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1. INTRODUCTION

Shear strength, compressibility and permeability are the three basic engineering properties of

a soil mass which are often essential in the design of most of the geotechnical engineering

problems. The major problems associated with the determination of engineering properties of

soil are the difficulties in obtaining undisturbed samples and the time and expenditure

required in conducting the required laboratory tests. . In a disturbed sample, the soil structure

will be completely altered that the engineering properties will not be representative of in-situ

conditions. Whereas, the methods to determine the index properties of soil like liquid limit,

plastic limit, water content etc. are favoured over those for determining engineering

properties because:

The test procedures are simple

The tests are less time consuming and inexpensive The tests could be conducted even on disturbed samples to get reasonable results.

Hence, for the above reasons many researchers have made several attempts to correlate the

engineering properties of soil with its index properties using many methods or techniques.

A few such recent attempts are the following:

Amardeep Singh and Shahid Noor (2012) describe the method to predict the compression

index (Cc) for fine grained soils based on index properties (LL and PI).

Nakhaei (2005) estimated the saturated hydraulic conductivity of granular material based on

grain size distribution curve.

Venkatasubramanian and Dhinakaran(2011) proposed method for correlating CBR values

with the liquid limit, plastic limit, plasticity index, OMC, Maximum dry density, UCC values

of various soils.

Burak Goktepe et.al(2008) performed statistical and artificial neural network (ANN)-based

methods on establishing correlations between index properties and shear strength parameters

of normally consolidated plastic clays.

As it is difficult to obtain undisturbed samples for determining the engineering properties of

soil, alternative methods are required which predict the engineering properties from index

properties. Artificial neural network has been used as a successful prediction tool for various

geotechnical engineering tasks. Here an attempt is made to predict the engineering properties

from index properties using ANN.

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2. CONVENTIONAL METHODS OF PREDICTING ENGINEERING PROPERTIES

FROM INDEX PROPERTIES

Several correlations have been proposed whereby engineering properties like compression

index, shear strength parameters and permeability have been evaluated using liquid limit,

natural moisture content, initial void ratio, plasticity index, specific gravity, void ratio at

liquid limit, and several other properties of soil. A few such well known co-relations are

given below.

Skemptons formula

Skempton (1994) established a relationship between Cc and liquid limits for remoulded clays

as,

Cc = 0.007 (wl10)

Where wlis the liquid limit in percent

Terzaghi and peck formula

Based on the work of Skempton and others, Terzaghi and Peck (1976) modified the above

equation applicable to normally consolidated clays of low to moderate sensitivity as,

Cc = 0.009 (wl10)

Azzouz et al formula

Azzouz et al (1976) proposed a number of co-relations based on the statistical analysis of a

number of soils. The one of the many reported to have 86 percent reliability is

Cc= 0.37 (eo+ 0.003 wl+ 0.0004wc- 0.34)

Where eo= in situ void ratio, wl = liquid limitandwc = water content in percent. For organic

soil they proposed

Cc= 0.115 wc

Houghs formula

Hough(1957), on the basis of experiments on precompressed soils, has given the following

equation

Cc= 0.3 (eo0.27)

This equation may be applicable to precompressed soils.

Nagaraj and Sr ini vasa Murthy formula

Nagaraj and Srinivasa Murthy formula (1983) have developed equations based on their

investigation as follows

Cc= 0.2343 el

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Cc= 0.39 eo

where el is the void ratio at liquid limit and eo in situ void ratio

Hazens formula

Extensive investigations of clean filter sands by Hazen (1892) lead to the correlation between

permeability and effective grain size (D10). Hence it was concluded that the permeability

could be determined from grain size or gradation. And obtained the empirical relation,

k = CD102

C, a factor which varies from 100 to 150. Sometimes a value of 100 is assumed for all

practical purposes.

The important factors that affect permeability of soil are: void ratio, grain size, structure and

stratification and temperature.

Taylors formula

To estimate k, at void ratios, Taylor (1948) proposed, k1: k2=

:

Where, the coefficients c1and c2depends on soil structure.

3. PREDICTION OF ENGINEERING PROPERTIES FROM INDEX PROPERTIES

USING NUMERICAL METHODS

Different numerical methods have gained wide acceptability among geotechnical engineers as

a valuable method of analysis. Some of those methods include the finite element method,

boundary element method, etc. In the last two decades there has been a great expansion in the

power and availability of numerical procedures.

Artificial neural network is one among the most widely used method for solving various

problems in geotechnical engineering. For the last few decades, several successful attempts

have been made in the field of artificial neural network to solve many geotechnical problems.

ANNs learn from data examples presented to them and use these data to adjust their weights

in an attempt to capture the relationship between the model input variables and the

corresponding outputs. ANNs do not need any prior knowledge about the nature of the

relationship between the input/output variables, which is one of the benefits that ANNs have

compared with most empirical and statistical methods.

Artificial neural networks (ANNs) are a form of artificial intelligence which attempt to mimic

the behaviour of the human brain and nervous system. A typical structure of ANNs consists

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of a number of processing elements (PEs), or nodes, that are usually arranged in layers: an

input layer, an output layer and one or more hidden layers (Figure 1).

Figure1. Artificial neural network

The input from each PE in the previous layer (xi) is multiplied by an adjustable connection

weight (wji). At each PE, the weighted input signals are summed and a threshold value (j) is

added. This combined input (Ij) is then passed through a non-linear transfer function (f(.)) to

produce the output of the PE (yj). The output of one PE provides the input to the PEs in the

next layer. This process is summarised in Equations 1 and 2 and illustrated in Figure 1.

Ij= wjixi + j------------- (1) summation

yj= f(Ij)---------------------(2) transfer

The propagation of information in ANNs starts at the input layer where the input data are

presented. The network adjusts its weights on the presentation of a training data set and uses

a learning rule to find a set of weights that will produce the input/output mapping that has the

smallest possible error. This process is called learning or training. Once the training phase of

the model has been successfully accomplished, the performance of the trained model has to

be validated using an independent testing set.

4. PROPOSED WORK

In the present work, an attempt is made to predict the engineering properties of soils from

index properties using artificial neural network. Various stages of the propose work are listed

below.

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Creation of dataset

The dataset for the present work will be based on the soil tests conducted in the Geotechnical

laboratory of Civil engineering department of NIT Calicut. The dataset include values of

various input parameters like liquid limit, plasticity index, fine fraction, natural moisture

content, natural density and various output parameters like compression index, cohesion,

angle of friction and permeability.

Selection of input parameters

The input parameters are selected based on their correlation with engineering properties.

Input parameters selected for the present study are liquid limit, plasticity index, fine fraction,

natural moisture content, natural density.

Development of algorithm for ANN

A suitable algorithm for the network has to be developed and the flowchart for the algorithm

is prepared.

Coding the algorithm

The algorithm is coded using C++ or Mat lab

Validation and testing

The soil data collected is divided into two parts using some mode of distribution. A portion of

the data is used for training the network and the other portion for testing. The feed forward

back propagation training network models have been coded into a MATLAB program using

neural network toolbox. The MATLAB software enables training with different convergence

criteria, tolerance level, activation functions and number of epochs. The network

training/testing halts automatically once the mean square error value converges within the

tolerance limit. After this the network is ready for prediction of desired output.

Comparison of ANN result with other standard correlations

Once we get the output, it is compared with those obtained from standard correlations.

Development of suitable user interface

A suitable user friendly interface is developed using Visual basic software. Different modules

are created for entering input values, analysis of the result, display the output values and even

for validation of the result.

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Provision of necessary help facilities

Provisions are given within the interface for any necessary help to the user. Provisions are

also given to save or print the result.

5. FLOWCHART FOR PREDICTION PROCESS

Data collection and dataset creation

Select input variables

Select neural structure

Training and testing

Validation

Implementation

Figure2. Flowchart for prediction process

Figure shows the steps in the general problem of nonlinear system identification: the data

collection problem and creation of dataset, the selection of the model family, the selection of

the structural parameters of the model in the family (which is equivalent to finding the

structure and size of the neural network), the selection of approximate values for the

parameters of the model (training of the network) and the validation of the model obtained,

and its implementation in a control system.

6. IMPLEMENTATION

The problem of prediction is simplified by an ANN based expert system developed in visual

basic software. Provisions are given for the user to enter the input parameters. Once the input

parameters are entered, the network computes the outputs which include shear strengthparameters, compressibility parameter and permeability. This obtained output is then

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compared with the result obtained from standard correlations. The user interface is provided

with provisions of necessary help facility to the user. Provisions are provided even for saving

the obtained result and also the user can go for printing the output.

Fine fraction liquid limit plasticity index NMC natural density D10

ANN model for engineering property prediction

Compression index cohesion angle of friction permeability

Figure3. Neural network for engineering property prediction

The user interface consists of 4 modules, input, analysis, output and validation. Input module,

to enter the numerical values of the input parameters. In the analysis module, the system

calculates the engineering properties which include shear strength parameters,

compressibility parameter and permeability. The calculated values are displayed in the output

module. And in the validation module, the obtained result is compared with some existing

conventional correlations.

Figure4. Expert system for prediction

EXPERTSYSTEM

INPUT

1. FINE FRACTION

2. LIQUID LIMIT

3. PLASTICITYINDEX

4. NATURALDENSITY

5. NATURAL WATERCONTENT

6. D10

ANALYSIS

1. SHEAR STRENGTHPARAMETER

CALCULATION

2. COMPRESSIBILITYPARAMETER

CALCULATION

3. PERMEABILITYCALCULATION

OUTPUT

1. C,

2. CC

3. k

VALIDATION

USING VARIOUSTHERRITICAL CO-

RELATIONS

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7. PROPOSED WORK SCHEDULE

TASK2013 2014

JUL AUG SEP OCT NOV DEC JAN FEB MAR

Literature survey

Problem formulation

Development of ANN algorithm

Familiarization with VB

Data collection and dataset creation

ANN coding

Development of user interface

ANN training

Testing and Validation

Draft preparation and thesis

submission

8. SUMMARY

The complexity associated with some geotechnical engineering materials such as sand and

gravel, is the difficulty in obtaining undisturbed samples and time consuming involving

skilled technicians. Shear strength of a soil is perhaps the most important of its Engineering

properties, as all stability analyses in the field of Geotechnical Engineering are dependent on

Shear strength of soil. Permeability is very important engineering property of soils.

Knowledge of permeability is essential in settlement of buildings; yield of wells, seepage

trough and below the earth structures. The compression of a saturated soil under a steady

static pressure is known as consolidation. It is entirely due to expulsion of water from the

voids. To cope up with the difficulties involved, an attempt has been made to model

Engineering properties of soil i.e. Shear Strength parameters, permeability and compression

index in terms of Fine Fraction (FF), Liquid Limit (WL), Plasticity Index (IP), Natural

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density, and natural Moisture content. A user friendly user interface is also developed for the

user to input the variables and get the output. Provisions are given to compare the result with

conventional correlations. The user can even save and print the results.

9.REFERNCES

1. Amardeep Singh, Shahid Noor., 2012, Soil Compression Index Prediction Model forFine Grained Soils, International Journal of Innovations in Engineering and

Technology, 4(1) , 34-37

2. Booker J. R, Carter J. P, Small J. C, Brown P. T, Poulos H. G., 1989, Some recentapplications of numerical methods to geotechnical analysis, Computers & Structures

31(1), 81-92.

3. Burak Goktep, Selim AltunGokhan Altintas, Ozcan Tan.,2008, Shear strengthestimation of plastic clays with statistical and neural approaches, Building and

Environment, 43 , 849860

4. Ghanbarian-Alavijeh B, Liaghat A.M., and Sohrabi S., 2010, Estimating SaturatedHydraulic Conductivity from Soil Physical Properties using Neural Networks Model,

World Academy of Science, Engineering and Technology , 38, 121-126

5. Mohamed A. Shahin, Mark B. Jaksa and Holger R. Maier., 2001, Artificial neuralnetwork applications in geotechnical engineering, Australian Geomechanics, 49-62

6. Nakhaei M., 2005, Estimating the Saturated Hydraulic Conductivity of GranularMaterial, Using Artificial Neural Network, Based on Grain Size Distribution Curve,

Journal of Sciences, Islamic Republic of Iran, 16(1), 55-62

7. Rajeev Jain, Pradeep Kumar Jain, Sudhir Singh Bhadauria., 2010, Computationalapproach to predict soil shear strength, International Journal of Engineering Science

and Technology, Vol. 2(8), 3874-3885

8. Sudha Rani, Phani Kumar Vaddi , Vamsi Krishna Togati N.V., 2013 , ArtificialNeural Networks (ANNS) For Prediction of Engineering Properties of Soils,International Journal of Innovative Technology and Exploring Engineerin, 3(1), 123-

130

9. Terzaghi K., Peck R B., 1948, Soil mechanics in engineering practice, Wiley, NewYork

10.V N S Murthy, 1993, A text book of soil mechanics and foundation engineering, SaiKripa technical consultants, 4thedition, Banglore.

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11.Venkatasubramanian.C, Dhinakaran.G., 2011, ANN model for predicting CBR fromindex properties of soils International journal of civil and structural engineering,

2(2), 605-611

12.WSDOT Geotechnical Design Manual, January 2010, chapter 10