Aggregating evidence in pavement management decision-making using belief functions and qualitative Markov tree

Abstract

This paper applies the combined use of qualitative Markov trees and belief functions (otherwise known as Dempster-Shafer theory of evidence), to pavement management decision-making. The basic concepts of the belief function approach-basic probability assignments, belief functions and plausibility functions-are discussed. This paper also discusses the construction of the qualitative Markov tree (join tree). The combined use of the two methods provides a framework for dealing with uncertainty, incomplete data, and imprecise information in the presence of multiple evidences on decision variables. The approach is very appropriate, since it presents more improved methodology and analysis than traditional probability methods applied in pavement management decision-making. Traditional probability theory as a mathematical framework for conceptualizing uncertainty, incomplete data and imprecise information has several shortcomings that have been augmented by several alternative theories. An example is presented to illustrate the construction of qualitative Markov trees, from the evidential network and the solution algorithm. The purpose of the paper is to demonstrate how the evidential network and the qualitative Markov tree can be constructed, and how the propagation of m-values can be handled in the network.