Application of neural networks in pavement management

Bredenhann S. J. (2000-03)

Thesis (MEng)--University of Stellenbosch, 2000.

Thesis

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.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/51619
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