end studies project - psau · web viewيوصي هذا المشروع باستكمال...

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Project II : GIS Application for Constructing Knowledge Base and Representing 3D Geotechnical Data

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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.

N. Al Dawsari – S. Al Harbi – M. Al Rwili – A. Al Ghahtani Academic Year – Second Semester : 2014

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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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.


(50 % of points).


(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


41


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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)


http://www.utexas.edu/depts/grg/hudson/grg394k/studentprojects/rush/rush.html

http://www.utexas.edu/depts/grg/hudson/grg394k/studentprojects/rush/rush.html

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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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