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A mechanistic-empirical approach to characterizing subgrade support and pavement structural condition for network-level applications /Murphy, Michael Ray, January 1998 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 461-498). Available also in a digital version from Dissertation Abstracts.
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Application of neural networks in pavement managementBredenhann S. J. 03 1900 (has links)
Thesis (MEng)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: The intent of this thesis is to examine the solving of problems with neural networks. Three cases are
investigated: the calculation of a Visual Condition Index (VCI), the determination ofthe reseal need, and the
back-calculation of E-moduli from measured deflection basins.
The calculation of a Visual Condition Index (VCI) is a very good example of how a neural network can be
applied to reach a conclusion through the association of a number of facts with one single outcome. VISual
assessments of the road condition are done on a yearly basis and the Assessor gives his impression of the
condition of a road. A neural network simulates the association between the inputs of elements of distress
on the road and the eventual assessment of the overall condition expressed as the VCI, very well.
Reseal need is determined by the Provincial Administration: Western Cape (PAWC) with a Reseal Expert
System. Data produced by the expert system was used to train a neural network to determine the reseal
need. The strength of using these two methods in combination is shown. Meaningful results could not be
obtained due to insufficient data in certain categories.
Deflection measurements with a Falling Weight Deflectometer are meaningful indicators of pavement
strength. Back-calculation is used to calculate E-moduli of pavement layers which can be used in a
mechanistic approach to estimate remaining pavement life from pavement response. Conventional backcalculation
programs, when implemented in a pavement management system, result in very long
computing times due to the large volumes of data available. Neural networks offer the alternative of very
fast processing, making the implementation of back-calculation in real-time possible. It is shown that neural
networks can back-calculate E-moduli, but with varying degrees of success. The main problem identified is
the basis on which the dataset used to train neural networks, is generated using linear elastic theory. The
biggest limitation in the linear elastic theory is that non-linear and stress dependent behaviour of materials
cannot be simulated, two aspects that have a major influence on the back-calculated E-moduli.
Improvements in the data generation process using a theory that accommodates non-linear and stress
dependent behaviour of materials may result in improved performance of the neural networks. It is also
shown that it is very difficult to design a single neural network that can be successfully used on all the
possible pavement types. It is better to identify representative pavement types and train neural networks for
each of these.
Neural networks can be applied with success in the pavement management field and the combination of
Expert Systems, Neural Networks and Fuzzy Logic can be a very powerful method to solve complicated
problems. Care should be taken in the design of the neural networks and a good understanding ofthe data
is a prerequisite for success. / AFRIKAANSE OPSOMMING: Die bedoeling met die tesis is om die vermoë van neurale netwerke om probleme op te los, te ondersoek.
Drie gevalle word beskou: die berekening van 'n Visuele Toestand Indeks (VTI), die bepaling van die
herseël behoefte en die terugberekening van die E-moduli vanaf defleksie metings.
Die berekening van die VTI demonstreer die vermoë van neurale netwerke om,deur middel van die
assosiasie tussen 'n hele aantal veranderlikes tot 'n enkele uitkoms, tot 'n gevolgtrekking te kom. Visuele
opnames van paaie word op 'n jaarlikse basis gedoen waar die opnemer sy indrukke gee van die toestand
van die pad. In Neurale netwerk simuleer die assosiasie tussen die insette (waargenome gebreke) en die
uiteindelike toestands beskrywing van die pad, uitgedruk as die VTI, baie goed.
Die Provinsiale Administrase: Wes-Kaap bepaal die jaarlikse herseëlbehoefte met behulp van 'n Herseël
Ekspertstelsel. Die uitsette van hierdie stelsel is gebruik om 'n neurale netwerk op te lei om die
herseëlbehoefte te bepaal. Die voordele om die twee stelsels saam aan te wend, word getoon.
Betekenisvolle resultate kom nie bekom word nie vanweë onvoldoende inligting in sekere kategorieë.
Defleksiemetings deur 'n vallende-gewig meetapparaat is betekenisvolle indikators van die plaveiselsterkte.
Die E-moduli van die plaveisellae word bepaal deur terugberekenings vanaf defleksiemetings. Hierdie Emoduli
kan gebruik word om met behulp van meganistiese metodes die oorblywende leeftyd van 'n
plaveisel te bepaal. Konvensionele terugberekenings programme, geïmplementeer in In
plaveiselbestuurstelsel, neem lank om die groot hoeveelheid defleksiemetings te verwerk. Neurale
netwerke bied die alternatief van die intydse berekening van E-moduli vanweë die besonder hoë
berekeningspoed wat behaal word. In hierdie tesis word aangetoon dat neurale netwerke aangewend kan
word om die terugberekenigs te doen, maar met 'n wisselende mate van sukses. Die gebruik van die
lineêre elastiese teorie om die data vir die neurale netwerke te genereer, word as 'n probleem
geïdentifiseer. Die grootste tekortkoming wat met die lineêre elastiese teorie ondervind word is dat dit nie
die nie-lineêre en spanningsafhanklike gedrag van materiale voldoende simuleer nie. Beide hierdie twee
aspekte het 'n groot invloed op die akkuraatheid van terugberekende E-moduli. Verbeteringe in die
generering van data deur 'n teorie te gebruik wat nie-lineêre en spanningsafhanklike gedrag van materiale
behoorlik simuleer, mag lei tot 'n beter prestasie van die neurale netwerke. Dit word ook getoon dat dit
moeilik is om 'n enkele neurale netwerk te ontwerp wat suksesvol gebruik kan word op alle plaveiseltipes.
Dit is beter om verteenwoordigende plaveiseltipes te identifiseer en dan neurale netwerke vir elkeen te
ontwerp.
Neurale netwerke kan met sukses in die plaveiselbestuur veld toegepas word en die kombinasie van
ekspertsteiseis, neurale netwerke en vaagheidstelsels (fuzzy) kan tot kragtige metodes lei om komplekse
probleme op te los. Sorg moet aan die dag gelê word met die ontwerp van neurale netwerke en 'n goeie
begrip van die data is 'n voorvereiste vir sukses.
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