Search In this Thesis
   Search In this Thesis  
العنوان
Investigating The Factors Affecting Pavement Overlay Service Life\
المؤلف
Elsayed,Marwan Elsayed Abd Elhaffiz
هيئة الاعداد
باحث / مروان السيد عبد الحفيظ السيد
مشرف / حسن عبد الظاهر حسن مهدي
مشرف / خالد أنور أحمد قنديل
مناقش / عيسي عبد الله سرحان
تاريخ النشر
2020.
عدد الصفحات
105p.:
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة المدنية والإنشائية
تاريخ الإجازة
1/1/2020
مكان الإجازة
جامعة عين شمس - كلية الهندسة - اشغال عامة
الفهرس
Only 14 pages are availabe for public view

from 142

from 142

Abstract

No doubt, studying the effect of different factors as traffic loading,
atmospheric temperature, density of rain fall or precipitation, pavement
thickness, etc. on pavement service life is very important to guarantee the
completion of pavements design period safely and as it was planned prior
construction. Different techniques have been proposed to study the effect
of various factors affecting pavement service life. However, most of them
are associated with various drawbacks either in historical data availability
which is considered as a main part in using empirical methods or in
causing damage to pavements as in destructive mechanical methods.
The main objective of this study is to develop a model to study the
effect of different factors on pavement service life. This model studies
the effect of the following factors: Age before overlay, AC thickness,
overlay thickness, age of overlay, international roughness index,
equivalent single axle load, temperature, and precipitation. This model
objective is to overcome the different drawbacks of both empirical and
mechanical methods. This study relies on (Neuro Solution 6) program to build a
network model through which studying the effect of various factors on
pavement service life will be accomplished.
In this model the pavement
survival probability S(t) acts as dependent variable while factors affecting
pavement service life as traffic loading, temperature, precipitation, etc. are the independent variables. Independent variables data for each
pavement section are extracted from General Pavement Studies (GPS 6)
Experiment which is one of the Long Term Pavement Performance
(LTPP) projects while the dependent variable S(t) is calculated using a
third degree regression model .
Survival probability S(t) is firstly calculated using Kaplan-Meier
analysis, a major drawback in this analysis is that the output data is not
described by a known distribution or function. Consequently, A third
degree regression model with the aid of MATLAB program is used to
best describe Kaplan –
Meier output data and be easily used in calculating
S(t) for each pavement section used for building the network model.
The results clarify that the Neural Network model is adequate in
predicting the influence of various factors on pavement service life. The
data extracted from the model can be used to aid in making right
decisions for pavement rehabilitation, overlay design, and pavement
expenses.