Today, Dr. Francis is giving a talk titled "Two Studies in Using Graphical Model for Infrastructure Risk Models" discussing some recent peer-reviewed conference papers given at ICVRAM and PSAM11/ESREL12. The abstract for today's talk is:
In this talk, I will discuss the use of Bayesian Belief Networks (BBNs) and Classification and Regression Trees (CART) for infrastructure risk modeling. In the first case study, we focus on supporting risk models used to quantify economic risk due to damage to building stock attributable to hurricanes. The increasingly complex interaction between natural hazards and human activities requires more accurate data to describe the regional exposure to potential loss from physical damage to buildings and infrastructure. While databases contain information on the distribution and features of the building stock, infrastructure, transportation, etc., it is not unusual that portions of the information are missing from the available databases. Missing or low quality data compromise the validity of regional loss projections. Consequently, this paper uses Bayesian Belief Networks and Classification and Regression Trees to populate the missing information inside a database based on the structure of the available data. In the second case study, we use Bayesian Belief Networks (BBNs) to construct a knowledge model for pipe breaks in a water zone. BBN modeling is a critical step towards real-time distribution system management. Development of expert systems for analyzing real-time data is not only important for pipe break prediction, but is also a first step in preventing water loss and water quality deterioration through the application of machine learning techniques to facilitate real-time distribution system monitoring and management. Our model is based on pipe breaks and covariate data from a mid-Atlantic United States (U.S.) drinking water distribution system network. The expert model is learned using a conditional independence test method, a score-based method, and a hybrid method, then subjected to 10-fold cross validation based on log-likelihood scores.
This talk is hosted by Ketra Schmitt in the Center for Engineering in Society on the Faculty of Engineering and Computer Science.