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ACKNOWLEDGEMENTS
The present graduation project was supported by College of Engineering in Salman Bin Abdelaziz
University and in collaboration with Atheeb Intergraph Company in Al Riyadh in order to carry out
GIS application for constructing knowledge base and representing 3D Geotechnical data concerning
the Central National Museum at Al Morabaa district in Al Riyadh city. At first, we express our
sincere thanks to Al Riyadh Municipality especially the General Department of Surveying and
Mapping, the Civil Engineering Department of College of Engineering in Salman Bin Abdelaziz
University and the King Abdulaziz City for Science and Technology (Space Research Institute) for
their support and cooperation. We would like to think Eng. Amar Al Dawsari General Manager of
General Department of Surveying and Mapping in Al Riyadh Municipality for his ongoing efforts
to look for data and maps according to this project in collaboration with Atheeb Intergraph
Company. Special thanks to Dr. Khaled KHEDER Assistant Professor in Civil Engineering
Department of College of Engineering in Salman Bin Abdelaziz University at Al Kharj, who helped
us to insight and suggested improvement concerning the methodology used to accomplish this
project, as well as his assistance to use GIS software to carry out this application, be grateful for his
comments. We hope that this applied study project using new methods such as GIS application will
help to further prediction for constructing knowledge based on the 3D geotechnical data concerning
the Central National Museum in Al Morabaa district in Al Riyadh city.
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ملخص
يتطلب المربع حي في وخاصة الرياض مدينة وسط في الثقيلة المباني إنشاء على المتواصل الطلب إن
لقواعد إستنادا واألساسات للقواعد األمثل للتصميم الباطنية باإلشتكشافات بالقيام فأكثر أكثر اإلهتمام
القواعد أنجزت ولذلك المستقبلية للمشاريع المجاورة المباني في أنجزت التي والمخططات البيانات
إلى تصل جسات بواسطة السطحية الصخرية الطبقات إستكشاف بعد الثقيلة المباني 20وأساسات
. كما واألساسات القواعد تصميم قبل الجيوتقنية وخصائصها السطحية الطبقات من التحقق بهدف مترا
واإلشراف تنفيذا ثم القواعد هذه لتصميم هندسي إستشاري مكتب بإختيار المشروع صاحب يقوم أنه
. الميدانية المعاينات الخالل من الشروط كراس في عليها المتفق الفنية للمواصفات طبقا إنجازها على
في فرقات هناك بأنه تبين الدراسة موقع أركان مختلف في أدرجت التي بالجسات الخاصة والبيانات
لهذه الكيميائي والتركيب الطبقة سمك بخصوص أخرى إلى جسة من السطحية الطبقات خصائص
اإلطار هذا في المشروع هذا يدخل ولذلك الطبقات لهذه الجيوتقنية الخصائص وكذلك الطبقات
الصخرية للطبقات األبعاد الثالثي التوزيع معرفة بهدف الجغرافية المعلومات نظم تقنية بإستخدام
الجغرافية المعلومات نظم برنامج بواسطة الجيوئحصائية للنمذجة إستنادا النتائج .السطحية أهم من
خصائصها تكون التي السطحية الطبقات توزيع في عشوائية هناك إنه المشروع إليها توصل التي
الموقع هذا في الثقيلة للمباني واألساسات القواعد نوعية من إختياره يتم لما مطابقة غير الجيوتقنية
البحث الضروري من فأنه الدراسة هذه في عليها أعتمد التي الجسات لمحدودية ونظرا الرياض بمدينة
بأجهزة الباطني اإلستكشاف ومنها الجيوتقنية اإلستكشافات لدعم متطورة جديدة أساليب عن
. بيانات قاعدة بناء الضروري من للحقيقة، األقرب العشوائي التوزيع أجل من التطبيقية الجيوفيزياء
المرئيات ( مصادر عدة على فيها اإلعتماد يتم التي و الجغرافية المعلومات نظم برنامج بواسطة جغرافية
( الشركات, يساعد مما الرياض بمدينة المربع لحي الميدانية والزيارات المواقع تحديد نظم الفضائية
مع تتناسب التي واألساسات القواعد إختيار على اإلنشاءات ميدان في تنشط التي والخاصة الحكومية
القواعد لهذه مضرة تكون ما عادة والتي السطحية الصخرية الطبقات لهذه العشوائية التوزيعات
التذبذب .واألساسات اإلعتبار بعين باألخذ المقترحة الرقمية النمذجة باستكمال المشروع هذا يوصي
تعتمد التي المواد خصائص على تأثر التي واألمالح للمواد المحملة الجوفية المياه منسوب في الحاصل
, اإلستكشافات تطوير كيفية على القريب المستقبل في التركيز يتعين كما القواعد هذه إلنجاز
المعلومات لنظم المكانية البيانات قاعدة مع وإدماجها واألساسات القواعد لتصميم الجيوتقنية
مدينة وسط في الثقيلة المباني قواعد تصميم بخصوص القرارات أفضل أخذ على للمساعدة الجغرافية
.الرياض
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ABSTRACT
During the last decade, the continued demand to build heavy buildings in the central area of Al
Riyadh city requires more attention by doing a geotechnical investigation in order to design an
optimized foundations based on geo-database and mapping that have been indexed in neighboring
buildings for future projects. However, to design the foundations of heavy buildings we need to
explore the shallow rock stratum layers using boreholes up to 20 meters in order to know the
thickness of each layer and their geotechnical proprieties before designing its foundations type. For
this reason, the owner of these projects must be chosen engineering consultant office to design these
foundations according to the civil engineering specifications. According to the field investigation and
the data from the boreholes which were included in various corners of the site, we observe that there
are a difference in the geotechnical proprieties from one borehole to another concerning the thickness
of layer and also the chemical composition of these layers as well as the geotechnical characteristics
of these layers, that why we intend to apply for this project a new method such as GIS application in
order to predict the 3D distribution of the rock stratum layers thickness based on the geo-statistics
modeling implemented within the GIS software. As an important results involved within this project,
we note the randomly distribution of the rock stratum layers thickness which have no conformity of
geotechnical proprieties with the best choice of heavy buildings foundations which were adopted by
the designer, however, it is necessary to look for a new methods allow to develop and support
geotechnical investigations, including the indirect applied geophysics methods. In addition, referring
to the randomly geotechnical proprieties distribution of the rock stratum layers thickness, it is
necessary to build a geo-database using GIS software taking into account many sources (Satellite
images, GPS, field visits to the site, and so on…) which help public and private companies that are
active in the construction field to help to take decision for better designing the heavy buildings
foundations and to avoid many reasons of foundations failure after construction phases. Finally, we
recommend to complete these findings involved in this project by taking into account the
groundwater phenomenon at shallow depth where the reinforcement foundations can be affected by
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salts or any other chemical elements associated with groundwater surface. As well as the need to be
in the short term to review and develop the required geotechnical investigations which can be
integrated with the spatial database in order to help to take better decisions when we design the
heavy buildings foundations in Al Riyadh city.
INTRODUCTION
Al Morouj District takes part of Al Riyadh city which is located in the central region of Saudi
Arabia. It is around 1 km2 in the north of Al Riyadh (Fig. 1). During the last decade, the Al Riyadh
city has experienced significant population growth specially in the northern area (Al Morouj district).
However, we observed a high consumption of quantities of drinking water according to this
important demographic growth. For this reason, the water company continues the completion of the
drinking water network of the city of Al Riaydh using ductile iron and PVC pipelines to supply
drinking water to all districts specially Al Morouj. In fact, National Water Company attached a great
importance to fight against the unexpected head-loss within the water supply network in this district.
There is need to explain the true scenario by digital modeling and GIS based on the geospatial data
from satellite images. Since the loss of flow is the most frequent problem, the National Water
Company has been focusing its attention to flow and pressure calculation as one of the priority tasks
to be accomplished in order to avoid the loss of flow close to the urban area.
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CHAPTER I :
Methodology and Literature Review
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I - 1 – Study area :
The site chosen for this application is the “Central National Museum” at Morabaa district in Riyadh
City. The general layout of the site is shown in Figure (1). Its area is about 202410 square meters.
The site is covered by 30 boreholes with depths varies between 10 and 20 meters distributed in the
area of the project as shown in the Figure. The spatial distribution of the boreholes locations in the
site was transferred to a digital plan by means of scanning the general layout. Then each borehole
location were digitized in a point data layer within the GIS software ArcGIS. Boreholes layers
description were collected from the soil report and summarized. In order to facilitate the analysis of
the soil type layer distribution, the detailed soil description is reduced to 5 main types (coded as 1, 2,
3, 4, and 5) that are common in most of soil logs. They are: Sand, gravel, clay, limestone, and
mixture of limestone sand & clay. Table (1) shows the reduced layers description for each borehole.
The database table to be used for the analysis is constructed from: Borehole ID, layer type, depth of
layer, elevation of layer, and borehole description. The number of layers was limited to 5 layers
along the borehole depth. The elevation of the upper surface of the top layer for each borehole was
taken from the soil report and the elevation of subsurface layers is calculated accordingly using the
depth of each layer. Table (2) shows the data after editing through the ArcGIS as attribute file.
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Fig. 1 : Al Riyadh city1.
Fig. 2 : The Central National Museum.
1 Satellite Image GeoEye-1 from Research Institute of Space in King Abdelaziz City for Science and Technology
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I - 2 – Project problem :
National Water Company attached a great importance to fight against the unexpected head-loss
within the water supply network in this district. There is need to explain the true scenario by digital
modeling and GIS based on the geospatial data from satellite images. Since the loss of flow is the
most frequent problem, the National Water Company has been focusing its attention to flow and
pressure calculation as one of the priority tasks to be accomplished in order to avoid the loss of flow
close to the urban area in. For this reason, we intend to expect many head-loss within the water
supply network in Al Morouj district. We distinguish the following problems :
Unexpected head-loss within the water supply network in this district taking into account the
internal and external factors linked to this network;
The loss of flow is the most frequent problem regarding the high level of water consumption during
the last ten years;
The age of the network and the degradation due to excessive loading nearby the network2 allow to
create a zone of failure around the pipelines.
I - 3 – Project objective :The project objective is to resort to new technology (Modeling and GIS) applications in order to help
to take decision by calculating the head-loss within the network, by estimating the excessive stresses
due to the loading surface and by designing a Digital Mapping and building Geo-database in order
to identify with more accuracy the risk zones according to the underground water supply network in
Al Morouj district. At the first, we shall investigate the area of study by visiting many locations to
make a measurement in different points of the network (Pressure and Flow). After that we shall
introduce the network within the EPANET 2 software to estimate head-loss and its distribution
within the water supply network. At the third time, we shall compare the above results with those
calculated by the analytical hydraulic formulation given by teacher in the class. Finally, we shall
propose an addition geospatial data using GIS software in order to integrate multi-layers overlapping
with the water supply network. This procedure allows to help for decision to find the multi-leakage
in the system when introducing updated data function time.
2 Depending the ground the maximum depth is 1 m
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I - 4 – Project methodology : This applied project methodology that will be adopted consist of :
At the first a literature review synthesis concerning the water resources for water supply, using
methods to finding leakage within the network and new technology3 used to monitoring and
controlling the head-loss and degradation in term of soil and rock mechanical modeling.
Second, we shall propose a methodology to estimate the head-loss in the network using hydraulic
software (EPANET 2) and compare the results with those observed in the field in different points of
the network.
In the field and in order to know the true geometry of these underground network a surveying
measurements will be conducted and also to know the depth of each single pipeline.
After collecting data available and consulting the geo-database in GIS of the National Water
company, we propose the methodology to improve the GIS application for water supply network
monitoring and controlling and to help to take decision.
Finally, a recommendations will be proposed to continue to improve this content of this project
regarding the geophysics aspect to detect the damage within the underground pipes network by a
new project involving new group of students for the next semester of this academic year 2013 - 2014.
I - 5 – Literature review :During the past two or three decades, the expert systems are of the major targets in all fields of
science. Using the advantage of computers, all necessary data about a subject is stored and reformed
using the concerning knowledge of its characteristics for future use. Expert systems have been
developed during that period for many of geotechnical applications such as site investigation
planning, soil classification parameter assessment, choice of type of foundation, and many others.
Some of these systems aimed to develop inference of the deposition patterns for subsurface layers by
means of interpreting the field and laboratory data such as Rehak, et al. (1985), Lok (1987) and
Adams et al. (1989) which provides two dimensional subsurface profiles. Also a knowledge based
system developed by Olephant et al (1996) provides two dimensional interpretations of the ground
conditions. An integrated GIS and knowledge based geostatistical system is developed by Adams
and Bosscher (1995) enables to view and retrieve the subsurface data. The system can provide
inference of soil formation and properties at any location within the area of data.
3 Application of software for help to decision
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“Geotechnical Expert System for the Arab Republic of Egypt” GES-ARE is developed jointly by the
General Authority for Educational Buildings and the Soil Mechanics and Foundations Research
Laboratory of Cairo University, includes three modules: soil matching, site wide interpretation, and
the proximity modules. It is mainly relying on similarity numbers between soil layer for each of the
soil features: type, consistency, and color, (Youssef and Elkhouly, 2000). With 3D interpolation
program such as ArcGIS, a more realistic approach for the extrapolation of a preliminary
geotechnical report can be conducted. Such computer programs provide spatial continuity of the
data, which eventually will enhance the quality of the recommendations of the geotechnical engineer.
The enhancement may result from the followings:
● Human errors are minimized in extrapolating the point–to–point data coming from the borehole
readings.
● The ability to verify the reliability of the obtained extrapolated results will be enhanced and can be
conducted at any point in the site.
● The engineering decisions may be easier since the overall picture of the site is available.
Geographic Information System GIS has been widely used recently in the field of geotechnical
engineering. The capability of the GIS system and its main advantage of dealing with database of
spatially distributed points meet the characteristics of soil boring logs information. GIS has been
defined as “a fundamental and universally applicable set of value-added tools for capturing,
transforming, managing, analyzing, and presenting information that are geographically referenced
(Tim, 1995).” In preliminary geotechnical site evaluations, GIS can be used in four ways: data
integration, data visualization and analysis, planning and summarizing site activities, and data
presentation. GIS can be employed in this field to create a model of the geotechnical conditions and
consideration facing a project through preliminary geotechnical site investigation. The model is then
used to analyze the project from the geotechnical point of view and decisions are made to prevent
foundation problems (Player, 2000). For example In Saudi Arabia about ***% of contractors claims
in projects are due to soil problems appeared at time of foundation constructions (Hesham, 2003). A
successful preliminary investigation may result in significant cost savings in design, construction,
and longevity of the project (Player, 2000). New Jersey Department of Transportation (NJDOT)
Bureau of Geotechnical Engineering maintains a large database of boring location plans and
corresponding test boring logs. They conducted a successful pilot study to investigate the
development of a GIS to better manage and disseminate soils information, as developed from test
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boring results (Williams, et al. 2002). That makes it easier to obtain information regarding soil types
at a specific project location. The main procedure used in expert geotechnical system is to benefit
from the extended soil data available at different points spatially distributed to interpolate or
extrapolate the subsurface soil layers in regard of type and characteristic. One of the important
parameters in the knowledge base that used in such system is the depth distribution of different soil
strata. Previous application was made by (Bahr and Aguib 2003), considered the distribution of two
layer types along a study area for Riyadh city in Saudi Arabia. Depths distribution among the boring
points as processed by 3D Analyst (an ArcGIS extension) depended on the TIN network. However,
modeling complex subsurface data as 3-dimentional body, merely surfaces are not supported by 3D
Analyst of the GIS software ArcGIS (Rush, 1999). That was a limitation to that procedure. In
addition this method considers soil types characteristic changes gradually from a type to another,
which is not the case spatially wise. Therefore methods of interpolations for soil types may not give
good results.
I - 7 – Time table of the projects :● Project I : Methodology and literature review.
FIRST TERM (2013) : Project I
N°TASKS REQUIRED
Months
123456
1Literature review
2Collect data from field by surveying
3Acquisition data and spatial data requirement
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● Project II : Water supply network modeling works.
SECOND TERM (2013) : Project II
N°TASKS REQUIRED
Months
123456
1Running Water EPANET 2 software.
2Building Geo-database based on satellite Image GeoEye-1 with ArcGIS10 software.
3Comparison between analytical and modeling has been performed .
4Writing a final report.
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CHAPTER II :
GEOSTATISTICS METHODS
OF INTERPOLATION
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II - 1 – Introduction :There are two main groupings of interpolation techniques: deterministic and
geostatistical. Deterministic interpolation techniques create surfaces from measured
points, based on either the extent of similarity (e.g., Inverse Distance Weighted) or the
degree of smoothing (e.g., Radial Basis Functions). Geostatistical interpolation
techniques (e.g., Kriging) utilize the statistical properties of the measured points. The
geostatistical techniques quantify the spatial autocorrelation among measured points
and account for the spatial configuration of the sample points around the prediction
location. Deterministic interpolation techniques can be divided into two groups, global
and local. The Geostatistical Analyst provides the Global Polynomial as a global
interpolator and the Inverse Distance Weighted, Local Polynomial, and Radial Basis
Functions as local interpolators, (ESRI).
II - 2 – Interpolation methods :Mathematically, the formula used in the IDW method to calculate Z variable for a kernel (the point whose third dimension is to be interpolated) is:
Z( x , y )=[∑i=1
n
(Z i /r ip )/∑
i=1
n
rip ]
(1)
Where
Zi is variable at point i,
n is the number of points in the neighborhood,
ri is the distance from the kernel to point i,
p is the weight exponent that allow very distant points to be penalized with respect to closer ones. The effect of the exponent upon interpolation accuracy has already been investigated (e.g. Declercq, 1996). An exponent p of value 1 or 2 is usually preferable (Yang and Hodler, 2000).
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Kriging is similar to IDW in that it weights the surrounding measured values to derive a prediction for an unmeasured location. The general Kriging formula for interpolation is formed as a weighted sum of the data:
Z( x , y )=∑i=1
n
λiZ i (2)
Where
Zi is the measured value at the ith location;
λi is an unknown weight for the measured value at the ith location;
s0 is the prediction location; n is the number of measured values.
In IDW, the weight, λi, depends solely on the distance to the prediction location. However, in Kriging, the weights are based not only on the distance between the measured points and the prediction location but also on the overall spatial arrangement among the measured points. To use the spatial arrangement in the weights, the spatial autocorrelation must be quantified. Thus, in Ordinary Kriging, the weight, λi, depends on a fitted model to the measured points, the distance to the prediction location, and the spatial relationships among the measured values around the prediction location.
In Geostatistical Analyst, RBFs are formed over each data location. An RBF is a function that changes with distance from a location. RBF methods are a series of exact interpolation techniques; that is, the surface must go through each measured sample value. There are five different basis functions: thin-plate spline, spline with tension, completely regularized spline, multiquadric function, and inverse multiquadric function. Each basis function has a different shape and results in a slightly different interpolation surface. RBF methods are a form of artificial neural networks. An example of RBF function is the Thin Plate Spline method which has well been descried by Wahba (1990). It is based on modeling the measurements Z(Si) where Si
=(xi,yi) is a point of coordinates xi, yi in a Domain D as follows:
Z(Si) = f(Si) + ε(Si) , i= 1,......,n (3)
Where n is the number of measurement points (control points) ; f is an unknown deterministic smooth function, and εi are random errors. The function can be estimated by minimizing:
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Σ [ Z(Si) – f(Si) ]2 + J2 (f) (4)
Where
J2 (f) is a measure of smoothness of f calculated by means of the following double integral:
J2 ( f )=∬R2{(δ 2 f /δx2)2+2(δ 2 f /δxδy )+(δ 2 f /δy2 )}dxdy (5)
is the smoothing parameter which regulates the trade off between the closeness of the function to the data and the smoothness of the function.
II - 3 – Conclusions :
Referring to the table and the figure given that the planned water resource projects
until 1450 H compared to supply and demand balance indicates that the consumption
increases from year to year and can reaches 2400 tcmd. This situation allows to give a
great importance to the water supply network in order to minimize the loss of flow
during the distribution and monitoring this network using the new technology
(modeling, GIS, Remote Sensing).
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CHAPTER III :
PREPARATION DATA
AND MANIPULATION
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III - 1 – Introduction :
The site chosen for this application is the “Central National Museum” at Morabaa
district in Riyadh City. The general layout of the site is shown in Figure (1). Its area is
about 202410 square meters. The site is covered by 30 boreholes with depths varies
between 10 and 20 meters distributed in the area of the project as shown in the Figure.
The spatial distribution of the boreholes locations in the site was transferred to a
digital plan by means of scanning the general layout. Then each borehole location
were digitized in a point data layer within the GIS software ArcGIS. Boreholes layers
description were collected from the soil report and summarized. In order to facilitate
the analysis of the soil type layer distribution, the detailed soil description is reduced
to 5 main types (coded as 1, 2, 3, 4, and 5) that are common in most of soil logs. They
are: Sand, gravel, clay, limestone, and mixture of limestone sand & clay. Table (1)
shows the reduced layers description for each borehole. The database table to be used
for the analysis is constructed from: Borehole ID, layer type, depth of layer, elevation
of layer, and borehole description. The number of layers was limited to 5 layers along
the borehole depth. The elevation of the upper surface of the top layer for each
borehole was taken from the soil report and the elevation of subsurface layers is
calculated accordingly using the depth of each layer. Table (2) shows the data after
editing through the ArcGIS as attribute file.
III - 2 – Application procedure :
The first step in this approach was to identify the main soil types that are exit in the
area by studying the boreholes logs in the area. The second step was to generalize the
vertical distribution of the soil strata in each log to the main identified soil types. The
number of soil strata to be presented was taken same as the main soil types identified;
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5 layers to be represented in the system. This number may be changed from an area to
another. Any layer type does not exist at a borehole log was given a zero depth value
in the attribute table. Following to that is the application of the interpolation technique
to the depths of each layer using the known depths at boreholes location, by means of
using Spatial and Geostatistical Analyst extensions. The interpolation was applied also
to the elevation values of the bottom of the lower layer. Then it was applied to the
depth of each of the 5 layers. Following to that was to find the elevation of each layer
by means of adding the derived depths from the interpolation to the top elevations of
the lower layer. Using the Arcscene extension a superimposing display of the 5 layer
on top of each other could represent the spatially continuous distribution of the layers
for the whole area as seen in Figure (2). At the same time the vertical log at any point
in the area could be displayed using the Identification Icon of the ArcGIS, Figure (3).
Figure (4) shows an example of soil type spatial distribution using Kriging
interpolation for layer 2. Figure (5) shows an example of layer depth spatial
distribution using IDW method for Layer 1. Also longitudinal cross section in any
direction that show elevation, depth, and soil type could be displayed by the Spatial
Analyst extension; for example cross section AA in Figure (6). All the aforementioned
information could be drawn for all layers. Three methods of interpolation were applied
to predict depths in testing points. The depths are measured in the field and predicted
from interpolations and are different than the points used for interpolations. Two
deterministic and one geostatistical methods were used: they are IDW, RBF, and
Kriging respectively for depths interpolation, while Kriging only was used for soil
types prediction for each layer. The ArcGIS default parameters for these methods were
used for the sake of comparison. In order to determine the effect number of known log
points on the predicted values of other log points for the area, three sets were used as
training points 40%, 50%, and 60% of the total known points. Accordingly 60%, 50%,
and 40% were used as testing points for each of the three interpolation methods. The
choosing of the locations of the training data was random according to the default of
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the software. Figure (7) shows the results of the average error resulted from the three
methods of interpolation for the three sets of training boreholes points. Chi 2 test for
independent frequencies showed that there are no significant differences between the
measured and predicted depths at the level of 1% for all methods. The maximum value
for the average error in depth based on individual layer reached 1.8 m at layer2 with
40% modeling points using the RBF method. The behavior of the average error from
the three methods is almost the same in all cases except for the average error of layers
2 and 3 resulted from the RBF method at the case of using 50% of points. Figure (8)
shows that the average error for the five layers is minimal when using 50% of the
points for modeling the area for the three methods. Using 60% as training ratio
resulted higher and positive average error, which means that predicted depths are
smaller than measured. In the contrary when using 40% of points as training, it gave
negative average error; which means that the predicted depths are bigger than the
measured. Therefore 50% of the executed boreholes were adequate to construct strata
depths model for this area with average error ranges between 0.13 and 0.5 m.
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Fig. 6 : Displaying the Information Along Chosen Cross-Sections.
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Fig. 7 : Layers elevation (m) displayed at any location using ArcMap10.
(IDW method)
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Fig. 8 : Mapping distribution of soil type for layer 2 according to Kriging Method.
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Fig. 9 : Mapping distribution of depth for layer 1 according to IDW Method.
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Fig. 10 : Elevation of layer 1 along the cross-section A-A.
Fig. 11 : Depth of layer 1 along Cross-Section A-A.
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Fig. 12 : Type of layer 1 along Cross-Section A-A.
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CHAPTER IV :
COMPARISON BETWEEN
APPLIED METHODS
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IV - 1 – Introduction :
GIS approach proved to be a useful technique that could be used in mapping and
enhancing the soil information. By means of interpolating from points data spatial 3D
information could be developed using the ArcGIS software and its extensions (Spatial,
3D, Geostatistical analyst and ArcScene). Information at any location in the site could
then be identified. It was found that; using interpolation methods for soil type could
not be applied using such simple coding system. That was due to the fact that the
interpolation gives value for any new point lies in between its neighbor codes.
Behavior of natural soil type distribution allows for any soil type to be neighbors and
is not limited to the one with next higher or lower code. Therefore, additional studies
concerning reasonable coding for soil types that suit the interpolation approach, are
suggested for better predicting soil types through the use of GIS technique.
III - 2 – Comparison between applied methods :
The three methods of interpolation; IDW, RBF, and Kriging promise to give good
results when applied to predicting depths of soil layers. For the site used for this study
(30 boreholes) three sets of training points (12, 15 and 18 boreholes) were used in
predicting the rest (18, 15 and 12 respectively). The maximum average error in layer
depths reached 1.88 m as absolute figure when using RBF method for modeling the
area using 40% of the data. The best interpolation method was the IDW, where the
average error in layer depths ranged from 0.13 to 0.16 m. However, one can expect
better results if a study made concerning the distribution of the chosen training points
in the site. Also there are several parameters in the interpolating methods which need
additional investigation to be determined precisely, for better results. For example, the
distance power, radius of searching data and number of used surrounding points used
in the IDW. Also determining the barrier limits to be respected when searching for
data. For this site, in general when using 50% of the points to predict the other points
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the results showed the minimum average error in the depths for the three methods of
interpolation; 0.13, 0.26 and 0.16m for IDW, RBF and Kriging respectively. On the
other hand for all the methods used, when using 40% of the points to predict the rest,
the average error had negative values: - 0.49, -0.50 and -0.54m. This result means that
the predicted depths are larger than the measured ones. When using 60% of the points
to predict the rest, the average errors were, 0.58, 0.62 and 0.56 m, which mean that the
predicted depths were smaller than the measured. It can be said that the IDW method
of interpolation is the best for constructing the spatial strata depths model for this site
using 50% of the measured data. For all layer depths determined by different methods
the Chi2 test showed no significant differences between measured and predicted values
at the level of 1%. One would say that average errors when using 60% of the data
were bigger due to random choice of the location of the controlling data points that
might not be well distributed allover the site. Generally one must recall that all
interpolation techniques are some type of estimation that should not be taken as exact
values, but as a rough guide specially when needed for foundation or structure design.
Fig. 13 : Average depth error for predicted points in each layer using three methods
(40 % of points).
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.
Fig. 14 : Average depth error for predicted points in each layer using three methods
(50 % of points).
Fig. 14 : Average depth error for predicted points in each layer using three methods
(60 % of points).
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Fig. 6 : Average depth errors for all layers by different methods of interpolation.
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CHAPTER IV :
GIS APPLICATION FOR MAPPING
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IV – 1 – What is GIS :
GIS technology combines mapping software with database management tools to
collect, organize, and share many types of information. Data is stored as thematic
layers in geo-database (data identified by its location coordinates) that can be accessed
and shared from the field, within a department, and across an entire enterprise. You
decide which layers are relevant. Utilities typically combine utility layers with land
base, parcel, street, land-use, and administrative area layers.
Figure VII - 1 – Representation of geographic information.
Geographic information can be represented with geometric information such as
location, shape and distribution, and attribute information such as characteristics and
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nature, as shown in Figure VII – 1. Vector and raster forms are the major
representation models for geometric information. The data structure is specified for
the vector form as follows : a point is represented by geographic coordinates. An edge
is represented by a series of line segments with a start point and end point. A polygon
is defined as the sequential edges of a boundary. The inter-relationship between
points, edges and areas is called a topological relationship. Any change in a point,
edge or area will influence other factors through the topological relationship.
Therefore the data structure should be specified to fulfill the relationship, as for the
example as shown in Figure VII – 2.
Figure VII - 2 – A basic data structure of vector data (topological relations).
In the raster form, the object space is divided into a group of regularly spaced grids
(sometimes called pixels) to which the attributes are assigned. The raster form is
basically identical to the data format of remote sensing data. As the grids are generated
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regularly, the coordinates correspond to the pixel number and line number, which is
usually represented in a matrix form as shown in Figure VII – 3.
Figure VII - 3 – A basic data structure of raster data.
Data acquisition occupies about 80 % of the total expenditure in GIS. Therefore data
input and editing are very important procedures for the use of GIS. Geometric data as
well as attribute data are input by the following methods :
1 – Direct data acquisition by land surveying or remote sensing : vector data can
be measured with digital survey equipment such as total station or analytical
photogrammetric plotters. Raster data are sometime obtained from remote sensing
data.
2 – Digitization of existing maps : Existing maps can be digitized with a scanner or
tablet digitizer. Raster data are obtained from a scanner while vector data are
measured by a digitizer. In GIS, raster data and vector data are frequently converted to
vector data and raster data respectively, which are called raster/vector conversion and
vector/raster conversion respectively (Figure VII – 4).
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Figure VII - 4 – A process of input / update of geographic data.
IV – 2 – GIS used for Mapping :
Utility keeps track of vast amounts of information about assets; distribution,
collection, and drainage networks; customers; and financial records. All this
information has a connection with location, whether it be the site of a water main or a
customer’s meter. Geographic information system (GIS) technology uses these
geographic connections to integrate key database systems, streamline asset data
management tasks, and help to visualize important geospatial relationships. Using GIS
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helps customer more effectively manage water distribution, sewer collection, and
storm water drainage networks as well as related planning and customer care. We can
think of a GIS as a “location-based operating picture” that unifies the databases
essential to diverse activities. Significantly more powerful and flexible than a
computer-aided design (CAD) system, a GIS stores both attributes and images of
pipes, valves, meters, manholes, and so forth, as objects with location coordinates. The
maps created link upstream and downstream objects through strong object-to-object
network connectivity and indicate normal flow direction through the pipe network.
GIS is a true model of the network and can be used to :
● Track and report on assets in the network inventory;
● Generate inputs into hydraulic modeling software;
● Create a common operational picture for access to network operations information.
In creating a single database of assets, we can eliminate redundant data collection and
maintenance activities. The shared database enables engineering to produce maps,
finance to calculate asset valuations, maintenance to track work activities, and
operations to create network models. GIS technology also uses geographic
relationships to link and merge disparate databases. For example, we can place a
demographic layer projecting future population values on top of an existing sewer
manhole layer and use it to estimate future loadings at specific nodes on the network.
We can also overlay water well data on hazardous material information to determine
proximity and assess contamination risks.
IV – 3 – Sharpen Planning and Engineering Analyses :
GIS software (ArcGIS10 for example) allows you to represent a project in three-
dimensional form and visualize the impact of facilities on the landscape during the
design process. Data can be combined with other computer-aided engineering
functions to assist in the planning and scenario testing of multiple designs.
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Water agencies use GIS software to map the full extent of their water
distribution systems and link them to a database, defining each element including
reservoirs, pipe segments, services, and system appurtenances. As a result, job
planning, equipment inventory, and flow analysis become automated procedures
integrated into one intelligent database.
Planning and engineering tasks that you can accomplish easily using GIS software
include :
● Watershed and groundwater management modeling;
● Water distribution system master planning;
● Population and demand projections;
● Water quality monitoring;
● Hazardous materials tracking and underground tank management;
● Well log and data management;
● Site analysis;
● Geo-bibliography (past studies);
●Development review and approval;
● Right-of-way engineering;
● Automated mapping;
● Capital improvement project tracking;
● Underground service alert.
IV – 4 – Conclusions :
In this chapter we have focused to learn GIS software and built the Geo-database
concerning the water supply network of Al Morouj. Our work involves the following
parts :
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● Acquisition spatial data : To build this geo-database, satellite image GeoEye has
been taken from Space Research Institute in King Abdelaziz City for Science and
Technology, this image is characterized by high level of accuracy which is 0.5 m by
PIXEL4.
● Geo-referencing : To taking into account the geographical coordinates in the map
using Project system5 the water supply network has been displayed as layer on the
satellite image (Figure VII - 3).
● Attribute data : To introduce all attribute data characterizing the different layers in
the Geo-database, a database has been introduced within the ArcGIS10 software in
which all parameters linking to the network saved in the system (Figure VII – 4 & 5).
● Design of layers : To taking into account the internal and external factors
overlapping with water supply network, it is necessary to design all layers in the Geo-
database in order to analyze the given problem according the multiple aspects. For this
reason some internal and external layers have been introduced in this Geo-database
such as junction, linked points, parcels. (Figure VII – 6 & 7 & 8).
V - CONCLUSIONS :
4 The small element of image called PIXEL (Picture Element)
5 UTM (Universal Transverse Mercator) zone 38
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The National Water Company gives a great importance for the maintenance and the
monitoring of the drinking water network. In the sense, efficient monitoring of water
distribution networks have long been a challenge for management, even in countries
with a well-developed infrastructure and good operating practices. Improperly
managed water networks might result in increased cost of supply, insufficient supply
of potable water, inconvenience, not satisfied customers and more. Such problems
might not only be caused by operating a poorly maintained infrastructure but also by
excessive use or misuse of water due policy of some governments to provide low
tariffs for water usage. Referring to the second chapter, the future planned water
resource projects until 2040 compared to supply and demand balance indicates that the
consumption increases from year to year and can reaches 2400 tcmd. This situation
allows to give a great importance to the water supply network in order to minimize the
loss of flow during the distribution and monitoring this network using the new
technology (modeling, GIS, Remote Sensing). Referring to the third, fourth, fifth and
sixth chapters, Hydraulic simulation modeling using EPANET 2 computes junction
heads and link flows for a fixed set of reservoir levels, tank levels, and water demands
over a succession of points in time. From one time step to the next reservoir levels and
junction demands are updated according to their prescribed time patterns while tank
levels are updated using the current flow solution. The solution for heads and flows at
a particular point in time involves solving simultaneously the conservation of flow
equation for each junction and the head loss relationship across each link in the
network. This process, known as “hydraulically balancing” the network, requires using
an iterative technique to solve the nonlinear equations involved. EPANET 2 employs
the “Gradient Algorithm” for this purpose. Referring to the last chapter, we have
focused to learn GIS software and built the Geo-database concerning the water supply
network of Al Morouj. Our work involves the following parts :
● Acquisition spatial data : To build this geo-database, satellite image GeoEye has
been taken from Space Research Institute in King Abdelaziz City for Science and
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Technology, this image is characterized by high level of accuracy which is 0.5 m by
PIXEL6.
● Geo-referencing : To taking into account the geographical coordinates in the map
using Project system7 the water supply network has been displayed as layer on the
satellite image (Figure VII - 3).
● Attribute data : To introduce all attribute data characterizing the different layers in
the Geo-database, a database has been introduced within the ArcGIS10 software in
which all parameters linking to the network saved in the system.
● Design of layers : To taking into account the internal and external factors
overlapping with water supply network, it is necessary to design all layers in the Geo-
database in order to analyze the given problem according the multiple aspects. For this
reason some internal and external layers have been introduced in this Geo-database
such as junction, linked points, parcels.
VI - RECOMMANDATIONS :
This applied project has been focused on Hydrologic modeling techniques, soil and rock modeling and GIS Geo-database. Some results have been undertaken to manage the head-loss within the water supply network in Al Morouj district. We recommend continuing this study project by introducing spatial analyst in GIS aspects in order to simulate the 3D distribution of water in the network. In order to design the hydraulic solutions works according the present network a Civil Engineering study will be required in order to choose the optimum depth and materials used for good stability of these pipelines in the network.
6 The small element of image called PIXEL (Picture Element)
7 UTM (Universal Transverse Mercator) zone 38
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VII - REFERENCES :]1[ Adams T.M., & Bosscher, P.J. (1995), “Integration 0f GIS_And_Knowledge-
Based Systems for Subsurface Characterization", Expert Systems for Civil Engineers: Integrated & Distributed Systems (eds Maher, M.L. & Tommelein, I.), New York: ASCE.
]2[ Adams T.M., Christiano P. & Hendrickson C. (1989), "Some expert system applications in geotechnical engineering", Foundation Engineering: Current principles and practices, ASCE: New York, pp 885-902.
]3[ Bahr, M. and A. Aguib, 2003. “Utilization of Information Technology (IT) for Mapping the Subsurface Soil Description” Civil Engineering Research Magazine, AlAzhar University, Egypt, 25(1): pp 125-141.
]4[ Declercq, F.A.N. 1996. “Interpolation methods for scattered sample data: accuracy, spatial patterns, processing time”. Cartography and Geographic Information Systems 23(3):pp. 128-144.
]5[ ESRI, Manual of ArcGIS version 8.2
]6[ Lok M. (1987), "LOGS: A Prototype Expert System to Determine Stratification Profile from Multiple Boring Logs", Master's thesis, Carnegie Mellon University, Pittsburgh.
]7[ Olephant, J., Ibrahim, J.A-R- & Jowitt, P.W. (1996), "ASSIST: A Computer-based Advisory System for Site Investigations", Proc. Instn. Civil Engineers Geotechnical Engineering, 119, pp 109-122.
]8[ Player R., 2000, “Using GIS in Preliminary Geotechnical Site Investigations for Transportation Projects” 174 mid-continent transportation symposium 2000 proceedings
]9[ Rehak D.R-, Christiano P.P. & Norkin D.D. (1986), "SITECHAR: An Expert System Component of a Geotechnical Site Characterization Workbench", Applications of Knowledge-based Systems to Engineering Analysis and Design (ed. Dym C.L.), Am Soc. Mech. Eng.: New York, pp 117-133.
]10[ Rush, J. W. 1999. “Integration of GIS and a High-Resolution Sequence Stratigraphic Framework”.www.utexas.edu/depts/grg/hudson/grg394k/studentprojects/rush/rush.html (2003-08-12)
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]11[ Tim, U.S., 1995, “ The Application of GIS in Environmental Health Sciences:Opportunities and Limitations”. Environmental Research, 71, 11, 1995,pp. 75-88.- Youssef, S., and M. A. Elkhouly 2000, “ A Rule Based Knowledge System for Interpreting Ground Stratification from Borehole Information” The Fourth International Geotechnical Engineering Conference – Cairo University- Egypt, 24 -27 January,2000.
]12[ William T., P. Snary, T. Thomann, C. Konnerth, and E. Nemeth, 2002, “GIS APPLICATIONS IN GEOTECHNICAL ENGINEERING” FINAL REPORT New Jersey Department of Transportation CN 600 Trenton, NJ 08625 Federal Highway Administration U.S. Department of Transportation Washington, D.C.
]13[ Wahba, G. 1990.”Spline models for observational data”. CBMS-NSF Regional Conference Series in Applied Mathematics, SIAM, Philadelphia, PA, USA, 169 pp.
]14[ Yang, X. and T. Holder, 2000. “Visual and Statistical comparison of surface modeling techniques for point-based environmental data”. Cartography and Geographic Information Science, 27 (2): pp 165-175.
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APPENDIX I :
Satellite Image GeoEye of the study area
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VIII – APPENDIX I : Satellite Images
Fig. A - I - 1 – Satellite Image LandSat -7 (2014).
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Fig. A - I - 2 – Satellite Image GeoEye -1 (2014).
TABLE OF CONTENTS
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ACKNOWLEDGEMENTS...................................................................................................................1
2.....................................................................................................................................................ملخص
ABSTRACT...........................................................................................................................................3
INTRODUCTION..................................................................................................................................5
CHAPITER I : METHODOLOGY AND LITERATURE RVIEW
I - 1 – Study area :..................................................................................................................................7
I - 3 – Project problem :.........................................................................................................................9
I - 4 – Project objective :........................................................................................................................9
I - 5 – Project methodology :................................................................................................................10
I - 6 – Literature review :.....................................................................................................................12
I - 7 – Time table of the projects :........................................................................................................13
CHAPITER II : WATER RESOURCES FOR AL RIYADH
II - 1 – Existing water resources :........................................................................................................15
II - 2 – Future water resources :...........................................................................................................32
II - 3 – Conclusions :............................................................................................................................37
CHAPITER III : THEORY OF LEAKAGE AND CONTROL
III - 1 – Introduction :...........................................................................................................................39
III - 2 – Control of leakage using DMA :.............................................................................................39
III - 3 – Theory of DMA management :...............................................................................................40
III - 4 – Control of leakage using DMA Design Network :.................................................................41
CHAPITER IV : WATER PLANNING SUPPLY AND CRITERIA
IV – 1 – Introduction :..........................................................................................................................45
IV – 2 – Peak Factors :.........................................................................................................................45
IV – 2 – Diurnal Variation :.................................................................................................................48
IV – 3 – Pipe Materials used in the network :......................................................................................52
IV – 4 – Design standard for pipe materials :......................................................................................56
CHAPITER V : PIPE NETWORK THEORY AND SIMULATION
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V – 1 – Pipe Networks Theory :...........................................................................................................60
V – 2 – Analytical Calculation for Pressure :......................................................................................64
V – 3 – Simulation using EPANET 2 :................................................................................................68
V – 4 – Results and interpretations :....................................................................................................68
CHAPITER VII : GIS METHODOLOGY APPLICATION
VII – 1 – What is GIS :........................................................................................................................94
VII – 2 – GIS used for Water supply :.................................................................................................95
VII – 3 – Sharpen Planning and Engineering Analyses :.....................................................................97
VII – 4 – Conclusions :........................................................................................................................98
VIII - CONCLUSIONS :...................................................................................................................102
IX - RECOMMANDATIONS :.........................................................................................................104
X - REFERENCES :..........................................................................................................................105
APPENDEX
XI – APPENDIX I :...........................................................................................................................108
XII – APPENDIX II :.........................................................................................................................111
XIII – APPENDIX III :......................................................................................................................113
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