A real-time decision support system for bridge management based on the
  rules generalized by CART decision tree and SMO algorithms
  Under dynamic conditions on bridges, we need a real-time management. To this
end, this paper presents a rule-based decision support system in which the
necessary rules are extracted from simulation results made by Aimsun traffic
micro-simulation software. Then, these rules are generalized by the aid of
fuzzy rule generation algorithms. Then, they are trained by a set of supervised
and the unsupervised learning algorithms to get an ability to make decision in
real cases. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun
and WEKA data mining software is used to execute the learning algorithms. Based
on this experiment, the accuracy of the supervised algorithms to generalize the
rules is greater than 80%. In addition, CART decision tree and sequential
minimal optimization (SMO) provides 100% accuracy for normal data and these
algorithms are so reliable for crisis management on bridge. This means that, it
is possible to use such machine learning methods to manage bridges in the
real-time conditions.
