Scientific Decision Prioritization of Operational Expressway Tunnel Maintenance Engineering based on GA-BP Neural Network
In order to solve the problems of heavy-load computation,subjective and empirical determination of index weights,a scientific decision prioritization model for operational tunnel maintenance engineering is established based on GA-BP neural network.Firstly,eight characteristic indexes of traffic volume,precipitation,inclined shaft,unfavorable geological conditions,number of lanes,in-service years,tunnel length and civil construction technology status are selected to establish the health evaluation standard of in-service expressway tunnel with quantitative and scoring system.The initial weights and thresholds of BP neural network are optimized by genetic algorithm to solve the defects of local optimal solution in BP neural network training.Finally,701 samples are selected for model training and prediction.The results show that the established model can quickly and accurately calculate the tunnel health score,and realize the objective,scientific and intelligent prioritization of scientific decision making in operational expressway tunnel maintenance.
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