Fuzzy decision tree based approach to predict the type of pavement repair
Devinder Kaur, Haricharan Pulugurta · 2007
Data mining is the process of extraction of hidden predictive information from large databases and expressing them in a simple and meaningful manner. This paper explains the use of Fuzzy logic as a data mining process to generate decision trees from a pavement (road) database containing historical pavement information. Generally there are many attributes in the pavement database and often it is a complicated process to develop any mathematical model to classify the data. This paper demonstrates the use of fuzzy logic to generate decision tree to classify the pavement data. The fuzzy decision tree is then converted to fuzzy rules. These fuzzy rules assist decision-making process for selecting a particular type of repair on a pavement based on its current condition. The fuzzy decision tree induction method used is based on minimizing the measure of classification ambiguity for different attributes. The model was developed and tested using the ODOT (Ohio Department of Transportation) data set.