A valuation-based system (VBS) for belief-function theory is applied to pavement management system (PMS) decision-making. The VBS provides a general framework for representing knowledge and drawing inferences under uncertainty. A VBS network is constructed and potentials are introduced in the form of belief functions (or basic probability assignments) in a PMS decision-making environment. A valuation network is another method of representing and solving Bayesian decision problems. It is based on the framework of VBSs. Valuation networks depict decision variables, random variables, utility functions and information constraints. The solution method for the valuation network is called a fusion algorithm, and Dempster's rule of combination can be successfully applied in this framework. It is shown that this approach can capture quantitative, qualitative and incomplete information in PMS decision-making.
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