Abstract
According to the coexistence of fuzziness and randomness of risk factors in engineering early warning, this paper proposes a risk early warning model based on a convolutional neural network, which identifies and analyzes the risk points in engineering through engineering site pictures and provides early warning for engineering risk points in time. The risk warning model fully characterizes the fuzziness and randomness of risk, and the warning results are more objective and in line with the actual situation, which provides a more feasible engineering risk warning method. The experiments prove that the method can accurately warn of project risks, and the warning readiness rate reaches 91.3%.




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Liu, Q., Chen, Z. Early warning control model and simulation study of engineering safety risk based on a convolutional neural network. Neural Comput & Applic 35, 24587–24594 (2023). https://doi.org/10.1007/s00521-022-08170-9
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DOI: https://doi.org/10.1007/s00521-022-08170-9