ABSTRACT
Detailed study and accurate analysis of topics such as risk, resilience, redundancy, exposure, consequences, and hazards associated with infrastructure requires adherence to several attributes noted in the following. Improper consideration of these attributes may lead to incomplete and/or inaccurate results and will lead to (wrong) decisions with potential negative consequences. Attributes of interest include the following:
• Realistic and accurate modeling linkages/interactions among variables • Accommodation of uncertainties associated with different variables • Accurate modeling of observations at any given time (snapshot or a time period) • Accommodation of objective and subjective variables and pertinent combinations • Seamless accommodation of decision under uncertainty (DUU) while accounting for all
of the above attributes • Ease of simulating complex models • Ease of changing, adding to, or trimming complex models
Closed-form solutions (Vose 2009), decision tree (Neapolitan 2004), and other tree-related constructs and weighted average models (Fenton and Neil 2013) are some of the possible analytical models that can be used in the studies mentioned earlier. However, each of the methods mentioned earlier can only accommodate some, but not all, attributes mentioned earlier. Probabilistic graph networks (GN) can accommodate all the attributes mentioned earlier and have potential for use in the field of infrastructure management.
