Roller compacted concrete compaction degree evaluation method based on GA-BP network

The invention discloses a roller compacted concrete compaction degree evaluation method based on a GA-BP network. The method comprises the following steps: selecting the moisture content, the rollinglayer surface stress transverse wave velocity, the rolling material grading factor and the rubber sand ratio of a rolling material at each measuring point of a construction site as input index parameters of a real-time evaluation model; Determining a neural network structure of the compaction degree real-time evaluation model; Optimizing an initial weight value and a threshold value by utilizing agenetic algorithm; Substituting the determined initial weight value and the threshold value into a BP neural network for fine tuning to establish an optimal neural network model; And performing real-time evaluation to obtain a compaction degree value. According to the method, BP neural network is adopted on the basis of the moisture content of the roller compaction material before concrete rolling compaction, the surface