Prediction of corrosion degree of reinforced concrete based on improved QPSO-neural network

The corrosion assessment of reinforced concrete is an important problem. Most of the current detection methods are to perform corrosion evaluation on a single corrosion feature. The evaluation result depends on expert experience and existing prior knowledge information. In this paper, a neural network model based on improved quantum particle swarms optimisation (QPSO-NN) is proposed, which uses quantum particle swarms to optimise neural network (NN) weights and thresholds to predict the corrosion degree of reinforced concrete. The improved QPSO optimise the adjustment strategy of the Mean Best Position weight and improves the convergence speed of particles. The Simulated annealing algorithm (SA) is introduced in the iterative process of the particles to enhance the global optimisation capability of the particles. Under a variety of environmental parameters(pH, water/cement ratio, Chloride concentration), a variety of corrosion characteristic data are detected through the designed embedded acquisition system. The improved QPSO-NN corrosion model algorithm has better convergence speed by simulated analysis and has better accuracy of reinforcement corrosion assessment by experimental.