A big data safety management platform for dyke engineering is presented in this paper. The internet of things (IoT) technology is adopted to build a cloud platform for the monitoring, collection, exchange, and sharing of massive data in the monitoring system. Meanwhile, big data and artificial intelligence technologies are employed for the fusion and sharing of massive, multi-source and heterogeneous data to identify and evaluate risks and construct early-warning model. Through the data acquisition and collection by the IoT monitoring platform, the model predicting the gradation after sandstone blasting is built to design the optimum blasting scheme and control the particle gradation of sandstone material. Engineering practice has demonstrated that the average relative error rate of controlling the gradation is within 21%, which meets the requirement and guarantees the dyke construction quality.
Key words
dyke engineering /
safety management platform /
big data /
artificial intelligence /
deep learning /
IoT /
design requirements of gradation
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