabilized Soils

Rakshya Shrestha1 and Abir Al-Tabbaa2

1Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ; Ph: + 44 (0) 1223 766683; email: rs544@cam.ac.uk 2Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ; Ph: +44 (0) 1223 332715; email: aa22@cam.ac.uk ABSTRACT: Factors that affect the engineering properties of cement stabilized soils such as strength are discussed in this paper using data on these factors. The selected factors studied in this paper are initial soil water content, grain size distribution, organic matter content, binder dosage, age and curing temperature, which has been collated from a number of international deep mixing projects. Some resulting correlations from this data are discussed and presented. The concept of Artificial Neural Networks and its applicability in developing predictive models for deep mixed soils is presented and discussed using a subset of the collated data. The results from the neural network model were found to emulate the known trends and reasonable estimates of strength as a function of the selected variables were obtained.

INTRODUCTION

The mechanical and/or engineering properties of cement stabilized deep mixed ground are a function of large number of factors such as the type and properties of soil, the stabilising agent and the conditions of mixing and curing such as time and temperature. Terashi (1997) has categorized these variables into (i) characteristics of soil (ii) characteristics of binder (iii) mixing conditions and (iv) curing conditions. There is a need to study the effect of these variables on the final strength gain by collating the large amount of data available from a number of deep mixing studies, which is the purpose of the present work.

The main factors affecting the strength development of deep mixed soils explored in this paper are soil properties such as initial soil water content, sand, silt and clay content and organic matter content in the soil; binder properties such as binder dosage and curing conditions such as curing time and temperature. Related studies (Endo, 1997; Porbaha et al., 2000; CDIT, 2001) have shown that an increase in the initial water content of the soil significantly reduces the compressive strength of the deep mixed soil for any particular cement content. Other studies (Taki and Yang, 1991; Terashi, 1997; Kitazume, 2005) have shown that grain size distribution in the soil affects the properties

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Development of Predictive Models for Cement Stabilized Soils

Rakshya Shrestha1 and Abir Al-Tabbaa2

1Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ; Ph: + 44 (0) 1223 766683; email: rs544@cam.ac.uk 2Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ; Ph: +44 (0) 1223 332715; email: aa22@cam.ac.uk ABSTRACT: Factors that affect the engineering properties of cement stabilized soils such as strength are discussed in this paper using data on these factors. The selected factors studied in this paper are initial soil water content, grain size distribution, organic matter content, binder dosage, age and curing temperature, which has been collated from a number of international deep mixing projects. Some resulting correlations from this data are discussed and presented. The concept of Artificial Neural Networks and its applicability in developing predictive models for deep mixed soils is presented and discussed using a subset of the collated data. The results from the neural network model were found to emulate the known trends and reasonable estimates of strength as a function of the selected variables were obtained.

INTRODUCTION

The mechanical and/or engineering properties of cement stabilized deep mixed ground are a function of large number of factors such as the type and properties of soil, the stabilising agent and the conditions of mixing and curing such as time and temperature. Terashi (1997) has categorized these variables into (i) characteristics of soil (ii) characteristics of binder (iii) mixing conditions and (iv) curing conditions. There is a need to study the effect of these variables on the final strength gain by collating the large amount of data available from a number of deep mixing studies, which is the purpose of the present work.

The main factors affecting the strength development of deep mixed soils explored in this paper are soil properties such as initial soil water content, sand, silt and clay content and organic matter content in the soil; binder properties such as binder dosage and curing conditions such as curing time and temperature. Related studies (Endo, 1997; Porbaha et al., 2000; CDIT, 2001) have shown that an increase in the initial water content of the soil significantly reduces the compressive strength of the deep mixed soil for any particular cement content. Other studies (Taki and Yang, 1991; Terashi, 1997; Kitazume, 2005) have shown that grain size distribution in the soil affects the properties

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Development of Predictive Models for Cement Stabilized Soils

Rakshya Shrestha1 and Abir Al-Tabbaa2

1Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ; Ph: + 44 (0) 1223 766683; email: rs544@cam.ac.uk 2Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ; Ph: +44 (0) 1223 332715; email: aa22@cam.ac.uk ABSTRACT: Factors that affect the engineering properties of cement stabilized soils such as strength are discussed in this paper using data on these factors. The selected factors studied in this paper are initial soil water content, grain size distribution, organic matter content, binder dosage, age and curing temperature, which has been collated from a number of international deep mixing projects. Some resulting correlations from this data are discussed and presented. The concept of Artificial Neural Networks and its applicability in developing predictive models for deep mixed soils is presented and discussed using a subset of the collated data. The results from the neural network model were found to emulate the known trends and reasonable estimates of strength as a function of the selected variables were obtained.

INTRODUCTION

The mechanical and/or engineering properties of cement stabilized deep mixed ground are a function of large number of factors such as the type and properties of soil, the stabilising agent and the conditions of mixing and curing such as time and temperature. Terashi (1997) has categorized these variables into (i) characteristics of soil (ii) characteristics of binder (iii) mixing conditions and (iv) curing conditions. There is a need to study the effect of these variables on the final strength gain by collating the large amount of data available from a number of deep mixing studies, which is the purpose of the present work.

The main factors affecting the strength development of deep mixed soils explored in this paper are soil properties such as initial soil water content, sand, silt and clay content and organic matter content in the soil; binder properties such as binder dosage and curing conditions such as curing time and temperature. Related studies (Endo, 1997; Porbaha et al., 2000; CDIT, 2001) have shown that an increase in the initial water content of the soil significantly reduces the compressive strength of the deep mixed soil for any particular cement content. Other studies (Taki and Yang, 1991; Terashi, 1997; Kitazume, 2005) have shown that grain size distribution in the soil affects the properties

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Development of Predictive Models for Cement Stabilized Soils

Rakshya Shrestha1 and Abir Al-Tabbaa2

1Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ; Ph: + 44 (0) 1223 766683; email: rs544@cam.ac.uk 2Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ; Ph: +44 (0) 1223 332715; email: aa22@cam.ac.uk ABSTRACT: Factors that affect the engineering properties of cement stabilized soils such as strength are discussed in this paper using data on these factors. The selected factors studied in this paper are initial soil water content, grain size distribution, organic matter content, binder dosage, age and curing temperature, which has been collated from a number of international deep mixing projects. Some resulting correlations from this data are discussed and presented. The concept of Artificial Neural Networks and its applicability in developing predictive models for deep mixed soils is presented and discussed using a subset of the collated data. The results from the neural network model were found to emulate the known trends and reasonable estimates of strength as a function of the selected variables were obtained.

INTRODUCTION

The mechanical and/or engineering properties of cement stabilized deep mixed ground are a function of large number of factors such as the type and properties of soil, the stabilising agent and the conditions of mixing and curing such as time and temperature. Terashi (1997) has categorized these variables into (i) characteristics of soil (ii) characteristics of binder (iii) mixing conditions and (iv) curing conditions. There is a need to study the effect of these variables on the final strength gain by collating the large amount of data available from a number of deep mixing studies, which is the purpose of the present work.

The main factors affecting the strength development of deep mixed soils explored in this paper are soil properties such as initial soil water content, sand, silt and clay content and organic matter content in the soil; binder properties such as binder dosage and curing conditions such as curing time and temperature. Related studies (Endo, 1997; Porbaha et al., 2000; CDIT, 2001) have shown that an increase in the initial water content of the soil significantly reduces the compressive strength of the deep mixed soil for any particular cement content. Other studies (Taki and Yang, 1991; Terashi, 1997; Kitazume, 2005) have shown that grain size distribution in the soil affects the properties

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121A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Cyan_08/07/2012_05:31:52 121A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Magenta_08/07/2012_05:31:52 121A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Yellow_08/07/2012_05:31:52 121A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Black_08/07/2012_05:31:52

Grouting and Deep Mixing 2012

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of the treated soil such that coarse grained soils show higher strength than fine grained soils for the same binder content. The presence of organic matter has been shown to interfere with the chemical reactions between the soil and binder and reduces the strength gain of the cement stabilized soil (EuroSoilStab, 2000). Increase in binder dosage has been shown to increase the strength irrespective of the soil types (Kawasaki et al., 1981; EuroSoilStab, 2000). An increase in strength of cement stabilized soil has been seen with curing time and similarly, an increase in curing temperature has been found to accelerate the rate of the hydration process increasing the rate of strength development in cement stabilised soils (Kawasaki et al., 1981).

While the numerous studies above reveal the independent effect of individual variables on the UCS (Unconfined Compressive Strength), the final strength gain is however determined by the simultaneous interaction of many of these individual variables and this is where the concept of Artificial Neural Networks (ANNs) finds its use. When a large number of variables affect the performance of a system and there is an interaction between the variables but these interactions are not obvious and difficult to define, ANNs enable the identification of the major and minor effects each individual variable has on the final performance of the system (Tarassenko, 1998; Mackay et al., 1999; Stegemann, 2001; Shahin, 2006). Neural network analysis is in fact a general method of regression which avoids the difficulties and assumptions that governs the traditional regression methods (Sourmail and Bhadeshia, 2005).

The potential of using ANNs for the development of predictive models in geotechnical problems has been demonstrated in different studies. Goh (1995) developed an ANN model to study the hydraulic conductivity of clay liners as a non-linear function of soil parameters such as plasticity index, clay percent, gravel percent and compacted weight which exhibited more reliably predicted values when compared to simple regression models. Lai and Serra (1997) developed neural computing model and predicted with sufficient approximation (an average error of 5%) the compressive strength of cement conglomerates as a function of mix design parameters such as type of cement, sand and aggregate content, cement content, water: cement ratio and super-plasticizer content using data from different studies. Stegemann (2001) developed models for the prediction of UCS of cement-solidified waste as a function of mix composition using data on Portland cement solidified waste products from a large number of studies. A comprehensive list of ANNs in geotechnical engineering has been summarized by Shahin et al. (2001).

To the authors’ best knowledge, ANNs have not yet been applied in developing predictive models for deep mix technology and there is a need to develop predictive models based on deep mix data from past experiences relating strength properties of soil mixed columns and walls to the many variables involved. This is very important for the

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of the treated soil such that coarse grained soils show higher strength than fine grained soils for the same binder content. The presence of organic matter has been shown to interfere with the chemical reactions between the soil and binder and reduces the strength gain of the cement stabilized soil (EuroSoilStab, 2000). Increase in binder dosage has been shown to increase the strength irrespective of the soil types (Kawasaki et al., 1981; EuroSoilStab, 2000). An increase in strength of cement stabilized soil has been seen with curing time and similarly, an increase in curing temperature has been found to accelerate the rate of the hydration process increasing the rate of strength development in cement stabilised soils (Kawasaki et al., 1981).

While the numerous studies above reveal the independent effect of individual variables on the UCS (Unconfined Compressive Strength), the final strength gain is however determined by the simultaneous interaction of many of these individual variables and this is where the concept of Artificial Neural Networks (ANNs) finds its use. When a large number of variables affect the performance of a system and there is an interaction between the variables but these interactions are not obvious and difficult to define, ANNs enable the identification of the major and minor effects each individual variable has on the final performance of the system (Tarassenko, 1998; Mackay et al., 1999; Stegemann, 2001; Shahin, 2006). Neural network analysis is in fact a general method of regression which avoids the difficulties and assumptions that governs the traditional regression methods (Sourmail and Bhadeshia, 2005).

The potential of using ANNs for the development of predictive models in geotechnical problems has been demonstrated in different studies. Goh (1995) developed an ANN model to study the hydraulic conductivity of clay liners as a non-linear function of soil parameters such as plasticity index, clay percent, gravel percent and compacted weight which exhibited more reliably predicted values when compared to simple regression models. Lai and Serra (1997) developed neural computing model and predicted with sufficient approximation (an average error of 5%) the compressive strength of cement conglomerates as a function of mix design parameters such as type of cement, sand and aggregate content, cement content, water: cement ratio and super-plasticizer content using data from different studies. Stegemann (2001) developed models for the prediction of UCS of cement-solidified waste as a function of mix composition using data on Portland cement solidified waste products from a large number of studies. A comprehensive list of ANNs in geotechnical engineering has been summarized by Shahin et al. (2001).

To the authors’ best knowledge, ANNs have not yet been applied in developing predictive models for deep mix technology and there is a need to develop predictive models based on deep mix data from past experiences relating strength properties of soil mixed columns and walls to the many variables involved. This is very important for the

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of the treated soil such that coarse grained soils show higher strength than fine grained soils for the same binder content. The presence of organic matter has been shown to interfere with the chemical reactions between the soil and binder and reduces the strength gain of the cement stabilized soil (EuroSoilStab, 2000). Increase in binder dosage has been shown to increase the strength irrespective of the soil types (Kawasaki et al., 1981; EuroSoilStab, 2000). An increase in strength of cement stabilized soil has been seen with curing time and similarly, an increase in curing temperature has been found to accelerate the rate of the hydration process increasing the rate of strength development in cement stabilised soils (Kawasaki et al., 1981).

While the numerous studies above reveal the independent effect of individual variables on the UCS (Unconfined Compressive Strength), the final strength gain is however determined by the simultaneous interaction of many of these individual variables and this is where the concept of Artificial Neural Networks (ANNs) finds its use. When a large number of variables affect the performance of a system and there is an interaction between the variables but these interactions are not obvious and difficult to define, ANNs enable the identification of the major and minor effects each individual variable has on the final performance of the system (Tarassenko, 1998; Mackay et al., 1999; Stegemann, 2001; Shahin, 2006). Neural network analysis is in fact a general method of regression which avoids the difficulties and assumptions that governs the traditional regression methods (Sourmail and Bhadeshia, 2005).

The potential of using ANNs for the development of predictive models in geotechnical problems has been demonstrated in different studies. Goh (1995) developed an ANN model to study the hydraulic conductivity of clay liners as a non-linear function of soil parameters such as plasticity index, clay percent, gravel percent and compacted weight which exhibited more reliably predicted values when compared to simple regression models. Lai and Serra (1997) developed neural computing model and predicted with sufficient approximation (an average error of 5%) the compressive strength of cement conglomerates as a function of mix design parameters such as type of cement, sand and aggregate content, cement content, water: cement ratio and super-plasticizer content using data from different studies. Stegemann (2001) developed models for the prediction of UCS of cement-solidified waste as a function of mix composition using data on Portland cement solidified waste products from a large number of studies. A comprehensive list of ANNs in geotechnical engineering has been summarized by Shahin et al. (2001).

To the authors’ best knowledge, ANNs have not yet been applied in developing predictive models for deep mix technology and there is a need to develop predictive models based on deep mix data from past experiences relating strength properties of soil mixed columns and walls to the many variables involved. This is very important for the

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of the treated soil such that coarse grained soils show higher strength than fine grained soils for the same binder content. The presence of organic matter has been shown to interfere with the chemical reactions between the soil and binder and reduces the strength gain of the cement stabilized soil (EuroSoilStab, 2000). Increase in binder dosage has been shown to increase the strength irrespective of the soil types (Kawasaki et al., 1981; EuroSoilStab, 2000). An increase in strength of cement stabilized soil has been seen with curing time and similarly, an increase in curing temperature has been found to accelerate the rate of the hydration process increasing the rate of strength development in cement stabilised soils (Kawasaki et al., 1981).

While the numerous studies above reveal the independent effect of individual variables on the UCS (Unconfined Compressive Strength), the final strength gain is however determined by the simultaneous interaction of many of these individual variables and this is where the concept of Artificial Neural Networks (ANNs) finds its use. When a large number of variables affect the performance of a system and there is an interaction between the variables but these interactions are not obvious and difficult to define, ANNs enable the identification of the major and minor effects each individual variable has on the final performance of the system (Tarassenko, 1998; Mackay et al., 1999; Stegemann, 2001; Shahin, 2006). Neural network analysis is in fact a general method of regression which avoids the difficulties and assumptions that governs the traditional regression methods (Sourmail and Bhadeshia, 2005).

The potential of using ANNs for the development of predictive models in geotechnical problems has been demonstrated in different studies. Goh (1995) developed an ANN model to study the hydraulic conductivity of clay liners as a non-linear function of soil parameters such as plasticity index, clay percent, gravel percent and compacted weight which exhibited more reliably predicted values when compared to simple regression models. Lai and Serra (1997) developed neural computing model and predicted with sufficient approximation (an average error of 5%) the compressive strength of cement conglomerates as a function of mix design parameters such as type of cement, sand and aggregate content, cement content, water: cement ratio and super-plasticizer content using data from different studies. Stegemann (2001) developed models for the prediction of UCS of cement-solidified waste as a function of mix composition using data on Portland cement solidified waste products from a large number of studies. A comprehensive list of ANNs in geotechnical engineering has been summarized by Shahin et al. (2001).

To the authors’ best knowledge, ANNs have not yet been applied in developing predictive models for deep mix technology and there is a need to develop predictive models based on deep mix data from past experiences relating strength properties of soil mixed columns and walls to the many variables involved. This is very important for the

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121B_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Cyan_08/07/2012_05:31:52121B_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Magenta_08/07/2012_05:31:52121B_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Yellow_08/07/2012_05:31:52121B_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Black_08/07/2012_05:31:52

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optimal and effective utilization of binders, soils, equipments, methods and the environmental conditions involved. The study reported here aims to investigate the relationships between the chosen set of variables mentioned above which are expected to have an effect on the final strength gain. The main objective is to explore the feasibility of using ANNs to develop predictive models in deep mix technology for soils stabilised with specific binders. ANNs SYSTEM AND DATASET DESCRIPTION In this work, the ANN model is developed using computer-based software package namely Model Manager (Sourmail, 2004), a feed-forward error back propagation non-linear data modeling tool that combines the Bayesian Probability theory with neural networks. The underlying Bayesian framework is described elsewhere (Mackay, 2003). The predictions of these Bayesian models are associated with error bars that give a limit to the reliability of the predictions. The model consists of a number of processing elements arranged in layers: an input layer, an output layer and one intermediate layer called the hidden layer as shown in Figure 1. The general ANN model can be given by the following equation:

)2.....(..........).........tanh(

)1......(..............................

)1()1(

)2()2(

ijjj

iji

ii

i

xwh

hwy

Each unit/input (xj) in the input layer which has an individual influence on the output are multiplied by adjustable connection weights (wij) and summed with a constant (θij) to form a combined input. This combined input serves as the input to the hidden layer. The units in the hidden layers are normally associated with non-linear transfer functions such as sigmoidal function, or in this case, hyperbolic tangent function (2) which transfers this combined input to the output layer in order to produce the final output (y) as in (1). wij

(1)) are defined as weights that determine the strength of input units and θij(1) is defined

as the bias. Similarly, wi(2) and θ(2) are second set of weights and biases. The number of

hidden nodes in the hidden layer and the connection weights are what we care to optimize during the process. The error (discrepancy between the target and the network output) calculated at output layer is propagated backward to adjust the weights during an iterative “training process”. The main objective of the training process is to limit this error which is minimized by successive iterations of the training data. This involves the adjustment of the connection weights between the layers until the network correctly reproduces the target values with the least error. Over-training or over-learning which is a common problem encountered in modeling is avoided by checking the performance of the model in a “test dataset” during training. Based on the performance of the model in the test set the model is rendered reliable for generalization. The next step is to check the predictive ability of the model in a “validation set” (a dataset not seen by the model

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optimal and effective utilization of binders, soils, equipments, methods and the environmental conditions involved. The study reported here aims to investigate the relationships between the chosen set of variables mentioned above which are expected to have an effect on the final strength gain. The main objective is to explore the feasibility of using ANNs to develop predictive models in deep mix technology for soils stabilised with specific binders. ANNs SYSTEM AND DATASET DESCRIPTION In this work, the ANN model is developed using computer-based software package namely Model Manager (Sourmail, 2004), a feed-forward error back propagation non-linear data modeling tool that combines the Bayesian Probability theory with neural networks. The underlying Bayesian framework is described elsewhere (Mackay, 2003). The predictions of these Bayesian models are associated with error bars that give a limit to the reliability of the predictions. The model consists of a number of processing elements arranged in layers: an input layer, an output layer and one intermediate layer called the hidden layer as shown in Figure 1. The general ANN model can be given by the following equation:

)2.....(..........).........tanh(

)1......(..............................

)1()1(

)2()2(

ijjj

iji

ii

i

xwh

hwy

Each unit/input (xj) in the input layer which has an individual influence on the output are multiplied by adjustable connection weights (wij) and summed with a constant (θij) to form a combined input. This combined input serves as the input to the hidden layer. The units in the hidden layers are normally associated with non-linear transfer functions such as sigmoidal function, or in this case, hyperbolic tangent function (2) which transfers this combined input to the output layer in order to produce the final output (y) as in (1). wij

(1)) are defined as weights that determine the strength of input units and θij(1) is defined

as the bias. Similarly, wi(2) and θ(2) are second set of weights and biases. The number of

hidden nodes in the hidden layer and the connection weights are what we care to optimize during the process. The error (discrepancy between the target and the network output) calculated at output layer is propagated backward to adjust the weights during an iterative “training process”. The main objective of the training process is to limit this error which is minimized by successive iterations of the training data. This involves the adjustment of the connection weights between the layers until the network correctly reproduces the target values with the least error. Over-training or over-learning which is a common problem encountered in modeling is avoided by checking the performance of the model in a “test dataset” during training. Based on the performance of the model in the test set the model is rendered reliable for generalization. The next step is to check the predictive ability of the model in a “validation set” (a dataset not seen by the model

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GROUTING AND DEEP MIXING 2012 223

optimal and effective utilization of binders, soils, equipments, methods and the environmental conditions involved. The study reported here aims to investigate the relationships between the chosen set of variables mentioned above which are expected to have an effect on the final strength gain. The main objective is to explore the feasibility of using ANNs to develop predictive models in deep mix technology for soils stabilised with specific binders. ANNs SYSTEM AND DATASET DESCRIPTION In this work, the ANN model is developed using computer-based software package namely Model Manager (Sourmail, 2004), a feed-forward error back propagation non-linear data modeling tool that combines the Bayesian Probability theory with neural networks. The underlying Bayesian framework is described elsewhere (Mackay, 2003). The predictions of these Bayesian models are associated with error bars that give a limit to the reliability of the predictions. The model consists of a number of processing elements arranged in layers: an input layer, an output layer and one intermediate layer called the hidden layer as shown in Figure 1. The general ANN model can be given by the following equation:

)2.....(..........).........tanh(

)1......(..............................

)1()1(

)2()2(

ijjj

iji

ii

i

xwh

hwy

Each unit/input (xj) in the input layer which has an individual influence on the output are multiplied by adjustable connection weights (wij) and summed with a constant (θij) to form a combined input. This combined input serves as the input to the hidden layer. The units in the hidden layers are normally associated with non-linear transfer functions such as sigmoidal function, or in this case, hyperbolic tangent function (2) which transfers this combined input to the output layer in order to produce the final output (y) as in (1). wij

(1)) are defined as weights that determine the strength of input units and θij(1) is defined

as the bias. Similarly, wi(2) and θ(2) are second set of weights and biases. The number of

hidden nodes in the hidden layer and the connection weights are what we care to optimize during the process. The error (discrepancy between the target and the network output) calculated at output layer is propagated backward to adjust the weights during an iterative “training process”. The main objective of the training process is to limit this error which is minimized by successive iterations of the training data. This involves the adjustment of the connection weights between the layers until the network correctly reproduces the target values with the least error. Over-training or over-learning which is a common problem encountered in modeling is avoided by checking the performance of the model in a “test dataset” during training. Based on the performance of the model in the test set the model is rendered reliable for generalization. The next step is to check the predictive ability of the model in a “validation set” (a dataset not seen by the model

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GROUTING AND DEEP MIXING 2012 223

optimal and effective utilization of binders, soils, equipments, methods and the environmental conditions involved. The study reported here aims to investigate the relationships between the chosen set of variables mentioned above which are expected to have an effect on the final strength gain. The main objective is to explore the feasibility of using ANNs to develop predictive models in deep mix technology for soils stabilised with specific binders. ANNs SYSTEM AND DATASET DESCRIPTION In this work, the ANN model is developed using computer-based software package namely Model Manager (Sourmail, 2004), a feed-forward error back propagation non-linear data modeling tool that combines the Bayesian Probability theory with neural networks. The underlying Bayesian framework is described elsewhere (Mackay, 2003). The predictions of these Bayesian models are associated with error bars that give a limit to the reliability of the predictions. The model consists of a number of processing elements arranged in layers: an input layer, an output layer and one intermediate layer called the hidden layer as shown in Figure 1. The general ANN model can be given by the following equation:

)2.....(..........).........tanh(

)1......(..............................

)1()1(

)2()2(

ijjj

iji

ii

i

xwh

hwy

Each unit/input (xj) in the input layer which has an individual influence on the output are multiplied by adjustable connection weights (wij) and summed with a constant (θij) to form a combined input. This combined input serves as the input to the hidden layer. The units in the hidden layers are normally associated with non-linear transfer functions such as sigmoidal function, or in this case, hyperbolic tangent function (2) which transfers this combined input to the output layer in order to produce the final output (y) as in (1). wij

(1)) are defined as weights that determine the strength of input units and θij(1) is defined

as the bias. Similarly, wi(2) and θ(2) are second set of weights and biases. The number of

hidden nodes in the hidden layer and the connection weights are what we care to optimize during the process. The error (discrepancy between the target and the network output) calculated at output layer is propagated backward to adjust the weights during an iterative “training process”. The main objective of the training process is to limit this error which is minimized by successive iterations of the training data. This involves the adjustment of the connection weights between the layers until the network correctly reproduces the target values with the least error. Over-training or over-learning which is a common problem encountered in modeling is avoided by checking the performance of the model in a “test dataset” during training. Based on the performance of the model in the test set the model is rendered reliable for generalization. The next step is to check the predictive ability of the model in a “validation set” (a dataset not seen by the model

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GROUTING AND DEEP MIXING 2012 223

122A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Cyan_08/07/2012_05:31:52 122A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Magenta_08/07/2012_05:31:52 122A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Yellow_08/07/2012_05:31:52 122A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Black_08/07/2012_05:31:52

Grouting and Deep Mixing 2012

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before) and the sensitivity of the model so that the model can be used confidently for predictive purposes.

In this work, the dataset used to develop the ANN models consisted of 220 data cases of cement-stabilized soils collected from a number of cement deep mixing projects: 129 data cases reported by Kawasaki et al. (1981); 19 reported by EuroSoilStab (2000); 15 reported by Ahnberg (2006); 33 by Hernandez-Martinez (2006) and 24 by Jegandan (2010).

UCSOrganic matter Content (%)

Soil water content (%)

Sand content (%)

Binder Dosage ( kg/m3)

Curing time (days)

Curing temperature (ºC)

Silt Content (%)

Clay Content (%)

Input layer Hidden layer

Output layer

FIG. 1. A Typical Structure of Artificial Neural Network

A sample of the dataset is shown in Table 1. Each case in the dataset corresponded to a soil-mixed sample prepared by wet mixing the soil with Portland cement in the laboratory. The soil type within the dataset varied from sand to silt to inorganic and organic clays and to highly organic soils such as peat. For each soil mixed sample, variables believed to affect the UCS such as initial soil water content, sand, silt and clay content, organic matter content, binder dosage in the mix, curing time and temperature has been collected. The dataset was examined by producing individual plots of UCS as a function of individual variables in the dataset. Although the individual plots showed a great deal of scatter, there was an indication for each soil type that the UCS increased with cement content (e.g. Figure 2), age, sand content and temperature but decreased with water content and organic matter content.

A similar dataset but comprising of ground granulated blast furnace slag-stabilized soils has been discussed in Shrestha and Al-Tabbaa (2011). Figure 2 shows the 28-day UCS variation of the soils with cement content. It can be seen that an increase in the amount of cement added increased the strength of the soil-cement for each type of soils. The increase of strength for organic soils (e.g. Peat) due to increased amount of cement

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224 GROUTING AND DEEP MIXING 2012

before) and the sensitivity of the model so that the model can be used confidently for predictive purposes.

In this work, the dataset used to develop the ANN models consisted of 220 data cases of cement-stabilized soils collected from a number of cement deep mixing projects: 129 data cases reported by Kawasaki et al. (1981); 19 reported by EuroSoilStab (2000); 15 reported by Ahnberg (2006); 33 by Hernandez-Martinez (2006) and 24 by Jegandan (2010).

UCSOrganic matter Content (%)

Soil water content (%)

Sand content (%)

Binder Dosage ( kg/m3)

Curing time (days)

Curing temperature (ºC)

Silt Content (%)

Clay Content (%)

Input layer Hidden layer

Output layer

FIG. 1. A Typical Structure of Artificial Neural Network

A sample of the dataset is shown in Table 1. Each case in the dataset corresponded to a soil-mixed sample prepared by wet mixing the soil with Portland cement in the laboratory. The soil type within the dataset varied from sand to silt to inorganic and organic clays and to highly organic soils such as peat. For each soil mixed sample, variables believed to affect the UCS such as initial soil water content, sand, silt and clay content, organic matter content, binder dosage in the mix, curing time and temperature has been collected. The dataset was examined by producing individual plots of UCS as a function of individual variables in the dataset. Although the individual plots showed a great deal of scatter, there was an indication for each soil type that the UCS increased with cement content (e.g. Figure 2), age, sand content and temperature but decreased with water content and organic matter content.

A similar dataset but comprising of ground granulated blast furnace slag-stabilized soils has been discussed in Shrestha and Al-Tabbaa (2011). Figure 2 shows the 28-day UCS variation of the soils with cement content. It can be seen that an increase in the amount of cement added increased the strength of the soil-cement for each type of soils. The increase of strength for organic soils (e.g. Peat) due to increased amount of cement

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224 GROUTING AND DEEP MIXING 2012

before) and the sensitivity of the model so that the model can be used confidently for predictive purposes.

In this work, the dataset used to develop the ANN models consisted of 220 data cases of cement-stabilized soils collected from a number of cement deep mixing projects: 129 data cases reported by Kawasaki et al. (1981); 19 reported by EuroSoilStab (2000); 15 reported by Ahnberg (2006); 33 by Hernandez-Martinez (2006) and 24 by Jegandan (2010).

UCSOrganic matter Content (%)

Soil water content (%)

Sand content (%)

Binder Dosage ( kg/m3)

Curing time (days)

Curing temperature (ºC)

Silt Content (%)

Clay Content (%)

Input layer Hidden layer

Output layer

FIG. 1. A Typical Structure of Artificial Neural Network

A sample of the dataset is shown in Table 1. Each case in the dataset corresponded to a soil-mixed sample prepared by wet mixing the soil with Portland cement in the laboratory. The soil type within the dataset varied from sand to silt to inorganic and organic clays and to highly organic soils such as peat. For each soil mixed sample, variables believed to affect the UCS such as initial soil water content, sand, silt and clay content, organic matter content, binder dosage in the mix, curing time and temperature has been collected. The dataset was examined by producing individual plots of UCS as a function of individual variables in the dataset. Although the individual plots showed a great deal of scatter, there was an indication for each soil type that the UCS increased with cement content (e.g. Figure 2), age, sand content and temperature but decreased with water content and organic matter content.

A similar dataset but comprising of ground granulated blast furnace slag-stabilized soils has been discussed in Shrestha and Al-Tabbaa (2011). Figure 2 shows the 28-day UCS variation of the soils with cement content. It can be seen that an increase in the amount of cement added increased the strength of the soil-cement for each type of soils. The increase of strength for organic soils (e.g. Peat) due to increased amount of cement

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224 GROUTING AND DEEP MIXING 2012

before) and the sensitivity of the model so that the model can be used confidently for predictive purposes.

In this work, the dataset used to develop the ANN models consisted of 220 data cases of cement-stabilized soils collected from a number of cement deep mixing projects: 129 data cases reported by Kawasaki et al. (1981); 19 reported by EuroSoilStab (2000); 15 reported by Ahnberg (2006); 33 by Hernandez-Martinez (2006) and 24 by Jegandan (2010).

UCSOrganic matter Content (%)

Soil water content (%)

Sand content (%)

Binder Dosage ( kg/m3)

Curing time (days)

Curing temperature (ºC)

Silt Content (%)

Clay Content (%)

Input layer Hidden layer

Output layer

FIG. 1. A Typical Structure of Artificial Neural Network

A sample of the dataset is shown in Table 1. Each case in the dataset corresponded to a soil-mixed sample prepared by wet mixing the soil with Portland cement in the laboratory. The soil type within the dataset varied from sand to silt to inorganic and organic clays and to highly organic soils such as peat. For each soil mixed sample, variables believed to affect the UCS such as initial soil water content, sand, silt and clay content, organic matter content, binder dosage in the mix, curing time and temperature has been collected. The dataset was examined by producing individual plots of UCS as a function of individual variables in the dataset. Although the individual plots showed a great deal of scatter, there was an indication for each soil type that the UCS increased with cement content (e.g. Figure 2), age, sand content and temperature but decreased with water content and organic matter content.

A similar dataset but comprising of ground granulated blast furnace slag-stabilized soils has been discussed in Shrestha and Al-Tabbaa (2011). Figure 2 shows the 28-day UCS variation of the soils with cement content. It can be seen that an increase in the amount of cement added increased the strength of the soil-cement for each type of soils. The increase of strength for organic soils (e.g. Peat) due to increased amount of cement

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Grouting and Deep Mixing 2012

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is however minor in comparison with inorganic soils (e.g. Clay). Such plots which show correlations between UCS and the individual variables were produced for each individual variable before predictive ANN models were developed in the Model Manager.

The input variables are initial soil water content, sand, silt and clay content, organic matter content, binder content in the mixes, age and temperature of curing and the output variable is the UCS of the soil mixed samples in MPa. The range, mean and standard deviation of the input and output data are listed in Table 2.

FIG. 2. Effect of binder dosage on 28-day UCS of cement stabilised soils

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Table. 1 Sample dataset

Soil type Soil water content(% )

Sand content

(% )

Silt content

(% )

Clay content

(% )

Organic matter

(% )

Binder Dosage

(kg/m3)

Age(days) Curing temperature

(°C)

UCS (Mpa)

Reference

clay 50 0 60 40 0 175 7 20 0.63 Jeganadan,2010clay 50 0 60 40 0 175 28 20 0.98 Jeganadan,2010clay 50 0 60 40 0 175 90 20 1.61 Jeganadan,2010silt 20 0 100 0 0 175 7 20 1.43 Jeganadan,2010silt 20 0 100 0 0 175 28 20 2.23 Jeganadan,2010silt 20 0 100 0 0 175 90 20 2.31 Jeganadan,2010

sand 10 90 0 10 0 350 7 20 5.29 Jeganadan,2010sand 10 90 0 10 0 350 28 20 7.09 Jeganadan,2010sand 10 90 0 10 0 350 90 20 9.69 Jeganadan,2010gytjja 370 0 0 36 10.8 100 7 20 0.01 Hernandez-Martinez, 2006gytjja 370 0 0 36 10.8 200 28 20 0.19 Hernandez-Martinez, 2006gytjja 370 0 0 36 10.8 200 91 20 0.30 Hernandez-Martinez, 2006peat 1600 0 0 0 97 100 91 20 0.17 Hernandez-Martinez, 2006peat 1600 0 0 0 97 200 91 20 0.30 Hernandez-Martinez, 2006peat 1600 0 0 0 97 300 91 20 0.56 Hernandez-Martinez, 2006clay 99.6 1 41 58 0.39 200 7 20 3.67 Kawasaki et al, 1981clay 99.6 1 41 58 0.39 200 28 20 4.88 Kawasaki et al, 1981clay 99.6 1 41 58 0.39 200 60 20 5.70 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 100 28 20 0.68 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 200 28 20 1.67 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 300 28 20 1.97 Kawasaki et al, 1981

Table. 2 Summary of the dataset

Variable name Min Max Mean S.D.

Soil Water Content (%) 10 1600 222.25 364.13

Sand Content (%) 0 90 10.07 17.32

Silt Content (%) 0 100 38.93 28.43

Clay Content (%) 0 80 28.28 21.99

Organic M atter Content (%) 0 97 16.64 34.67

Binder Dosage(kg/m3) 100 500 206.38 95.61

Age(Days) 7 800 55.62 106.65

Curing Temperature (°C) 8 21 19.37 2.99

UCS (M Pa) 0.08 10.35 2.06 2.05

RESULTS AND DISCUSSION

Model Development and Validation

The dataset was divided into training and test set. A total of 125 models were trained. The trained models were ranked on the basis of their performance on the test set. A committee of the models was then formed based on the minimum combined test error. Figure 3(a) shows that the combined test error for a committee of nine models is the minimum making this committee the most favorable. In Figure 3(b) the committee model predictions for the whole data are compared against the actual data and in Figure

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Table. 1 Sample dataset

Soil type Soil water content(% )

Sand content

(% )

Silt content

(% )

Clay content

(% )

Organic matter

(% )

Binder Dosage

(kg/m3)

Age(days) Curing temperature

(°C)

UCS (Mpa)

Reference

clay 50 0 60 40 0 175 7 20 0.63 Jeganadan,2010clay 50 0 60 40 0 175 28 20 0.98 Jeganadan,2010clay 50 0 60 40 0 175 90 20 1.61 Jeganadan,2010silt 20 0 100 0 0 175 7 20 1.43 Jeganadan,2010silt 20 0 100 0 0 175 28 20 2.23 Jeganadan,2010silt 20 0 100 0 0 175 90 20 2.31 Jeganadan,2010

sand 10 90 0 10 0 350 7 20 5.29 Jeganadan,2010sand 10 90 0 10 0 350 28 20 7.09 Jeganadan,2010sand 10 90 0 10 0 350 90 20 9.69 Jeganadan,2010gytjja 370 0 0 36 10.8 100 7 20 0.01 Hernandez-Martinez, 2006gytjja 370 0 0 36 10.8 200 28 20 0.19 Hernandez-Martinez, 2006gytjja 370 0 0 36 10.8 200 91 20 0.30 Hernandez-Martinez, 2006peat 1600 0 0 0 97 100 91 20 0.17 Hernandez-Martinez, 2006peat 1600 0 0 0 97 200 91 20 0.30 Hernandez-Martinez, 2006peat 1600 0 0 0 97 300 91 20 0.56 Hernandez-Martinez, 2006clay 99.6 1 41 58 0.39 200 7 20 3.67 Kawasaki et al, 1981clay 99.6 1 41 58 0.39 200 28 20 4.88 Kawasaki et al, 1981clay 99.6 1 41 58 0.39 200 60 20 5.70 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 100 28 20 0.68 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 200 28 20 1.67 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 300 28 20 1.97 Kawasaki et al, 1981

Table. 2 Summary of the dataset

Variable name Min Max Mean S.D.

Soil Water Content (%) 10 1600 222.25 364.13

Sand Content (%) 0 90 10.07 17.32

Silt Content (%) 0 100 38.93 28.43

Clay Content (%) 0 80 28.28 21.99

Organic M atter Content (%) 0 97 16.64 34.67

Binder Dosage(kg/m3) 100 500 206.38 95.61

Age(Days) 7 800 55.62 106.65

Curing Temperature (°C) 8 21 19.37 2.99

UCS (M Pa) 0.08 10.35 2.06 2.05

RESULTS AND DISCUSSION

Model Development and Validation

The dataset was divided into training and test set. A total of 125 models were trained. The trained models were ranked on the basis of their performance on the test set. A committee of the models was then formed based on the minimum combined test error. Figure 3(a) shows that the combined test error for a committee of nine models is the minimum making this committee the most favorable. In Figure 3(b) the committee model predictions for the whole data are compared against the actual data and in Figure

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Table. 1 Sample dataset

Soil type Soil water content(% )

Sand content

(% )

Silt content

(% )

Clay content

(% )

Organic matter

(% )

Binder Dosage

(kg/m3)

Age(days) Curing temperature

(°C)

UCS (Mpa)

Reference

clay 50 0 60 40 0 175 7 20 0.63 Jeganadan,2010clay 50 0 60 40 0 175 28 20 0.98 Jeganadan,2010clay 50 0 60 40 0 175 90 20 1.61 Jeganadan,2010silt 20 0 100 0 0 175 7 20 1.43 Jeganadan,2010silt 20 0 100 0 0 175 28 20 2.23 Jeganadan,2010silt 20 0 100 0 0 175 90 20 2.31 Jeganadan,2010

sand 10 90 0 10 0 350 7 20 5.29 Jeganadan,2010sand 10 90 0 10 0 350 28 20 7.09 Jeganadan,2010sand 10 90 0 10 0 350 90 20 9.69 Jeganadan,2010gytjja 370 0 0 36 10.8 100 7 20 0.01 Hernandez-Martinez, 2006gytjja 370 0 0 36 10.8 200 28 20 0.19 Hernandez-Martinez, 2006gytjja 370 0 0 36 10.8 200 91 20 0.30 Hernandez-Martinez, 2006peat 1600 0 0 0 97 100 91 20 0.17 Hernandez-Martinez, 2006peat 1600 0 0 0 97 200 91 20 0.30 Hernandez-Martinez, 2006peat 1600 0 0 0 97 300 91 20 0.56 Hernandez-Martinez, 2006clay 99.6 1 41 58 0.39 200 7 20 3.67 Kawasaki et al, 1981clay 99.6 1 41 58 0.39 200 28 20 4.88 Kawasaki et al, 1981clay 99.6 1 41 58 0.39 200 60 20 5.70 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 100 28 20 0.68 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 200 28 20 1.67 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 300 28 20 1.97 Kawasaki et al, 1981

Table. 2 Summary of the dataset

Variable name Min Max Mean S.D.

Soil Water Content (%) 10 1600 222.25 364.13

Sand Content (%) 0 90 10.07 17.32

Silt Content (%) 0 100 38.93 28.43

Clay Content (%) 0 80 28.28 21.99

Organic M atter Content (%) 0 97 16.64 34.67

Binder Dosage(kg/m3) 100 500 206.38 95.61

Age(Days) 7 800 55.62 106.65

Curing Temperature (°C) 8 21 19.37 2.99

UCS (M Pa) 0.08 10.35 2.06 2.05

RESULTS AND DISCUSSION

Model Development and Validation

The dataset was divided into training and test set. A total of 125 models were trained. The trained models were ranked on the basis of their performance on the test set. A committee of the models was then formed based on the minimum combined test error. Figure 3(a) shows that the combined test error for a committee of nine models is the minimum making this committee the most favorable. In Figure 3(b) the committee model predictions for the whole data are compared against the actual data and in Figure

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Table. 1 Sample dataset

Soil type Soil water content(% )

Sand content

(% )

Silt content

(% )

Clay content

(% )

Organic matter

(% )

Binder Dosage

(kg/m3)

Age(days) Curing temperature

(°C)

UCS (Mpa)

Reference

clay 50 0 60 40 0 175 7 20 0.63 Jeganadan,2010clay 50 0 60 40 0 175 28 20 0.98 Jeganadan,2010clay 50 0 60 40 0 175 90 20 1.61 Jeganadan,2010silt 20 0 100 0 0 175 7 20 1.43 Jeganadan,2010silt 20 0 100 0 0 175 28 20 2.23 Jeganadan,2010silt 20 0 100 0 0 175 90 20 2.31 Jeganadan,2010

sand 10 90 0 10 0 350 7 20 5.29 Jeganadan,2010sand 10 90 0 10 0 350 28 20 7.09 Jeganadan,2010sand 10 90 0 10 0 350 90 20 9.69 Jeganadan,2010gytjja 370 0 0 36 10.8 100 7 20 0.01 Hernandez-Martinez, 2006gytjja 370 0 0 36 10.8 200 28 20 0.19 Hernandez-Martinez, 2006gytjja 370 0 0 36 10.8 200 91 20 0.30 Hernandez-Martinez, 2006peat 1600 0 0 0 97 100 91 20 0.17 Hernandez-Martinez, 2006peat 1600 0 0 0 97 200 91 20 0.30 Hernandez-Martinez, 2006peat 1600 0 0 0 97 300 91 20 0.56 Hernandez-Martinez, 2006clay 99.6 1 41 58 0.39 200 7 20 3.67 Kawasaki et al, 1981clay 99.6 1 41 58 0.39 200 28 20 4.88 Kawasaki et al, 1981clay 99.6 1 41 58 0.39 200 60 20 5.70 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 100 28 20 0.68 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 200 28 20 1.67 Kawasaki et al, 1981clay 55.8 2 62 36 0.33 300 28 20 1.97 Kawasaki et al, 1981

Table. 2 Summary of the dataset

Variable name Min Max Mean S.D.

Soil Water Content (%) 10 1600 222.25 364.13

Sand Content (%) 0 90 10.07 17.32

Silt Content (%) 0 100 38.93 28.43

Clay Content (%) 0 80 28.28 21.99

Organic M atter Content (%) 0 97 16.64 34.67

Binder Dosage(kg/m3) 100 500 206.38 95.61

Age(Days) 7 800 55.62 106.65

Curing Temperature (°C) 8 21 19.37 2.99

UCS (M Pa) 0.08 10.35 2.06 2.05

RESULTS AND DISCUSSION

Model Development and Validation

The dataset was divided into training and test set. A total of 125 models were trained. The trained models were ranked on the basis of their performance on the test set. A committee of the models was then formed based on the minimum combined test error. Figure 3(a) shows that the combined test error for a committee of nine models is the minimum making this committee the most favorable. In Figure 3(b) the committee model predictions for the whole data are compared against the actual data and in Figure

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Grouting and Deep Mixing 2012

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FIG. 3. (a) Test error as a function of number of models used in the committee (b) Predicted UCS values against Measured for entire dataset (c) Predicted UCS

against Actual for an unseen dataset

Effectiveness of the Model

The effectiveness and applicability of a predictive model is determined by examining how well the predictions of the model compare with the available geotechnical knowledge and with the experimental data. With the model, it was found that UCS is

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3(c) the predictions of the committee model for an entirely new set of data (validation set) not seen by the model before is shown. Figures 3 (b) and (c) indicate that the model performed well both in the whole dataset as well as the validation set.

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FIG. 3. (a) Test error as a function of number of models used in the committee (b) Predicted UCS values against Measured for entire dataset (c) Predicted UCS

against Actual for an unseen dataset

Effectiveness of the Model

The effectiveness and applicability of a predictive model is determined by examining how well the predictions of the model compare with the available geotechnical knowledge and with the experimental data. With the model, it was found that UCS is

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3(c) the predictions of the committee model for an entirely new set of data (validation set) not seen by the model before is shown. Figures 3 (b) and (c) indicate that the model performed well both in the whole dataset as well as the validation set.

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FIG. 3. (a) Test error as a function of number of models used in the committee (b) Predicted UCS values against Measured for entire dataset (c) Predicted UCS

against Actual for an unseen dataset

Effectiveness of the Model

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3(c) the predictions of the committee model for an entirely new set of data (validation set) not seen by the model before is shown. Figures 3 (b) and (c) indicate that the model performed well both in the whole dataset as well as the validation set.

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FIG. 3. (a) Test error as a function of number of models used in the committee (b) Predicted UCS values against Measured for entire dataset (c) Predicted UCS

against Actual for an unseen dataset

Effectiveness of the Model

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found to be sensitive to factors such as binder dosage, age, curing temperature and natural soil water content as expected. Figure 4 (a) shows the variation of 28-day UCS of Tokyo1 clay (a case from the dataset) with binder dosage as predicted by the model.

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Actual 28‐day UCS;Tokyo1 clay

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FIG. 4. Predicted and Measured UCS variation for Tokyo 1 clay (a) at 28 days with binder dosage and (b) with age

Similarly, Figure 4 (b) shows the variation of UCS for the same soil with age for a binder dosage of 200 kg/m3. It can be seen that the rectangular data points (in both cases) which are the actual measured values for the soil are in reasonable agreement with the model predicted values (diamonds) which confirms the ability of the model to

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found to be sensitive to factors such as binder dosage, age, curing temperature and natural soil water content as expected. Figure 4 (a) shows the variation of 28-day UCS of Tokyo1 clay (a case from the dataset) with binder dosage as predicted by the model.

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3

4

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6

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100 150 200 250 300 350 400 450 500

28‐day UCS(MPa)

Cement dosage(kg/m3)

Predicted 28‐day UCS; Tokyo1 clay

Actual 28‐day UCS;Tokyo1 clay

(a)

0

2

4

6

8

10

12

14

0 30 60 90 120 150 180 210 240 270 300

UCS(MPa)

Age(days)

Predicted UCS

Actual UCS

(b)

FIG. 4. Predicted and Measured UCS variation for Tokyo 1 clay (a) at 28 days with binder dosage and (b) with age

Similarly, Figure 4 (b) shows the variation of UCS for the same soil with age for a binder dosage of 200 kg/m3. It can be seen that the rectangular data points (in both cases) which are the actual measured values for the soil are in reasonable agreement with the model predicted values (diamonds) which confirms the ability of the model to

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found to be sensitive to factors such as binder dosage, age, curing temperature and natural soil water content as expected. Figure 4 (a) shows the variation of 28-day UCS of Tokyo1 clay (a case from the dataset) with binder dosage as predicted by the model.

0

1

2

3

4

5

6

7

100 150 200 250 300 350 400 450 500

28‐day UCS(MPa)

Cement dosage(kg/m3)

Predicted 28‐day UCS; Tokyo1 clay

Actual 28‐day UCS;Tokyo1 clay

(a)

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2

4

6

8

10

12

14

0 30 60 90 120 150 180 210 240 270 300

UCS(MPa)

Age(days)

Predicted UCS

Actual UCS

(b)

FIG. 4. Predicted and Measured UCS variation for Tokyo 1 clay (a) at 28 days with binder dosage and (b) with age

Similarly, Figure 4 (b) shows the variation of UCS for the same soil with age for a binder dosage of 200 kg/m3. It can be seen that the rectangular data points (in both cases) which are the actual measured values for the soil are in reasonable agreement with the model predicted values (diamonds) which confirms the ability of the model to

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228 GROUTING AND DEEP MIXING 2012

found to be sensitive to factors such as binder dosage, age, curing temperature and natural soil water content as expected. Figure 4 (a) shows the variation of 28-day UCS of Tokyo1 clay (a case from the dataset) with binder dosage as predicted by the model.

0

1

2

3

4

5

6

7

100 150 200 250 300 350 400 450 500

28‐day UCS(MPa)

Cement dosage(kg/m3)

Predicted 28‐day UCS; Tokyo1 clay

Actual 28‐day UCS;Tokyo1 clay

(a)

0

2

4

6

8

10

12

14

0 30 60 90 120 150 180 210 240 270 300

UCS(MPa)

Age(days)

Predicted UCS

Actual UCS

(b)

FIG. 4. Predicted and Measured UCS variation for Tokyo 1 clay (a) at 28 days with binder dosage and (b) with age

Similarly, Figure 4 (b) shows the variation of UCS for the same soil with age for a binder dosage of 200 kg/m3. It can be seen that the rectangular data points (in both cases) which are the actual measured values for the soil are in reasonable agreement with the model predicted values (diamonds) which confirms the ability of the model to

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124B_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Cyan_08/07/2012_05:31:52124B_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Magenta_08/07/2012_05:31:52124B_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Yellow_08/07/2012_05:31:52124B_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Black_08/07/2012_05:31:52

Grouting and Deep Mixing 2012

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reflect the effect of the input parameters on the UCS. The error bars in Figures 4 indicate the magnitude of modeling uncertainty, which is

the standard deviation of the values predicted by the models in the committee and varies depending upon the availability of data in the input space. The uncertainties associated with the predictions are higher in the range where input data are sparse. It can be seen in Figure 4 (a) and (b) that the predictions are more certain in the range where most of the cement dosage values in the dataset concentrated between 100 to 300 kg /m3 and where majority of the curing times ranged from 7 to 90 days respectively.

CONCLUSIONS

Work to date has shown that a large amount of data/information on measurements of UCS values and the factors affecting it are available in the published literatures and elsewhere in the form of project technical reports. This paper showed that bringing data together from these large numbers of deep mixing projects has enabled the observation of useful correlations between the factors influencing the engineering properties such as UCS. Further, the effectiveness of the concept of neural network in developing predictive models was discussed briefly in the paper implying the applicability of this concept to deep mix technology. Results showed that reasonable predictions have been made which were in good agreement with the known trends and that emulated by the actual measured values.

ACKNOWLEDGEMENTS

The authors would like to thank all the members of the International Deep Mixing Community who have provided data from deep mixing projects. The authors also thank Dr. Arul Britto for discussions. Thanks go to Professor Harry Bhadeshia at Cambridge University Department of Material Science and Dr. Richard Kemp for their advice on using and interpreting the Model Manager Software. Thanks also go to the Cambridge Overseas Trust for financial support provided to the first author during her PhD studies.

REFERENCES

Ahnberg, H. (2006). "Strength of stabilised soils- A laboratory study on clays and organic soils stabilised with different types of binders." Ph.D thesis, Lund University, Sweden.

Coastal Development Institute of Technology (CDIT). (2001). "The deep mixing method: Principle, design and construction." A.A. Balkema, The Netherlands.

Endo, M. (1976). "Recent development in dredged material stabilization and deep chemical mixing in Japan." Soils and Site Improvement- Lifelong Learning Seminar, Berkeley, California.

EuroSoilStab. (2000). "Design guide soft soil stabilisation. CT97-0351. Project no: BE 96-3177." European Commission.

Goh, A.T.C. (1995). "Modelling soil correlations using neural networks." Journal of Computing in Civil Engineering 9 (4): 275-278.

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reflect the effect of the input parameters on the UCS. The error bars in Figures 4 indicate the magnitude of modeling uncertainty, which is

the standard deviation of the values predicted by the models in the committee and varies depending upon the availability of data in the input space. The uncertainties associated with the predictions are higher in the range where input data are sparse. It can be seen in Figure 4 (a) and (b) that the predictions are more certain in the range where most of the cement dosage values in the dataset concentrated between 100 to 300 kg /m3 and where majority of the curing times ranged from 7 to 90 days respectively.

CONCLUSIONS

Work to date has shown that a large amount of data/information on measurements of UCS values and the factors affecting it are available in the published literatures and elsewhere in the form of project technical reports. This paper showed that bringing data together from these large numbers of deep mixing projects has enabled the observation of useful correlations between the factors influencing the engineering properties such as UCS. Further, the effectiveness of the concept of neural network in developing predictive models was discussed briefly in the paper implying the applicability of this concept to deep mix technology. Results showed that reasonable predictions have been made which were in good agreement with the known trends and that emulated by the actual measured values.

ACKNOWLEDGEMENTS

The authors would like to thank all the members of the International Deep Mixing Community who have provided data from deep mixing projects. The authors also thank Dr. Arul Britto for discussions. Thanks go to Professor Harry Bhadeshia at Cambridge University Department of Material Science and Dr. Richard Kemp for their advice on using and interpreting the Model Manager Software. Thanks also go to the Cambridge Overseas Trust for financial support provided to the first author during her PhD studies.

REFERENCES

Ahnberg, H. (2006). "Strength of stabilised soils- A laboratory study on clays and organic soils stabilised with different types of binders." Ph.D thesis, Lund University, Sweden.

Coastal Development Institute of Technology (CDIT). (2001). "The deep mixing method: Principle, design and construction." A.A. Balkema, The Netherlands.

Endo, M. (1976). "Recent development in dredged material stabilization and deep chemical mixing in Japan." Soils and Site Improvement- Lifelong Learning Seminar, Berkeley, California.

EuroSoilStab. (2000). "Design guide soft soil stabilisation. CT97-0351. Project no: BE 96-3177." European Commission.

Goh, A.T.C. (1995). "Modelling soil correlations using neural networks." Journal of Computing in Civil Engineering 9 (4): 275-278.

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reflect the effect of the input parameters on the UCS. The error bars in Figures 4 indicate the magnitude of modeling uncertainty, which is

the standard deviation of the values predicted by the models in the committee and varies depending upon the availability of data in the input space. The uncertainties associated with the predictions are higher in the range where input data are sparse. It can be seen in Figure 4 (a) and (b) that the predictions are more certain in the range where most of the cement dosage values in the dataset concentrated between 100 to 300 kg /m3 and where majority of the curing times ranged from 7 to 90 days respectively.

CONCLUSIONS

Work to date has shown that a large amount of data/information on measurements of UCS values and the factors affecting it are available in the published literatures and elsewhere in the form of project technical reports. This paper showed that bringing data together from these large numbers of deep mixing projects has enabled the observation of useful correlations between the factors influencing the engineering properties such as UCS. Further, the effectiveness of the concept of neural network in developing predictive models was discussed briefly in the paper implying the applicability of this concept to deep mix technology. Results showed that reasonable predictions have been made which were in good agreement with the known trends and that emulated by the actual measured values.

ACKNOWLEDGEMENTS

The authors would like to thank all the members of the International Deep Mixing Community who have provided data from deep mixing projects. The authors also thank Dr. Arul Britto for discussions. Thanks go to Professor Harry Bhadeshia at Cambridge University Department of Material Science and Dr. Richard Kemp for their advice on using and interpreting the Model Manager Software. Thanks also go to the Cambridge Overseas Trust for financial support provided to the first author during her PhD studies.

REFERENCES

Ahnberg, H. (2006). "Strength of stabilised soils- A laboratory study on clays and organic soils stabilised with different types of binders." Ph.D thesis, Lund University, Sweden.

Coastal Development Institute of Technology (CDIT). (2001). "The deep mixing method: Principle, design and construction." A.A. Balkema, The Netherlands.

Endo, M. (1976). "Recent development in dredged material stabilization and deep chemical mixing in Japan." Soils and Site Improvement- Lifelong Learning Seminar, Berkeley, California.

EuroSoilStab. (2000). "Design guide soft soil stabilisation. CT97-0351. Project no: BE 96-3177." European Commission.

Goh, A.T.C. (1995). "Modelling soil correlations using neural networks." Journal of Computing in Civil Engineering 9 (4): 275-278.

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reflect the effect of the input parameters on the UCS. The error bars in Figures 4 indicate the magnitude of modeling uncertainty, which is

the standard deviation of the values predicted by the models in the committee and varies depending upon the availability of data in the input space. The uncertainties associated with the predictions are higher in the range where input data are sparse. It can be seen in Figure 4 (a) and (b) that the predictions are more certain in the range where most of the cement dosage values in the dataset concentrated between 100 to 300 kg /m3 and where majority of the curing times ranged from 7 to 90 days respectively.

CONCLUSIONS

Work to date has shown that a large amount of data/information on measurements of UCS values and the factors affecting it are available in the published literatures and elsewhere in the form of project technical reports. This paper showed that bringing data together from these large numbers of deep mixing projects has enabled the observation of useful correlations between the factors influencing the engineering properties such as UCS. Further, the effectiveness of the concept of neural network in developing predictive models was discussed briefly in the paper implying the applicability of this concept to deep mix technology. Results showed that reasonable predictions have been made which were in good agreement with the known trends and that emulated by the actual measured values.

ACKNOWLEDGEMENTS

The authors would like to thank all the members of the International Deep Mixing Community who have provided data from deep mixing projects. The authors also thank Dr. Arul Britto for discussions. Thanks go to Professor Harry Bhadeshia at Cambridge University Department of Material Science and Dr. Richard Kemp for their advice on using and interpreting the Model Manager Software. Thanks also go to the Cambridge Overseas Trust for financial support provided to the first author during her PhD studies.

REFERENCES

Ahnberg, H. (2006). "Strength of stabilised soils- A laboratory study on clays and organic soils stabilised with different types of binders." Ph.D thesis, Lund University, Sweden.

Coastal Development Institute of Technology (CDIT). (2001). "The deep mixing method: Principle, design and construction." A.A. Balkema, The Netherlands.

Endo, M. (1976). "Recent development in dredged material stabilization and deep chemical mixing in Japan." Soils and Site Improvement- Lifelong Learning Seminar, Berkeley, California.

EuroSoilStab. (2000). "Design guide soft soil stabilisation. CT97-0351. Project no: BE 96-3177." European Commission.

Goh, A.T.C. (1995). "Modelling soil correlations using neural networks." Journal of Computing in Civil Engineering 9 (4): 275-278.

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125A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Cyan_08/07/2012_05:31:52 125A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Magenta_08/07/2012_05:31:52 125A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Yellow_08/07/2012_05:31:52 125A_PB_4out_Same_50835_ASCE_Vol_01_Final.job_Process Black_08/07/2012_05:31:52

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Hernandez-Martinez, F. (2006). "Ground improvement of organic soils using deep soil mixing." Ph.D thesis, University of Cambridge, Cambridge, UK

Jegandan, S. (2010). "Deep soil mixing with conventional and novel binders." Ph.D thesis, University of Cambridge, Cambridge, UK.

Kawasaki, T., Niina, A., Suzuki, Y., Saito, S., and Babasaki, R. (1981). "On the deep mixing chemical mixing method using cement hardening agent- Takenaka technical research report." Report No. 26: 13-42.

Kitazume, M. (2005). "State of practice report- Field and laboratory investigations, properties of binders and stabilized soil." DeepMixing’05- International Conference on Deep Mixing Best Practice and Recent Advances, Stockholm, Sweden: 660-684.

Lai, S., and Serra, M. (1997). "Concrete strength prediction by means of neural network." Construction and Building Materials 11 (2): 93-98.

MacKay, D. J. C. (2003). "Introduction to neural networks." Information Theory, Inference and Learning Algorithms, Cambridge University Press, Cambridge: 468-470.

Porbaha, A., S Shibuya, S., and Kishida, T. (2000). "State of the art in deep mixing technology. Part III: Geo-material characterization." Proceedings of the ICE - Ground Improvement 4 (3): 91-110.

Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). "Artificial neural network applications in geotechnical engineering." Australian Geomechanics 36 (1): 49-62.

Shahin, M. A., and Indraratna, B. (2006). "Modelling the mechanical behavior of railway ballast using artificial neural networks." Canadian Geotechnical Journal 43 (11): 1144-1152.

Shrestha, R, and Al-Tabbaa., A. (2011). "Introduction to the development of an information management system for soil mix technology using artificial neural networks." Geo-Frontiers 2011- Advances in Geotechnical Engineering, Geotechnical Special Publication 211, ASCE, Dallas: 816-825.

Sourmail, T., and Bhadeshia, H.K.D.H. (2005). "Neural networks." Introduction to Materials Modelling, Maney Publishing, UK: 153-165.

Sourmail, T. (2004). "NEUROMAT model manager manual." NEUROMAT Ltd, UK. Stegemann, J.A. (2001). "Neural network analysis of the effects of contaminants on

properties of cement pastes." Ph.D thesis, Imperial College of Science, Technology and Medicine, London, UK

Taki, O., and Yang, D. (1991). "Soil-cement mixed wall technique." Geotechnical Engineering Congress, Geotechnical Special Publication 27, ASCE, NY: 298-309.

Tarassenko, L. (1998). "Mathematical background for neural computing." A guide to neural computing applications, Arnold Publishers, UK.

Terashi, M. (1997). "Deep mixing method- Brief state-of-the-art." Proc., 14th Intl. Conference on Soil Mechanics and Foundation Engineering, Hamburg: 2475-2478

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Hernandez-Martinez, F. (2006). "Ground improvement of organic soils using deep soil mixing." Ph.D thesis, University of Cambridge, Cambridge, UK

Jegandan, S. (2010). "Deep soil mixing with conventional and novel binders." Ph.D thesis, University of Cambridge, Cambridge, UK.

Kawasaki, T., Niina, A., Suzuki, Y., Saito, S., and Babasaki, R. (1981). "On the deep mixing chemical mixing method using cement hardening agent- Takenaka technical research report." Report No. 26: 13-42.

Kitazume, M. (2005). "State of practice report- Field and laboratory investigations, properties of binders and stabilized soil." DeepMixing’05- International Conference on Deep Mixing Best Practice and Recent Advances, Stockholm, Sweden: 660-684.

Lai, S., and Serra, M. (1997). "Concrete strength prediction by means of neural network." Construction and Building Materials 11 (2): 93-98.

MacKay, D. J. C. (2003). "Introduction to neural networks." Information Theory, Inference and Learning Algorithms, Cambridge University Press, Cambridge: 468-470.

Porbaha, A., S Shibuya, S., and Kishida, T. (2000). "State of the art in deep mixing technology. Part III: Geo-material characterization." Proceedings of the ICE - Ground Improvement 4 (3): 91-110.

Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). "Artificial neural network applications in geotechnical engineering." Australian Geomechanics 36 (1): 49-62.

Shahin, M. A., and Indraratna, B. (2006). "Modelling the mechanical behavior of railway ballast using artificial neural networks." Canadian Geotechnical Journal 43 (11): 1144-1152.

Shrestha, R, and Al-Tabbaa., A. (2011). "Introduction to the development of an information management system for soil mix technology using artificial neural networks." Geo-Frontiers 2011- Advances in Geotechnical Engineering, Geotechnical Special Publication 211, ASCE, Dallas: 816-825.

Sourmail, T., and Bhadeshia, H.K.D.H. (2005). "Neural networks." Introduction to Materials Modelling, Maney Publishing, UK: 153-165.

Sourmail, T. (2004). "NEUROMAT model manager manual." NEUROMAT Ltd, UK. Stegemann, J.A. (2001). "Neural network analysis of the effects of contaminants on

properties of cement pastes." Ph.D thesis, Imperial College of Science, Technology and Medicine, London, UK

Taki, O., and Yang, D. (1991). "Soil-cement mixed wall technique." Geotechnical Engineering Congress, Geotechnical Special Publication 27, ASCE, NY: 298-309.

Tarassenko, L. (1998). "Mathematical background for neural computing." A guide to neural computing applications, Arnold Publishers, UK.

Terashi, M. (1997). "Deep mixing method- Brief state-of-the-art." Proc., 14th Intl. Conference on Soil Mechanics and Foundation Engineering, Hamburg: 2475-2478

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230 GROUTING AND DEEP MIXING 2012

Hernandez-Martinez, F. (2006). "Ground improvement of organic soils using deep soil mixing." Ph.D thesis, University of Cambridge, Cambridge, UK

Jegandan, S. (2010). "Deep soil mixing with conventional and novel binders." Ph.D thesis, University of Cambridge, Cambridge, UK.

Kawasaki, T., Niina, A., Suzuki, Y., Saito, S., and Babasaki, R. (1981). "On the deep mixing chemical mixing method using cement hardening agent- Takenaka technical research report." Report No. 26: 13-42.

Kitazume, M. (2005). "State of practice report- Field and laboratory investigations, properties of binders and stabilized soil." DeepMixing’05- International Conference on Deep Mixing Best Practice and Recent Advances, Stockholm, Sweden: 660-684.

Lai, S., and Serra, M. (1997). "Concrete strength prediction by means of neural network." Construction and Building Materials 11 (2): 93-98.

MacKay, D. J. C. (2003). "Introduction to neural networks." Information Theory, Inference and Learning Algorithms, Cambridge University Press, Cambridge: 468-470.

Porbaha, A., S Shibuya, S., and Kishida, T. (2000). "State of the art in deep mixing technology. Part III: Geo-material characterization." Proceedings of the ICE - Ground Improvement 4 (3): 91-110.

Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). "Artificial neural network applications in geotechnical engineering." Australian Geomechanics 36 (1): 49-62.

Shahin, M. A., and Indraratna, B. (2006). "Modelling the mechanical behavior of railway ballast using artificial neural networks." Canadian Geotechnical Journal 43 (11): 1144-1152.

Shrestha, R, and Al-Tabbaa., A. (2011). "Introduction to the development of an information management system for soil mix technology using artificial neural networks." Geo-Frontiers 2011- Advances in Geotechnical Engineering, Geotechnical Special Publication 211, ASCE, Dallas: 816-825.

Sourmail, T., and Bhadeshia, H.K.D.H. (2005). "Neural networks." Introduction to Materials Modelling, Maney Publishing, UK: 153-165.

Sourmail, T. (2004). "NEUROMAT model manager manual." NEUROMAT Ltd, UK. Stegemann, J.A. (2001). "Neural network analysis of the effects of contaminants on

properties of cement pastes." Ph.D thesis, Imperial College of Science, Technology and Medicine, London, UK

Taki, O., and Yang, D. (1991). "Soil-cement mixed wall technique." Geotechnical Engineering Congress, Geotechnical Special Publication 27, ASCE, NY: 298-309.

Tarassenko, L. (1998). "Mathematical background for neural computing." A guide to neural computing applications, Arnold Publishers, UK.

Terashi, M. (1997). "Deep mixing method- Brief state-of-the-art." Proc., 14th Intl. Conference on Soil Mechanics and Foundation Engineering, Hamburg: 2475-2478

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230 GROUTING AND DEEP MIXING 2012

Hernandez-Martinez, F. (2006). "Ground improvement of organic soils using deep soil mixing." Ph.D thesis, University of Cambridge, Cambridge, UK

Jegandan, S. (2010). "Deep soil mixing with conventional and novel binders." Ph.D thesis, University of Cambridge, Cambridge, UK.

Kawasaki, T., Niina, A., Suzuki, Y., Saito, S., and Babasaki, R. (1981). "On the deep mixing chemical mixing method using cement hardening agent- Takenaka technical research report." Report No. 26: 13-42.

Kitazume, M. (2005). "State of practice report- Field and laboratory investigations, properties of binders and stabilized soil." DeepMixing’05- International Conference on Deep Mixing Best Practice and Recent Advances, Stockholm, Sweden: 660-684.

Lai, S., and Serra, M. (1997). "Concrete strength prediction by means of neural network." Construction and Building Materials 11 (2): 93-98.

MacKay, D. J. C. (2003). "Introduction to neural networks." Information Theory, Inference and Learning Algorithms, Cambridge University Press, Cambridge: 468-470.

Porbaha, A., S Shibuya, S., and Kishida, T. (2000). "State of the art in deep mixing technology. Part III: Geo-material characterization." Proceedings of the ICE - Ground Improvement 4 (3): 91-110.

Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). "Artificial neural network applications in geotechnical engineering." Australian Geomechanics 36 (1): 49-62.

Shahin, M. A., and Indraratna, B. (2006). "Modelling the mechanical behavior of railway ballast using artificial neural networks." Canadian Geotechnical Journal 43 (11): 1144-1152.

Shrestha, R, and Al-Tabbaa., A. (2011). "Introduction to the development of an information management system for soil mix technology using artificial neural networks." Geo-Frontiers 2011- Advances in Geotechnical Engineering, Geotechnical Special Publication 211, ASCE, Dallas: 816-825.

Sourmail, T., and Bhadeshia, H.K.D.H. (2005). "Neural networks." Introduction to Materials Modelling, Maney Publishing, UK: 153-165.

Sourmail, T. (2004). "NEUROMAT model manager manual." NEUROMAT Ltd, UK. Stegemann, J.A. (2001). "Neural network analysis of the effects of contaminants on

properties of cement pastes." Ph.D thesis, Imperial College of Science, Technology and Medicine, London, UK

Taki, O., and Yang, D. (1991). "Soil-cement mixed wall technique." Geotechnical Engineering Congress, Geotechnical Special Publication 27, ASCE, NY: 298-309.

Tarassenko, L. (1998). "Mathematical background for neural computing." A guide to neural computing applications, Arnold Publishers, UK.

Terashi, M. (1997). "Deep mixing method- Brief state-of-the-art." Proc., 14th Intl. Conference on Soil Mechanics and Foundation Engineering, Hamburg: 2475-2478

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