Belief Networks for Construction Performance Diagnostics

Abstract

Belief networks, also referred to as Bayesian networks, are a form of artificial intelligence that incorporates uncertainty through probability theory and conditional dependence. Variables are graphically represented by nodes, whereas conditional dependence relationships between the variables are represented by arrows. A belief network is developed by first defining the variables in the domain and the relationships between those variables. The conditional probabilities of the states of the variables are then determined for each combination of parent states. During evaluation of the network, evidence may be entered at any node without concern about whether the variable is an input or output variable. The probability of each state for the remaining variables, where the state is unknown, is evaluated. An automated approach for the improvement of construction operations involving the integration of belief networks and computer simulation is described. In this application, the belief networks provide diagnostic functionality to the performance analysis of the construction operations. Computer simulation is used to model the construction operations and to validate the changes to the operation recommended by the belief network.