Analysis of rigid pavement distresses on interstate highway using decision tree algorithms

This paper describes the application of two decision tree algorithms, Logistic Regression Trees with Unbiased Selection (LOTUS) and Classification Rule with Unbiased Interaction Selection and Estimation (CRUISE), to identify prevailing distress types that affect pavement maintenance decision making. The maintenance history of the selected sections within Interstate highway I-90 in the State of Wisconsin is modeled to build decision trees. The historical field distress survey data include the types, quantities, severities, and locations of distresses. The primary objective of this paper is to present the decision tree algorithms that may be incorporated into the decision making process and help state highway agencies make rational decisions during the course of pavement management. The paper discusses the model development process and prevailing distress identification procedures employing the decision tree algorithms. A case study is conducted on another Interstate highway, I-43, with a view to furthering the analysis of the results from the two algorithms. These analysis models and the results will provide the state highway agencies and practitioners with a means to identify prevailing distress types in a specific location and facilitate making decisions on future maintenance practices.