dc.description.abstract | Due to the economic boom in Taiwan in recent years, transportation and communication increases daily and the airport pavement load also becomes heavier. How to maintain and extend the life of pavement has now become one of the major topics in the transportation engineering department. However, since air cargo is almost at its saturation, it is not advisable to close the airport for a long period of time to repave the field. Therefore, how to repair the distressed pavement in time and effectively has now become the major issue of maintaining the pavement at the airport effectively.
Repair of distressed pavement at the airport in Taiwan at present mostly relies on the accumulated experience of repair experts or on the repair material suggested by the material supplier(s). There is still no set of perfect and practical pavement repair criteria to be followed. The main reason for poor pavement repair at the airport in Taiwan was that repairs were done without understanding and diagnosing the cause of distress or adopting improper repair method and materials. Also, it lacks a complete file record after repairing the pavement, so there is no way of accumulating repair experience and unable to evaluate the results of repair methods and materials. As a result, there is frequent repetitious repair failure, which is also one of the main reason of poor pavement repair at the airport in Taiwan.
The purpose of this study is to create an airport pavement repair system, that is able to process, store, and show the attribute data and spatial data of airport pavement by collecting and integrating the knowledge and experience of airport pavement repair experts. First, fuzzy logic was applied to do the inferring, based on the knowledge of experts, and create a fuzzy inferring mode of diagnostic expert system for the cause of pavement distress at the airport, to enable the system to process uncertain problems of experts on the degree of rigid pavement distress at the airport, in order to improve the restrictions on conventional expert system and thus diagnose a more rational and accurate cause. The machine learning of neural network theory was also applied in the experts’ questionnaire survey, and then study and classify the cases in order to accumulate the knowledge for the advantage of giving suggestions to repairs on field distress in the future and be able to choose an appropriate plan in order to ensure effect of pavement service. Innovative and appropriate repair materials were also taken in account. The function of feedback learning on repair materials was simultaneously created in this study to enable the system to keep on learning any time in order to ensure the suitability of repair materials.
In this study a set of GIS & GPS pavement survey method was also developed to conduct a total and temporary pavement survey on the airport pavement in order to deal with the characteristic of business operation at the airport.
Initially the airport pavement repair system that was constructed in this study already has the function of diagnosing the cause of pavement distress, suggesting repair plan, and storing and showing the subsequent repair records of each block. Upon practical field application, the pavement survey method developed is more convenient and faster than the conventional pavement survey method.
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