Abstract: The flow plasticity inspection by the worker is time-consuming and labor-intensive during earth pressure balance shield tunneling; thus, the results are heavily influenced by subjective state. As a result, an automatic method for detecting the flow plasticity of the conditioned soil is proposed. First, preprocessing techniques, such as cutting, recovering, and strengthening are used to construct a conditioned soil sample data set from real-time monitoring video images. Second, a conditioned soil detection network model is established to identify and classify the flow plasticity of the conditioned soil in the monitoring video, enabling automatic identification of the flow plasticity of the conditioned soil. Finally, the recommended method is applied in a project, and the proposed improved Faster R-CNN neural network method shows a good detection effect in three kinds of conditioned soils. The average accuracy, precision, and recall rates are 91.55%, 86.83%, and 87.42%, respectively, indicating that the recommended method should be widely used.

Key words:  , earth pressure balance shield, soil conditioning, flow plasticity, convolutional neural network