Multi step prediction of shield tunneling posture based on machine-stratum state recognition and fusion attention
To predict the shield tunneling posture at multiple future moments and help shield operators identify the trend of shield tunneling posture changes in advance for early decision-making.A knowledge data dual drive shield tunneling posture multi-step prediction method based on machine-formation state recognition and fusion of multi-scale feature attention mechanism time-domain attention mechanism was proposed.The method introduced composite parameter indicators that reflects the real-time relationship between machine-stratum working state such as field penetration index FPI,cutterhead torque per unit penetration index TPI,partition excavatability index FPIR,and screw machine performance index STP as input feature parameters for the model.An Encoder Decoder network structure based on GRU was constructed,with the features as the basis unit.In terms of dimensions,a feature attention mechanism that integrates multi-scale CNN was adopted to adaptively capture different levels Characteristics of shield tunneling parameters at different scales.In terms of time dimension,a time attention mechanism was introduced into the decoder to fully explore the hidden dependencies between long-term historical information and short-term input-output sequences in shield tunneling data.Through the analysis of model testing results on historical data of shield tunneling on Guangzhou Metro Line 12,it is shown that this prediction method not only alleviates the problems of poor interpretability and low efficiency generated by the algorithm itself during optimization,iteration,and matching without increasing network complexity,but also significantly improves the model's ability to extract features,capture correlation in time series,and mine long-term trends,achieving accurate multi-step prediction of shield tunneling posture.Its performance is significantly better than that of gated recurrent neural networks LSTM,GRU,and their classic combination models GRU SelfAttention,GRU MultiheadAttention,etc.The research results can provide reference for further improving the prediction method of shield tunneling posture and enhancing the optimization control level of shield tunneling posture.
万方数据
维普
