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 of the reseat 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. Reseat need is determined by the Provincial Administration: Western Cape (PAWC) with a Reseat Expert System. Data produced by the expert system was used to train a neural network to determine the reseat 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 back- calculation 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-me 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 identity 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 of the data is a prerequisite for success.
|Subject 2||Civil engineering|
|Degree Type||Masters degree|