Rough set-support vector machine-based real-time monitoring model of safety status during dangerous dam reinforcement

There are usually a small amount and short sequence of prototype monitoring data on structural behavior during dangerous dam reinforcement. According to above data characteristics, some methods, such as support vector machine, particle swarm optimization, genetic algorithm, rough set, are combined to build the real-time monitoring model of safety status during structural reinforcement of dangerous dam. Firstly, the construction principle on standard support vector machine-based monitoring model of safety status is demonstrated. To improve the modeling accuracy and efficiency, particle swarm optimization and genetic algorithm are introduced to implement the support vector machine parameters optimization. Secondly, the ability on data mining in rough set is developed to determine the input vector of support vector machine. An approach is presented to obtain the simplified non-linear mapping relationship between dam behavior (effect-quantity) and its cause (influence-quantity). An index and its calculating formula are proposed to measure the influence-quantity importance. A real-time monitoring model based on rough set-support vector machine is established to describe reasonably the working mechanism during dangerous dam reinforcement. Lastly, the fitting and prediction capability of above monitoring model is demonstrated with an actual case.