Prediction of Pullout Capacity of Anchor Foundation in Sand using Artificial Neural Networks

124 Pages·2017·2.67 MB·English

Prediction of Pullout Capacity of Anchor Foundation in
Sand using Artificial Neural Networks

by

Bayshakhi Deb Nath

A thesis submitted to the Department of Civil Engineering, Khulna University of
Engineering & Technology in partial fulfillment of the requirements for the degree of
Master of Science in Civil Engineering

Khulna University of Engineering & Technology
Khulna 9203, Bangladesh
September 2017
Declaration
This is to certify that the thesis work entitled "Prediction of Pullout Capacity of Anchor
Foundation in Sand using Artificial Neural Networks” has been carried out by Bayshakhi Deb
Nath in the Department of Civil Engineering, Khulna University of Engineering & Technology,
Khulna, Bangladesh. The above thesis work or any part of this work has not been submitted
anywhere for the award of any degree or diploma.

Signature of Supervisor Signature of Candidate

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Approval
This is to certify that the thesis work submitted by Bayshakhi Deb Nath entitled Prediction of
Pullout Capacity of Anchor Foundation in Sand using Artificial Neural Networks has been
approved by the board of examiners for the partial fulfillment of the requirements for the degree
of Master of Science in Civil Engineering in the Department of Civil Engineering, Khulna
University of Engineering & Technology, Khulna, Bangladesh in 2017.

BOARD OF EXAMINERS

1. Chairman

(Supervisor)

Dr. Md. Rokonuzzaman
Professor
Department of Civil Engineering
Khulna University of Engineering & Technology

2.
Member

Head of the Department
Department of Civil Engineering
Khulna University of Engineering & Technology

4.
Member

Dr. Abu Zakir Morshed
Professor
Department of Civil Engineering
Khulna University of Engineering & Technology

5.
Member

Dr. Ismail Saifullah
Assistant Professor
Department of Civil Engineering
Khulna University of Engineering & Technology

6.
Member

Dr. Syed Abdul Mofiz (External)
Professor
Department of Civil Engineering
Rajshahi University of Engineering & Technology
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Acknowledgement
I would like to express my gratitude to my supervisor Prof. Dr. Md.
Rokonuzzaman as without his continuous support, expertise and motivation, this thesis
would not become in reality. While performing this research, his guidance and support,
while still allowing me the freedom to learn first-hand, proved to be technical in the
advancement of my work. His high standards and commitment to research set a strong
example for me in my time in the work, and provides me with the motivation necessary
to perform the required tasks. He has passionately inspired me throughout this research.
I am really thankful for his great mentorship, time, and support throughout the thesis
work over the last one year. I would also like to thank my committee members, Prof.
Dr. Abu Zakir Morshed and Dr. Ismail Saifullah for their crucial suggestion on my
research projects, and my external examiner Prof. Dr. Syed Abdul Mofiz for his time
to read and critique my thesis.

I would also like to thank my respective teachers, and colleagues in the
Department of Civil Engineering, Khulna University of Engineering & Technology for
their valuable supports.

Finally, I would like to express my deepest love, respect and appreciation to my
parents, husband, brother and sisters, for their consistent and unconditional love and
support. I am very much indebted to my parents and husband for their motivation which
has sustained me throughout the long journey of my academic life.

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Abstract
Plate anchors are widely used in offshore foundation to resist the uplift forces. Over the
years, many theoretical and numerical methods have been developed to predict the uplift
capacity of plate anchor in cohesionless soil. However, these methods are inconsistent with
each other and with the experimental results. Moreover, most of the theories comprises the
pullout capacity of plate anchor oriented in horizontal or vertical direction. Although very
few theories cover the effect of anchor rotation on the pullout capacity for strip anchor but
cannot be used for other shaped anchor such as square, rectangular etc. Therefore, an
attempt is taken to make a correlation with the existing experimental investigation as
correlations are very significant, when it is difficult to measure the amount directly, and in
other cases it is desirable to ascertain the results with other tests through correlations. Soft
computing techniques are now being used as alternate statistical tool, and new techniques
such as artificial neural networks (ANNs) has been employed for developing the predictive
models to estimate the required parameters. Literatures reveals that the uses of ANN in
engineering has increased over the last few years and could be used to many geotechnical
engineering problems (pile capacity prediction, modelling soil behaviour, site
characterization, earth retaining structures, settlement of structures, slope stability, design
of tunnels and underground openings, liquefaction, soil permeability and hydraulic
conductivity, soil compaction, soil swelling and classification of soils) with some degree
of success. In this investigation, artificial neural network is used to predict the pullout
capacity of anchor foundation. The personal computer based software MATLAB 2015 is
used to simulate the ANN operation. About 583 pullout capacity test data consisting of
both field and laboratory measurement obtained from published literature are used to
develop the prediction model. Anchor width (B), embedment ratio (H/B), aspect ratio
(L/B), Rotation (), unit weight of soil () and friction angle of soil () are used as input
parameters, while pullout capacity factor (Nq) is used as output. Feed Forward Back
propagation algorithm is used to train the network. These data are divided into three
subsets: training, testing and validation sets containing different percentage of data.
Various error criteria such as mean square error (MSE) and mean absolute error (MAE),
and correlation coefficient (R) have been considered to check the accuracy of the work.
The ANN geometry (no. of hidden neurons, no. of hidden layers, and training functions)
are also varied to optimize the network weight i.e. minimum error and maximum
correlation coefficient value. It is seen that one hidden layer containing two neurons, and
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Levenberg-Marquardt (trainlm) training function gives the optimum weight of the network.
Different sensitivity analysis has been shown to identify the significance of different input
parameters that affects the developed models. Finally, an ANN based prediction equation
is developed and the predicted values are compared with the testing data sets includes
experimental results and the existing theories. The performance comparison reveals that
the ANN model is a good tool for minimizing the uncertainties and inconsistency of
correlations in the prediction of pullout capacity of plate anchor in cohesionless soil.

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Contents
DECLARATION .............................................................................................................. II
APPROVAL .................................................................................................................... III
ACKNOWLEDGEMENT .............................................................................................. IV
ABSTRACT ...................................................................................................................... V
CONTENTS ................................................................................................................... VII
LIST OF TABLES .......................................................................................................... XI
LIST OF FIGURES ..................................................................................................... XIII
CHAPTER 1 Introduction ........................................................................................... 1
1.1 Introduction ........................................................................................................... 1
1.2 Background of This Study .................................................................................... 1
1.3 Objectives of the Research Work .......................................................................... 4
1.4 Organization of the Thesis .................................................................................... 4
CHAPTER 2 Uplift Capacity Theories ....................................................................... 6
2.1 Introduction ........................................................................................................... 6
2.2 Plate Anchors ........................................................................................................ 6
2.3 Classical Theories of Uplift capacity for Plate Anchors ....................................... 7
2.3.1 Horizontal Plate Anchors ...................................................................................... 8
2.3.1.1 Meyerhof and Adams ............................................................................................ 9
2.3.1.2 Clemence and Veesaert ....................................................................................... 11
2.3.1.3 Murray and Gaddes ............................................................................................. 12
2.3.1.4 Frydman and Shaham .......................................................................................... 14
2.3.1.5 Ovesen ................................................................................................................. 15
2.3.1.6 Tagya ................................................................................................................... 15
2.3.2 Vertical Plate Anchors ........................................................................................ 16
2.3.2.1 Ovesen and Stromann ......................................................................................... 17
2.3.2.2 Biarez et al. ......................................................................................................... 18
2.3.2.3 Ovesen ................................................................................................................. 19
2.3.2.4 Meyerhof ............................................................................................................. 19
2.3.3 Inclined Plate Anchors ........................................................................................ 20
2.3.3.1 Meyerhof ............................................................................................................. 21
2.3.3.2 Hanna et al. ......................................................................................................... 22
2.3.3.3 Ovesen ................................................................................................................. 22
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CHAPTER 3 Experimental DATABASE ................................................................. 24
3.1 Introduction ......................................................................................................... 24
3.2 Past Experimental Investigations ........................................................................ 24
CHAPTER 4 Artificial Neural NetworkS ................................................................ 28
4.1 Introduction ......................................................................................................... 28
4.2 Basic Neural Network Model ............................................................................. 28
4.3 Artificial Neural Network ................................................................................... 30
4.4 Activation Function ............................................................................................. 30
4.5 Architecture of ANNs ......................................................................................... 31
4.5.1 Feed Forward ANNs ........................................................................................... 31
4.5.2 Feedback/Recurrent ANNs ................................................................................. 32
4.6 Multilayer Perceptron (MLP) Networks ............................................................. 32
4.7 Back propagation ................................................................................................ 33
4.7.1 Batch Training ..................................................................................................... 33
4.7.2 Batch Gradient Descent ...................................................................................... 34
4.7.3 Gradient Descent with Momentum ..................................................................... 34
4.7.4 Variable Learning Rate ....................................................................................... 34
4.7.5 Resilient Backpropagation .................................................................................. 35
4.7.6 Conjugate Gradient Algorithms .......................................................................... 36
4.7.6.1 Fletcher-Reeves Update ...................................................................................... 36
4.7.6.2 Polak-Ribiére Update .......................................................................................... 37
4.7.6.3 Powell-Beale Restarts ......................................................................................... 38
4.7.6.4 Scaled Conjugate Gradient .................................................................................. 38
4.7.7 Quasi-Newton Algorithms .................................................................................. 39
4.7.8 One Step Secant Algorithm ................................................................................. 39
4.7.9 Levenberg-Marquardt (trainlm) .......................................................................... 39
4.8 Learning Rules .................................................................................................... 40
4.8.1 Supervised Learning ........................................................................................... 40
4.8.2 Unsupervised Learning ....................................................................................... 41
4.9 ANN Model Equation ......................................................................................... 41
CHAPTER 5 Methodology ........................................................................................ 43
5.1 Introduction ......................................................................................................... 43
5.2 Data Collection ................................................................................................... 43
5.3 Model Inputs and Output .................................................................................... 46
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5.4 Data Division ...................................................................................................... 46
5.5 Data Pre-processing ............................................................................................ 46
5.6 ANN Model Architecture .................................................................................... 47
5.6.1 Activation Function ............................................................................................. 48
5.6.2 Model optimization ............................................................................................. 49
5.6.3 Stopping criteria .................................................................................................. 49
5.6.4 Model Validation ................................................................................................ 49
5.6.5 Post Processing ................................................................................................... 50
5.7 Sensitivity analysis .............................................................................................. 50
5.7.1 Connection weight approach ............................................................................... 51
5.7.2 Garson’s algorithm .............................................................................................. 52
5.8 Development of the ANN Model ........................................................................ 53
CHAPTER 6 Results & Discussions.......................................................................... 57
6.1 Introduction ......................................................................................................... 57
6.2 Performance Evaluation of ANN Models ........................................................... 57
6.2.1 Effect of Data Division ....................................................................................... 57
6.2.2 Effect of ANN Model Geometry ........................................................................ 60
6.2.2.1 Training Function ................................................................................................ 60
6.2.2.2 No. of Hidden Layer ........................................................................................... 61
6.2.2.3 No. of Hidden Neurons ....................................................................................... 63
6.3 Performance of proposed ANN Model ............................................................... 65
6.4 Comparison of the ANN model with the Previous Theories .............................. 68
6.5 Proposed Design Equation .................................................................................. 82
6.6 Sensitivity Analysis ............................................................................................. 85
6.6.1 Connection weight approach ............................................................................... 86
6.6.2 Garson’s algorithm .............................................................................................. 86
6.7 Parametric Study ................................................................................................. 87
6.7.1 Embedment Effect ............................................................................................... 87
6.7.2 Shape Effect ........................................................................................................ 88
6.7.3 Rotation Effect .................................................................................................... 89
6.7.4 Friction Angle Effect .......................................................................................... 93
CHAPTER 7 Conclusions and Recommendations .................................................. 95
7.1 Conclusions ......................................................................................................... 95
7.2 Limitations .......................................................................................................... 96
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7.3 Recommendations ............................................................................................... 97
REFERENCES ................................................................................................................ 98
DEVELOPED ANN MODEL ...................................................................................... 106

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