The structural failure of a tunnel is a process that evolves from local damage to overall destruction. The system reliability analysis method can be useful for analyzing the evolutionary law of local structural failure. In many complex stress environments, the structural performance function may be very complicated or even impossible to solve. This paper establishes a response surface function to represent the implicit tunnel performance function. The reliability of the shear capacity of a tunnel is considered. The critical parts and critical failure paths of the tunnel system are determined using the β-unzipping method. Failure evolution is analyzed to obtain the failure process of the tunnel system. Different failure modes are shown under different cases. Based on the partial failure probability and impact on the tunnel system, the risk levels of the critical parts at each stage of the failure history are evaluated. Therefore, the tunnel failure tree model obtained by combining the response surface method and β-unzipping method plays an important role in tunnel reliability evolution, and can evaluate tunnel safety comprehensively.
Medical text data records detailed clinical data; named entity recognition is the basis of text information processing and an important part of mining valuable information in medical texts. The named entity recognition technology can accurately identify the information needed in medical texts and help medical staff make clinical decision-making, evidence-based medicine, and epidemic disease monitoring. This paper proposes a hybrid neural network medical text named entity recognition model. First, a coding method based on a fully self-attentive mechanism is proposed. The vector representation of each word is related to the entire sentence through the attention mechanism. It determines the weight distribution by scoring the characters or words in all positions and obtains the position information in the sentence that needs the most attention. The encoding vector at each position is integrated with the context information of full sentence, which solves the ambiguity problem. Second, a multivariate convolutional decoding method is proposed. This method can effectively pay attention to the characteristics of medical text named entity recognition in the decoding process. It uses two-dimensional convolutional decoding to associate the current position word with surrounding words to improve decoding efficiency while extracting features from the logic of the preceding and following words. Using the same number of convolution kernels as the entity category, it can effectively extract effective features from the label dimension. Besides, according to the characteristics of the named entity recognition task, a special mixed loss is designed. The experimental results verify that the proposed method is effective, and it is improved compared with some existing medical text named entity recognition methods.
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