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

A real-time decision support system for bridge management based on the rules generalized by CART decision tree and SMO algorithms