the selection of optimal equipment...

ARAB ACADEMY FOR SCIENCE, TECHNOLOGY AND MARITIME TRANSPORT COLLEGE OF ENGINEERING AND TECHNOLOGY

The Selection of Optimal Equipment Combinations In The Construction of Irrigation Canals &Drains

Using Neural Networks

Thesis submitted to the Construction Engineering & Management Department In partial fulfillment of the requirements for the Degree of

"Master of Construction Engineering & Management"

Submitted by

Eng. Hatem Hasan Bakr Elammary B.Sc. Civil Engineering1991

Post Graduate Diploma in Structural Engineering 1994 Alexandria University

Supervised by Prof. Dr. Mohamed Abd EI-Rahman Eiganainy

Professor of Irrigation structures Faculty of Engineering-Civil Department

Alexandria University

Dr.Ehab. Mahmoud Elkassas Associate Professor of Structural Engineering

College of Engineering & Technology Arab Academy for Science & Technology& Maritime Transport

ALEXANDRIA, EGYPT . December 2006

ARAB ACADEMY FOR SCIENCE, TECHNOLOGY AND MARITIME TRANSPORT

College of Engineering and Technology

Using Neural Networks

In The Selection of Optimal Equipment Combinations

In The Construction of Irrigation Canal & Drains

by

Hatem Hassan Bakr Elammary

A Thesis Submitted in Partial Fulfillment to the Requirements

for the Master's Degree in

Construction and Building Engineering

Prof. Dr. Mohamed EIGanainy Supervisor

Dr. Ebab Elkassas Supervisor

C :J Prof. Dr. Mobamt~ Examiner

DECEMBER 2006

Prof. Dr. Diaa EI-Quosy Examiner

To my family

with my gratitude for all that they have

done to help me through this stage

The best introduction to this work is the first five

Verses revealed of the Quran:

"Read in the name of thy Lord who created *

Created man out of a clot of conngealed blood *

Read! And thy Lord is Most Bountiful*

He Who taught(the use of)the Pen*

Taught man that which he knew not."

Verses 1-5, Chapter 96

Declaration

I hereby declare that the following thesis has been composed by me, that the work of

which a record has been carried out by myself, and it has not been presented in any

previous application for a higher degree.

Hatem Hasan Bakr EI-Ammary

December 2006

1

Acknowledgements

I would like to expresss my deepest gratitude to my supervisor Dr.Ehab EI-Kassas

for his encouragement throughout the writing process.

His extremely helpful suggestions proved to be of invaluable assistance.

I would like to expresss my most sincere thanks to my supervisor Dr. Mohammed Abd

EI-Rahman Elganainy for valuable suggestions and comments during this work.

My greatest gratitude goes to my wife, who has faithfully supported me throughout this

time.

Finally, I would like to express my greatest appreciation to my parents for their moral

support.

11

Abstract

A frequently problem conforts a contractor is the selection of the most suitable equipment for a

certain project. Contractor should consider the money spent for equipment as an investment,

with a profit, during the useful life of the equipment. A contractor can never afford to own all

types or sizes of equipment that might be used for the kind of work he does. It may be possible

to determine what kind and size of equipment seem most suitable for a given project.

The problems of equipment management is very complex for various reasons.The magnitude

of present day construction and the pace of their working necessitate the fullest utilization of

the equipment on projects. Since a substantial percentage of the total project cost is usually

spent in the purchase of construction equipment, it becomes necessary that the equipment is

judiciously selected. The variety of equipment of different makes, offering improved technical

features, complicates the problem of selection.Where a team work among different machines is

involved, a proper matching of the sizes of machines is a must.The objective is to get the

highest production at a minimum cost.

In this research the employment of construction equipment with emphasis on heavy earth

moving equipment used in the construction of irrigation canals and drains have been studied to

produce the most efficient and cost effective construction possible.

Artificial Neural Networks(ANN) have been applied to many engineering applications in

recent years. The ANN is a new computational technique inspired by studies of the brain and

nervous systems consisting of a large number of highly interconnected neurons. The ANN can

be used to solve the type of problems, for which, we can provide sets of input-output cases but

we neither have an equation nor a procedure that can help us to map between the input and the

output data.

The artificial neural networks have been used as a tool for better management and facilitate

the most economic solution to excavate any type of soil according to the hauling distance, type

of excavator, size of its bucket, and number of hauling units required taking in consideration

size effect of available hauling units, bucket size, cross section of irrigation canal, berm's

width, machine size, job conditions, soil type, swing angle, height of cut, different

combinations of excavators, loaders and trucks, operator skill, type, condition and

111

maintenance of hauling road, inclination of the grade, motional direction of hauling units for

loaded and empty cases, productivity requirements, equivalent machine models for different

common brands, one way haul distances, and wheel pressure condition for truck's tires.

The presented procedure facilitates the selection of the best matched combinations for certain

problems, which makes decision taking easier and faster than that of classical procedures.

The work presented, also, discusses the results of field surveying which have been conducted

during the last two years to gain insights into the current equipment practices of medium to

large size irrigation structures construction firms in Egypt, to facilitate comparisons, to aid in

the identification of trends, and to identify current industry practice with regard to selected

aspects of equipment ownership, record keeping, preferable equipment in different cases,

equipment rental value for different equipment per hour, day, and month, selection method for

proper equipment, common speed for hauling units, recognition of replacement decision

factors, replacement decision making practice, equipment retention periods, available and

common excavation and earth moving plant in Egypt.

The present research paves the way for future development of practical decision tools

according to the field assessment, for each company according to its fleet, and actual rental

values, taking the decesion in one step instead of several steps

IV

Contents

Page

Declaration.. ..... ....... .. . ........ . ..... . ..... ... ....... .. ... .. ..... .. . . . . . ..... 1

Acknowledgements ............................................................ U

Abstract ........................................................................... 111

Contents ......................................................................... v

N otati ons ........................................................................ . xu

Abbreviations .................................................................. . XIV

List of figures XVIU

List of tables .................................................................. XXIII

Chapter (1) Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 01

1.1 Introduction......................................................... 01

1.2 Why use neural networks .......................................... 02

1.3 Outline of the Thesis ................................................ 03

Chapter (2) Earth works ....................................................... 06

2.1 Introduction ........................................................... 06

2.2 Major types of earth works ......................................... 06

2.2.1 Confined excavation ........................................... 07

2.2.2 Battered excavations ........................................... 07

2.2.3 Bulk excavation ................................................. 08

v

2.2.4 Rock excavation ................................................ 09

2.2.5 Embankment construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 10

2.2.6 Grading ........................................................... 11

2.3 Management of earth works ......................................... 12

2.4 Characteristics of soils ............................................... 13

2.4.1 Trafficability .................................................... 14

2.4.2 Loadability ....................................................... 14

2.4.3 Moisture content and drainage characteristics .............. 14

2.4.4 Bulk density ..................................................... 14

2.4.5 Soil volume change ........................................... 14

2.5 Equipment production .............................................. 15

2.5.1 Cost analysis ................................................. 16

2.5.2 Application of queuing theory method ........ ........... 16

2.5.3 Field observations......................... ................... 18

2.5.4 Equipment rental......................... ................... 18

2.6 Mass haul diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . ... 20

2.7 Construction equipment ............................................. 21

2.7.1 Loading equipment. ............................................ 21

2.7.2 Haul equipment ................................................ 22

2.7.3 Support equipment. . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . .. 22

2.7.4 Factors affecting equipment selection ...................... 23

2.7.5 Equipment selection. . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . ... 23

2.7.6 Basic consiqerations common to Load- And - Haul operations ...................................................... 24

. VI

2.7.6.1 Haul road length and profile........................ 25

2.7.6.2 Grade resistance ....................................... 25

2.7.6.3 Rolling resistance ..................................... 25

2.7.6.4 Total hauls road resistance.............. ........ . .. 26

2.7.6.5 Altitude................................................ 26

2.7.6.6 Traction ................................................. 27

2.7.7 Using performance and retarder curves ..................... 27

2.7.7.1 Performance curves.... . . . ... . .... . . . ............... 28

2.7.7.2 Retarder curves ....................................... 29

2.7.8 Travel time determination.. ... .. ...... . . .. ......... . ....... 30

2.8 Excavators ............................................................. 31

2.8.1 Back hoes.................................................... 32

2.8.2 Hoe production.............................................. 32

2.8.3 Job conditions....... .......... . ................. . ........ ..... 33

2.9 Loaders................. ........ . ....................... ... ......... .... 33

2.9.1 Buckets.............. ...................................... 33

2.9.2 Fill factors ...................................................... 35

2.9.3 Operating loads .............................................. 35

2.9.4 Operating specifications..................................... 35

2.9.5 Production rates for wheel loaders ........................ 35

2.9.6 Loaders job management.. . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 36

2.10 Trucks ................................................................ 37

2.10.1 Trucks job management ..................................... 37

Vll

2.10.2 Truck Load-And -Haul analysis........................ 39

2.10.2.1 Truck load time determination................. . 40

2.10.2.2 Loader or excavator, truck haul unit and spread productivity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 40

Chapter (3) Neural networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 42

3.1 Introduction ........................................................... 42

3.2 Historical background .............................................. 42

3.3 Biological neural networks......... . ... . . . .... ..... . .. .... . .. . ... 43

3.4 Why use neural networks .......................................... 44

3.5 Neural networks versus computational models . . . . . . . . . . . . . .. 4t;

3.6 Advantages and disadvantages of ANNs........................ 47

3.7 Modelling the single neuron ....................................... 48

3.8 Feed forward and feed back NNs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.9 The feed forward single layer perceptron ........................ 50

3.10 Limitations of the single layered perceptron .................. 50

3.11 The feed forward multilayer perceptrons ........................ 51

3.12 The learning process ............................................... 52

3.13 Back-Propagation (BP) neural network ......................... 54

3 .14 The mathematics .................................................... 57

Chapter (4) Management of earth work equipment & some applications using ANN technique and expert systems .... 60

4.1 Introduction......................................................... 60

4.2 Methods of operation research in equipment management. .. 60

4.3 Linear programming as a decision tool for selecting the optimum excavator .................................................. 63

Vlll

4.4 Application of artificial neural networks ........................ 64

4.4.1 Artificial neural networks model for predicting excavator productivity ....................................... 65

4.4.2 A neural network based system for predicting earth . d t· movmg pro uc 100 ...................................... . 67

4.4.3 Integrating neural networks with special purpose simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 67

4.5 Selecting earth moving equipment fleets using Genetic algorithms ........................................................... 69

4.6 Selecting earth moving equipment using expert system. . . . . . 69

4.7 Neural networks as a tool in cold formed steel design. . . . . . . .. 70

4.8 Neural networks for non linear analysis of cables .............. 70

4.9 Artificial intelligence applications in geotechnical engineering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 71

4.9.1 Ground waterlDams ...................................... 72

Chapter (5) Contractor equipment management practices ................. 73

5.1 Introduction .......................................................... 73

5.2 Field survey ......................................................... 73

5.3 Equipment service lives............................. . . . . . . . . . . . . . . .. 77

5.4 Equipment commonly used in Egypt ............................. 77

5.5 Equipment utilization decisions .................................... 79

5.6 Dimensional relations between excavator and canal ........... 84

5.7 Ratio between bucket size and hauling unit ..................... 85

5.8 Preferable choice between using small number of large size hauling units or big number of small size hauling units ...... 86

5.9 Excavator productivity difference for tracks or wheel mounting86

5.10 Loader - excavator combinations ................................ 87

IX

5.11 Common speeds of hauling units in haul roads ................ 87

5.12 Common trade mark for used construction equipment in irrigation field ....................................................... 89

5.13 Buy / rent decision and time effect on optimum selection ... 91

5.14 Equipment average rental values ................................ 92

5.15 Replacement decision factors .................................... 93

5.16 Conclusions ............................................................ 95

Chapter (6) Neural network modelling ....................................... 96

6 .1 Introduction ............................................................ 96

6.1.1 Excavation & earth moving optimization problems ... 97

6.1.2 Factors affecting productivity of excavators............ 98

6.1.3 Utilization criteria for excavators ........................... 99

6.2 Aim and objective........... .......................... ............. 102

6.3 Using Neural networks in selection optimization ............... l 08

6.3.1 Production data generation .................................. 109

6.3.2 Training of neural networks .............................. 109

6.3.3 Stages of development ....................................... 110

6.4 Conclusions......................................................... 137

Chapter (7) Results& discussion ............................................. 138

7.1 Introduction ....... . ,_ ............................................... ... 13 8

7.2 Assessment of the neural network to predict difficulty of excavation process ................................................. 139

7.3 Assessment of neural network to predict the best excavator ... 141

7.4 Assessment of the neural networks to predict bucket size and

x

required number of trucks for excavator-truck combinations 141

7.5 Assessment of the neural networks to predict bucket size and required number of trucks for loader-truck combinations ....... 159

7.6 Solved example .................................................... 174

7.7Conclusions ........................................................ 180

Chapter (8) Comparative product lines ....................................... 181

8.1 Introduction ......................................................... 181

8.2 Equipment commonly used in Egypt ............................ 182

8.3 Common trademarks for construction equipment used in Egypt 182

8.4 Equipment average rental values ..................................... 183

8.5 Comparative product lines according to operating weights ... 183

8.6 Solved example .................................................... 192

8.7 Conclusions............. ....... ...... ... . .. .. . .... . ......... ......... 194

Chapter (9) Summary, conclusions and recommendations.............. 195

9.1 Introduction......................................................... 195

9.2 Summary............................................................. 195

9.3 Conclusions .......................................................... 199

9.4 Recommendations for future work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 201

References ...................................................................... 203

Appendix A ( Original questionnair in Arabic ) ............................ 207

Appendix B ( Tables of chapter (2) ) ....................................... 217

Xl

Notations

Earth works

n = number of haul units in the fleet

a = mean arrival rate of a particular haul unit (arrivalslb)

L= mean loading rate of the excavator (unitslb)

r = ratio of arrival rate to loading rate.

Po = probability that no haul unit is available at the loader.

Pt = probability that one or more haul units are available at the loader

THRR: total haul road resistance

Q=heaped bucket capacity (Lcm)

F= bucket fill factor

AS:D=angle of swing and depth (height) of cut correction

T= Cycle time in seconds

E-Effeciency (min per hour)

Neural network

<D The activation function

m The number of output nodes

n The number of input nodes

o The neuron output

OJ The output value from neuron j in the hidden layer

t The target output

w The weight

wij The weight connecting neuron i in the input layer to neuron j in the hidden layer

wj t The weight connecting neuron j in the hidden layer to neuron t

in the output layer

xu

II w The previous weight change

x The present input

Xo The bias

ex The momentum factor

11 The learning coefficient

oj The error for neuron j in the hidden layer

of The error for neuron f in the output layer

Neural network modelling

V cost = volume cost;

Hcost (L.E.Ihr)= hire cost of each machine per hour;

Machine output (m31 hr) = capacity and time of a machine to remove the substrata;

XllI

Abbreviations

Earth works

AED: Associated Equipment Distributers

Bcm : bank cubic meters

Ccm : compacted cubic meters

CIMA: The Construction Industry Manufacturers Association

Lcm : loose cubic meters

THRR: total haul road resistance

Neural network

BP: Back propagation

SSE: Sum of square errors

Neural network modeling

AEX: Best excavator- neural network

EXC: excavator trucks combination- neural network

JCC: job condition category- neural network

LT: Loader trucks combination- neural network

Soil type in neural networks JCC, AEX, EXC

0: Clay, dry

P : Clay, wet

Q: Earth, dry

EM: Earth, moist

R : Earth, wet

EG: Earth and grave'

XIV

GD: Gravel dry

GW: Gravel, wet

S : Limestone

T : Rock, well blasted

U: Sand, dry

V: Sand, wet

SG: Sand& gravel, dry

SW: Sand& gravel, wet

W: shale

Percent of rock content in neural networks JCC:

RA: 0%

RB: Less than 25%

RC: From 25% to 50%

RD: From 50% to 75%,

Swing angle in neural networks JCC:

SA: Less than 300

SB: From 300 to 600

SC: From 600 to 900

SD: From 900 to 1200

Depth of cut in proportional to machine's max. Capability in neural networks JCC:

DA: Less than 40%

DB: Less than 50%

DC: Less than 70%

DD: Less than 90% ,

xv

Obstruction condition in neural networks JCC:

Y: few

X: No

Job condition category in neural networks JCC, AEX, EXC:

I: Easy digging

J: Medium digging

K: Medium to hard

M: Hard digging

N: Tough digging

Type of excavator in neural network AEX:

1: CAT320

2: CAT320L

3: CAT325

4: CAT325L

5: CAT325BLC

6: CAT330

7: CATE450

8: CAT5110BME

9: CAT5130BME

10: CAT5230BME

Haul road type in neural networks EXC, LT:

A: smooth concrete

B: Good Asphalt

C: Loose sand and gravel

D: Earth compacted and maintained

XVI

E: Earth poorly maintained

F: Earth, rutted, muddy, no maintenance

G: Earth, rutted, very muddy, soft

Wheel pressure in neural networks EXC, L T:

H: high

L: low

Soil type in Neural networks LT:

0: Clay, dry

P : Clay, wet

Q : Earth, dry

Y Earth, moist

R Earth, wet

X: Earth and gravel

J : Gravel dry

I : Gravel, wet

S : Limestone

T : Rock, well blasted

U: Sand, dry

V: Sand, wet

K : Sand& gravel, dry

M : Sand& gravel, wet

XVll

List of figures

Page

2.1 Confined excavation. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . .. 07

2.2 Proposed battered excavation .................................... 07

2.3 Stages of battered excavation................ . ............... . . .. 08

2.4 Proposed bulk excavation and its stages........................ 09

2.5 Proposed embankment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.6 Embankment construction stages..... . . . .. ... .. . .. ..... .. ........ 11

2.7 Perform.ance chart ................................................... 28

2.8 Retarder chart ........................................................ 29

2.9 Loader's Bucket ...................................................... 34

3.1 The basic features of a biological neuron ..................... 44

3.2 The basic features of biological modeL....................... 44-

3.3 Outline of the basic model ....................................... 48

3.4 Schematic drawing of an artificial neuron ...................... 49

3.5 The most common types of linear and non linear transfer function............. . ....... . .... ....................... ............ 50

3.6 Multilayer neural network .......................................... 51

3.7 Schematic drawing of a multilayer Feed forward neural network model .............................. ....... ............... 52

3.8 Schematic drawing of a Multilayer neural network training using the back propagation BP learning fundamentals...... 54

3.9Flowchart for the neural network training and operation stages 56

XV111

3.10 Schematic drawing of a neuron in the input layer . . . . . . . .. 58

3.11 Schematic drawing of a neuron in the hidden layer ......... 59

3.12 Schematic drawing of a neuron on the output layer ......... 59

4.1 The plant selection decision process .............................. 64

5.1 Foundation age for survey's participating companies ........... 75

5.2 Egyptian union classification for survey's participating compani es ............................................................ 75

5.3 Number of irrigation projects for survey's participating compani es ............................................................ 76

5.4 The distribution percentage of construction equipment ... ... .78

5.5 The distribution percentage of construction equipment selection methods ..................................................... 81

5.6The common used combinations for different site conditions 82

5.7 The optimal combination regarding to type of soil and water existence conditions ................................................ 83

5.8 The boom length -channel depth ratio............... ........ ... 84

5.9 The boom length -channel bed width ratio ...................... 85

5.10 Ratio between bucket size and hauling unit. .................... 85

5.11 Selection between using small number of large size hauling units or big number of small size hauling units .................. 86

5.12 Productivity difference between tracks or wheel mounted excavator ............................................................. 87

5.13 Common speeds of hauling units in haul roads............. .88

5.14 Average speeds on rough terrain. ........ ...... . .. ............ .88

5 . 15Common trade mark for used construction equipment in irrigation field.... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .. 90

. XIX

6.1 Excavator's bucket and teeth............... ................... 99

6.2 Excavator's dimensions ............... ................... . ....... 100

6.3 Digging envelope range dimension....... ................... 101

6.4 Flowchart of traditional procedure to select the best plant . .

team composItIon ... .................................................. 104

6.S Flow chart represents the proposed procedure for

determination of the best plant team composition

6.6 Neural network to predict classification of job condition

lOS

category .......................................................... 116

6.7 Neural network to predict the best excavator model. . . . . . . . ... .. 118

6~8 Neural network to predict bucket size and required number of trucks for excavator -truck combinations EXC 1 through EXC 16 ................................................................. 129

6.9 Neural network to predict bucket size and required number of trucks for loader -truck combinations L Tl through L T 14 ...... 131

7.1 Neural network JCC to predict classification of job condition category .......................................................... 140

7.2 Neural network AEX to predict the best excavator model................................................................ 142

7.3 Neural network EXCI to predict bucket size and required number of trucks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . .. . .. 143

7.4 Neural network EXC2 to predict bucket size and required number of trucks ..................................................... 144

7.S Neural network E~C3 to predict bucket size and required number of trucks ..................................................... 145

7.6 Neural network EXC4 to predict bucket size and required number of trucks ..................................................... 146

7.7 Neural network EXCS to predict bucket size and required number of tru.cks ................................................... 147

xx

7.8 Neural network EXC6 to predict bucket size and required number of trucks ................................................... 148




7.12 Neural network EXC 1 0 to predict bucket size and required number 0 f true ks ................................................... 152

7.13 Neural network EXCll to predict bucket size and required number of trucks ................................................... 153


7.15 Neural network EXC 13 to predict bucket size and required number of trucks ................................................... 155




7 .19 Neural network L T 1 to predict bucket size and required number of trucks ................................................... 160

7.20 Neural network L T2 to predict bucket size and required number of trucks ................................................... 161

7.21 Neural network LT3 to predict bucket size and required number of trucks ................................................... 162


XXI







7.29 Neural network LT11 to predict bucket size and required number of'tru.cks ................................................... 1 70

7.30 Neural network LT12 to predict bucket size and required number of trucks ................................................... 1 71

7.31 Neural network LT13 to predict bucket size and required number of'tru.cks ................................................... 1 72


XXIl

List of tables

Page

5.1 Equipment average rental ranges per hour ................... 92

5.2 Equipment average rental ranges per day . . . . . . . . . . . . . . . . .. 92

5.3 Equipment average rental ranges per month .................. 93

6.1 Inputs& outputs of JCC neural network, with BP for the classification of job condition category... ...................... 111

6.2 The classification of job condition category ....................... .112

6.3 Inputs& outputs of AEX neural network, with BP for prediction of the best excavator ................................................. 117

6.4 Part of AEX spread sheet for prediction of the best excavator 120

6.5 Inputs& outputs of EXC neural networks, for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ................................................. 122

6.6 Part ofEXCI spread sheet for prediction of bucket capacity, and matched number of trucks for excavator CAT320+trucks CAT725 combination ....................................... 124

6.7 Part of EXC2 spread sheet for prediction of bucket capacity, and matched number of trucks for excavator CAT320+trucks CA T725 combination . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

6.8 Comparison between EXC neural networks ............... 128

6.9 Inputs& outputs ofLT Neural networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ......................... .......... . . . . . . . . . . . . . . . . . . . . 130

6.10 Part ofLTI spread sheet for prediction of bucket capacity, and matched number of trucks in Loader CAT950G+Trucks CAT725 combination . . . . . . . . . . . . . . . . . . . .. ........................... 133

XXlll

6.11 Part ofLT2 spread sheet for prediction of bucket capacity, and matched number of trucks in Loader CAT950G+Trucks CAT725 combination . . . . . . . . . . . . . . . . . . . .. . ................................ 13 5

6.12 Comparison between L T neural networks ....................... 136

7.1 JCC neural network, parameters and training results, network with BP for the classification of job condition category ............. 139

7.2 AEX neural network, parameters and training results, network with BP for prediction of the best excavator ..................... 141

7.3 EXC 1 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............. 143

7.4 EXC2 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............. 144



7.7 EXC5 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 147




XXlV


7.12 EXC 1 0 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 152

7.13 EXCll neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 153



7.16 EXC 14 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 156



7.19 L Tl neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............... 160

7.20 L T2 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............... 161


xxv

7.22 LT4 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............... 163

7.23 L T5 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ... 00 00 00 .... 00164

7.24 L T6 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations .... 00 00 00 .... .l65

7.25 L T7 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ................ 166


7.27 L T9 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations .............. 168

7.28 L T1 0 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............ 169

7.29 L TIl neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............ 170

7.30 LT12 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............ 171

7.31 L T13 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............ 172

7.32 LT14 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations......... . . . 173

XXVI

8.1 Comparative product lines for back hoes...................... 184

8.2 Comparative product lines for wheel loaders. .......... ..... 188

8.3 Comparative product lines for offhigh way trucks..... ..... 189

8.4 Comparative product lines for articulated dump trucks ....... 190

8.5 Comparative product lines for bulldozers ...................... 191

B.1 Bulk density and bulking factors. . .. ......... . . . . ..... ... . . .. . 217

B.2 Characteristics of soil types according to unified system ... 217

B.3 The values of efficiency factor due to job& management condItIons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

B.4 The probability of haul unit being available.................. 218

B.5 Possible earth moving equipment selections .................. 219

B.6 Rolling resistances (kg/ton) for various types of wheels and surfaces ............. ............................................... 219

B.7 The effect of grade on the tractive effort of vehicles.... ..... 220

B.8 Percent fly wheel horse power available for select Caterpillar machines at specified altitudes. . . . . . . . .. ....................... 220

B.9 Typical values for coefficient of traction for common surfaces ............................................................. 221

B.10 Fill factor for hydraulic hoe buckets ............................ 221

B.l1 Representative dimensions and clearances for hydraulic crawler -mounted hoes .......................................... .. 221

B.12 Cycle time accord~ng to bucket size for back hoes ........... 222

B.13 Bucket fill factor for loaders.................. ................ 222

B.14 Representative specifications for wheel loaders .............. 222

B.15 Cycle time according to bucket size for loaders ............... 223

XXVll

B.16 Cycle time according to soil type ........................... 223

B.17 Correction factors to basic time. . . . . . . . . . . .. . . . . . . . . . .... . .. . . 223

XXV111

the selection of optimal equipment...

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