This work introduces signature-based data mining of pavement distress data. The goal is to understand the factors that influence pavement distress. The presented approach maintains multiple types of flexible pavement distress scores throughout the analysis and considers them as signatures. The signatures are used to establish the relationship between distress score increases and overweight truck characteristics. Hierarchical clustering of pavement distress signatures provides insights into similarities among road segments. The use of signatures, rather than composite distress scores, is consistent with a data mining approach to the pavement distress problem. One set of experiments showed a relationship between the discovered signature groups and a difference between overweight truck traffic. Group validation has been implemented with Fisher's exact test. Future work related to algorithm improvements have been identified and considered.
|Commitee:||Lu, Pan, Salem, Saeed, Yan, Changhui|
|School:||North Dakota State University|
|School Location:||United States -- North Dakota|
|Source:||MAI 55/03M(E), Masters Abstracts International|
|Subjects:||Transportation planning, Computer science|
|Keywords:||Clustering, Data mining, Distance, Pavement distress, Signature, Transportation|
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