the selection of optimal equipment...
TRANSCRIPT
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
7.9 Neural network EXC7 to predict bucket size and required number of trucks ................................................... 149
7.10 Neural network EXC8 to predict bucket size and required number of trucks ................................................... 150
7.11 Neural network EXC9 to predict bucket size and required number of trucks ................................................... 151
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
7.14 Neural network EXC12 to predict bucket size and required number of trucks ................................................... 154
7.15 Neural network EXC 13 to predict bucket size and required number of trucks ................................................... 155
7.16 Neural network EXC 14 to predict bucket size and required number of trucks ................................................... 156
7.17 Neural network EXC 15 to predict bucket size and required number of trucks ................................................... 157
7.18 Neural network EXC 16 to predict bucket size and required number of trucks ................................................... 158
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
7.22 Neural network LT4 to predict bucket size and required number of trucks ................................................... 163
XXI
7.23 Neural network L T5 to predict bucket size and required number of trucks ................................................... 164
7.24 Neural network L T6 to predict bucket size and required number of trucks ................................................... 165
7.25 Neural network L T7 to predict bucket size and required number of trucks ................................................... 166
7.26 Neural network LT8 to predict bucket size and required number of trucks ................................................... 167
7.27 Neural network L T9 to predict bucket size and required number of trucks ................................................... 168
7.28 Neural network LT10 to predict bucket size and required number of trucks ................................................... 169
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
7.32 Neural network LT14 to predict bucket size and required number of trucks ................................................... 173
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
7.5 EXC3 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............. 145
7.6 EXC4 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............. 146
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
7.8 EXC6 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 148
7.9 EXC7 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 149
7.10 EXC8 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 150
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7.11 EXC9 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 151
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
7.14 EXC12 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 154
7.15 EXC13 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 155
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
7.17 EXC 15 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 157
7.18 EXC 16 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for excavator-truck combinations ............... 158
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
7.21 L T3 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............... 162
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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
7.26 L T8 neural network, parameters and training results, networks for prediction of bucket capacity, and matched number of trucks for Loader-Truck combinations ............... 167
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
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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
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B.16 Cycle time according to soil type ........................... 223
B.17 Correction factors to basic time. . . . . . . . . . . .. . . . . . . . . . .... . .. . . 223
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