Dissertation/Thesis Abstract

A data mining approach for identifying pavement distress signatures
by Bouret, Megan Sue, M.S., North Dakota State University, 2015, 47; 1606201
Abstract (Summary)

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.

Indexing (document details)
Advisor: Denton, Anne
Commitee: Lu, Pan, Salem, Saeed, Yan, Changhui
School: North Dakota State University
Department: Computer Science
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
Publication Number: 1606201
ISBN: 978-1-339-37911-1
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