A real-time decision support system for bridge management based on the rules generalized by CART decision tree and SMO algorithms
This provides a real-time decision support system for bridge managers, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackled real-time bridge management by developing a rule-based decision support system using simulation data, achieving over 80% accuracy with supervised algorithms and 100% accuracy with CART and SMO for normal data.
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.