Metro tunnel boring machine prediction model based on multi-source heterogeneous data
Based on the on-site data of a certain metro section,this article establishes an LSTM prediction model for tunnel boring machine excavation speed.On this basis,the influence of geological parameters on the accuracy of the prediction model is compared and analyzed.In order to excavate more effective information,the various influencing indicators are subjected to dynamic factor model dimensionality reduction in the first place before data training.The conclusion is as follows:① The LSTM time series model can effectively predict the excavation speed of tunnel boring machines,with a prediction accuracy of up to 99.02%and F1 values all above 95%;② After considering the influence of geological parameters on the excavation speed of tunnel boring machines,the model prediction accuracy increased from 96.91%to 99.02%;③ By reducing the dimensionality of the dynamic factor model data and then carry out data training,the model's prediction accuracy increased from 98.96%to 99.02%.The relevant research can provide reference and inspiration for multi-source heterogeneous mixed data modeling of large boring equipment for tunnel boring machines.
metrotunnel boring machinemulti-source heterogeneousgeological parametersdynamic factor modelLSTM prediction model
NETL
NSTL
万方数据
