A real-time rule-based system for bridge management based on CART decision tree and SMO algorithms

To real-time management of the bridges under dynamic conditions, this paper develops a rule-based decision support framework to extract the necessary rules from simulation results made by Aimsun. In this rule-based system, the supervised and the unsupervised learning algorithms are applied to generalize the rules where the initial set of rules are provided by the aid of fuzzy rule generation algorithms on the results of Aimsun traffic micro-simulation software. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun7 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 so 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. Keywords—Knowledge Extraction; Learning Algorithms; Traffic Simulators; Fuzzy Rule Generation Algorithm;