Prediction Of Flyrock In Boulder Blasting Using Neural Network. A Simulation Approach

 
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hereby declare that I have read this report and in my opinion this report is sufficient in terms of scope and quality for the award of the degree of Master of Engineering (Civil-Geotechnics)

Signature

: Assoc. Prof. Dr. Hj. Edy. Tonnizam Bin Hj. Mohamad 14 AGUST 2012

Name of Supervisor : Date :

PREDICTION OF FLYROCK IN BOULDER BLASTING BY USING ARTIFICIAL NEURAL NETWORK

DANIAL JAHED ARMAGHANI

A project report submitted in fulfilment of the requirements for the award of the degree of Master of Engineering (Civil Geotechnics)

Faculty of Civil Engineering Universiti Teknologi Malaysia

AUGUST 2012

I declare that this project report entitled BOULDER BLASTING BY USING ARTIFICIAL NEURAL NETWORK result of my own research except as cited in the references. The report has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.

Signature Name Date

: : : DANIAL JAHED ARMAGHANI 14 AUGUST 2012

Specially Dedicate To:

My Beloved Father and Mother And My Sister For their never ending support and thanks for all the loves and motivation

My Supervisor Assoc. Prof. Dr. Hj. Edy. Tonnizam Bin Hj. Mohamad For his guidance and assistance throughout the whole thesis

ACKNOWLEDGEMENT

To complete this master project report, I had received a lot of information and valuable guidance from my supervisor, Assoc. Prof. Dr. Hj. Edy. Tonnizam Bin Hj. Mohamad. His knowledge and research on blasting related subjects are particularly helpful to provide me the insights and understanding needed to complete this report. Thus, I wish to express my deepest gratitude and appreciation to him for his generosity, critics and intellectual support.

I would like to appreciate my family members for their unconditional love and support that assert me to pass obstacles while doing this study. I owe them a great deal of both time and love. Great thanks also to all my best friends who have provided their great assistance at various occasions.

I also offer my regards and blessings to all of those who supported me in any respect during the completion of the thesis.

ABSTRACT

Rock mass is blasted to break it into smaller pieces such as in most surface mining, quarrying operation, dimensional stone mining and some civil engineering application. Flyrock is one of the most hazardous side effects of blasting operation in surface mining. This phenomenon can be considered as the main cause of casualties and damages. The aim of this study is to compare the actual distance of flyrock with the prediction suggested by empirical methods and by using Artificial Neural Network. In addition, this study is also aimed to investigate the most significant input parameters that affecting the flyrock. During this study, flyrock projections for 16 granitic boulders were monitored at Ulu Tiram-quarry site. Blasting parameters such as amount of explosive used, burden, stemming, hole depth, hole angle and hole diameter were carefully measured and recorded. By using these data and applying MATLAB (Matrix Laboratory) program (neural network toolbox), the flyrock distances were predicted for similar condition. The result shows that the coefficient of correlation between the actual and the predicted flyrock distance based on empirical methods is insignificant that is around 0.2. However the result revealed that the coefficient of correlation for overall analysis of flyrock distance is 0.92 based on ANN method. Based on Max-Min method powder factor, stemming and charge length are the most significant parameters in controlling the flyrock distance. This study found that ANN method produced a more accurate prediction than the empirical methods in assessing the actual flyrock projection.

ABSTRAK

Kerja letupan perlu dilakukan untuk memecahkan batuan besar kepada saiz yang lebih kecil dalam banyak projek kejuruteraan awam seperti perlombongan dan operasi kuari. Batu terbang adalah merupakan salah satu kesan negatif akibat operasi letupan dalam industri perlombongan permukaan. Fenomena ini boleh menyebabkan kerosakan dan kebinasaan. Matlamat kajian ini dilakukan adalah untuk membandingkan jarak sebenar batu terbang dengan ramalan seperti yang dicadangkan oleh cara empirical dan dengan menggunakan Jaringan Neural Buatan (Artificial Neural Network). Tambahan lagi, kajian ini juga dilakukan untuk menyiasat parameter input yang paling member kesan kepada batu terbang. Semasa kajian dilakukan, pemerhatian di atas unjuran batu terbang untuk 16 batuan granite besar dilakukan di tapak kajian yang terletak di Ulu Tiram. Parameter letupan seperti jumlah bahan letupan digunakan, beban, kedalaman lubang, sudut lubang dan diameter lubang diukur dan direkod. Dengan menggunakan data-data yang dicatat, dan menggunakan program MATLAB , jarak batu terbang telah diramal didalam keadaan yang sama. Hasil kajian menunjukkan pekali hubungkait antara jarak sebenar dengan jarak ramalan menggunakan kaedah empirikal adalah tidak begitu besar iaitu 0.2. Walau bagaimanapun, hasil kajian mendedahkan bahawa pekali hubungkait untuk keseluruhan analisis batu terbang ialah 0.92 berdasarkan Kaedah Jaringan Neural Buatan. Berdasarkan kaedah Max-Min, factor debu, punca dan panjang cas adalah parameter yang paling penting dalam mengawal jarak batu terbang. Kajian ini menyimpulkan bahawa Kaedah Jaringan Neural Buatan menghasilkan ramalan jarak batu terbang empirikal. yang lebih tepat berbanding kaedah

TABLE OF CONTENTS

CHAPTER

TITLE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF SYMBOLS

PAGE ii iii iv v vi vii xii xiii xviii

1

INTRODUCTION

1.1 1.2 1.3 1.4 1.5 Background Problem Statement Aim and Objectives of Study Scope of Study Site Location

1

1 2 3 4 6

2

LITERATURE REVIEW

2.1 2.2 Introduction Flyrock 2.2.1 2.2.2 2.3 Backfilling Blasting Mats

8

8 9 9 11 12 13 14 14 15 17 17 19 20 21 21 21 22 23 25

Flyrock Mechanism 2.3.1 2.3.2 2.3.3 2.3.4 Cratering Face Bursting Rifling Secondary Blasting

2.4

Causes of Flyrock 2.4.1 2.4.2 2.4.3 2.4.4 2.4.5 2.4.6 2.4.7 Inadequate Burden and Spacing Overloaded Holes Inadequate Stemming Inaccurate Drilling Geological Condition Faulty Delay Timing and Initiation Sequence Miscellaneous Causes

2.5 Calculation of Flyrock Distance 2.6 Blasting 2.6.1 2.6.2 Primary Blasting Secondary Blasting 2.6.2.1 Boulders

26 26 26

2.6.2.1.1

Blockholding

29 30 31

2.6.2.1.2 Mudcapping 2.6.2.1.3 Snakehole 2.6.2.1.1 Formation of Boulders in Granite 2.7 Blast Design 2.7.1 2.7.2 2.7.3 2.7.4 2.7.5 2.7.6 2.7.7 2.7.8 2.7.9 Blast Geometry Bench Height Bench Width Blasthole Inclination Blasthole Diameter The Burden Spacing Subdrilling Stemming

31 33 33 34 35 35 36 37 39 40 41 42 44 44 45 45 46 47 47

2.7.10 Weathered Condition 2.7.11 Powder Factor and Specific Charge

2.8 Blasting Material 2.8.1 Explosive 2.8.1.1 2.8.1.2 2.8.1.3 2.8.1.4 Dynamite Ammonium Nitrate and Fuel Oil Slurry (Water Gel) Emulsion Explosive

2.8.2 2.8.3 2.9

Type and Amount of Explosive Classification of Explosive

48 49 52 56

Igneous Rock 2.9.1 Granite Rock

3

RESEARCH METHODOLOGY

3.1 3.2 3.3 Introduction Site Investigation Data Collection

58

58 59 61 62 69 69 70 71 72 73 73

3.4 Artificial Neural Network 3.5 Neural Network Learning 3.5.1 3.5.2 3.6 Hebbian Learning Perceptron Learning Rule

Training and Testing Neural Networks 3.6.1 3.6.2 3.6.3 Choosing the Number of Neorons Choosing the Initial Weights Choosing the Learning Rate

4

RESULTS AND ANALYSIS

4.1 4.2 Introduction Field Investigation and Observation

74

74 74 75 75 79 83

4.3 Calculation Flyrock Distance by Empirical Formula 4.3.1 4.3.2 Using Formula Using Figure

4.4 Artificial Neural Network Analysis

4.4.1

Train-Validate 4.4.1.1 Prediction of Flyrock Distance

84 84 90 90 96 96 102 103 103

4.4.2

Train-Test 4.4.2.1 Prediction of Flyrock Distance

4.4.3

Train-Validate-Test 4.4.3.1 Prediction of Flyrock Distance

4.5 Significant Parameters 4.5.1 4.5.2 Min-Max Method Significant Parameters Analysis

5

CONCLUSION AND RECOMMENDATION

5.1 Conclusion

106

106 107

4.5 Recommendation

REFRENCES

108

LIST OF TABLES

TABLE NO.

2.1 2.2

TITLE

Flyrock mechanisms and directivity Explosive commonly used in blasting operation

PAGE

16 48

4.1 4.2 4.3 4.4 4.5

Blasting parameters Fragment size in blasting operation Real flyrock distance and flyrock distance by formula (km) Real flyrock distance and flyrock distance by figure (km) Real flyrock distance and flyrock distance by ANN in phase Train-Validate (km)

75 76 77 80 88

4.6

Real flyrock distance and flyrock distance by ANN in phase Train-Test (km)

94

4.7

Real flyrock distance and flyrock distance by ANN in phase Train-Validate-Test (km)

100

4.8 4.9

Coefficient of correlation in different analysis Range and symbol of blasting parameters

102 104

LIST OF FIGURES

FIGURE NO.

1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 2.4

TITLE

Fatal injury due to flyrock at a limestone mine Overall view of the blasting site Overall view of the blasting site (view from bottom) Some boulders at the top of bench Site location Ulu Tiram, Johor Bahru

PAGE

3 5 5 6 7 7 10 11 12 18

Plan view of site location Backfilling Blasting mat prohibiting flyrock from traveling Schematic flyrock terminology Insufficient burden caused due to deviation of hole in flyrock

2.5 2.6

Large burden and top priming causes flyrock Collaring too near top of the face in bench or highly inclined face

18 19

2.7

Open joints, mud seams and cavities may result in escape of gases and also blasting nuisances

20

2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19

Effective of adequate and inadequate delay

22

Relationship between fragment size and maximum throw 24 Blasting Boulder parameters Boulder with two holes in the blasting site Boulder with two holes in the blasting site Placement of a blockhole charge Placement of a mudcapped charge Placement of a snakehole charge Some granite boulders Blasting parameters Boulders in soft material. Boulder marked A will be broken while boulders marked B will not be broken 25 27 28 28 29 30...

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