TY - ELEC
AU - Stern, Raphael E.
AU - Song, Junho
AU - Work, Daniel B.
PY - 2015
TI - Network reliability analysis for cluster connectivity using AdaBoost
KW - Conference Paper
LA - eng
M3 - Text
AB - In the aftermath of a natural disaster, knowledge of the connectivity of different regions of
infrastructure networks is crucial to aid decision makers. For large-scale networks it can be extremely
time-consuming to obtain a converged estimate by performing a large number of Monte Carlo simulations
to compute the network failure probability. To reduce computational requirements, this work develops a
surrogate model using an AdaBoost classifier for predicting probabilities of disconnections between node
clusters in lifeline infrastructure networks. The proposed approach uses spectral clustering to partition
the network, and it estimates the connectivity of these clusters using an AdaBoost classifier. Numerical
experiments on a California gas distribution network demonstrate that using the surrogate model to determine
cluster connectivity introduces less than five percent error and is two orders of magnitude faster
than methods using an exact network model to estimate the probability of network failure through Monte
Carlo simulations.
N2 - In the aftermath of a natural disaster, knowledge of the connectivity of different regions of
infrastructure networks is crucial to aid decision makers. For large-scale networks it can be extremely
time-consuming to obtain a converged estimate by performing a large number of Monte Carlo simulations
to compute the network failure probability. To reduce computational requirements, this work develops a
surrogate model using an AdaBoost classifier for predicting probabilities of disconnections between node
clusters in lifeline infrastructure networks. The proposed approach uses spectral clustering to partition
the network, and it estimates the connectivity of these clusters using an AdaBoost classifier. Numerical
experiments on a California gas distribution network demonstrate that using the surrogate model to determine
cluster connectivity introduces less than five percent error and is two orders of magnitude faster
than methods using an exact network model to estimate the probability of network failure through Monte
Carlo simulations.
UR - https://open.library.ubc.ca/collections/53032/items/1.0076251
ER - End of Reference