基于IPSO-ANN时间序列模型的泥水平衡盾构机盾尾密封油脂消耗预测分析

Prediction and Analysis of Tail Seal Grease Consumption of Slurry Shield Machine Based on IPSO-ANN Time Series Model

准确预测盾尾密封油脂消耗对盾尾密封、盾构施工安全和成本控制具有重要意义。为此,采用K折交叉验证法结合ANN神经网络,改进传统粒子群优化算法,实现IPSO算法自动寻优ANN神经网络神经元超参数,构建泥水平衡盾构机盾尾密封油脂消耗量IPSO-ANN时间序列模型。基于济南黄河隧道,结合东西双线密封油脂用量和通过双重筛选得到的影响因素,制定混合训练、东西线单独训练3种策略对模型进行训练并对施工段油脂消耗量进行预测及分析。结果表明,IPSO-ANN模型能有效寻优具有最佳神经元超参数的神经网络模型;不同训练策略下最优模型平均预测精度均高于80%,其中混合训练策略下最优模型预测精度高达85.142%,并兼具稳定性,对盾尾密封油脂消耗量预测具有参考意义。

Accurately predicting consumption of tail seal grease is very important to tail seal,shield tunnelling safety and cost control.Therefore,the K-fold cross-validation and ANN neural network have been used to improve the conventional particle swarm optimization algorithm,so as to allow the IPSO algorithm to automatically find the optimum neuron hyper-parameters of ANN neural network and build the IPSO-ANN time series model for the slurry shield tail seal grease consumption.In the Jinan Yellow River Tunnel project,based on the seal grease consumption of the east and west lines and the influencing factors that are obtained through double screening,the 3 strategies including hybrid training,east line separate training and west line separate training have been established that are used for model training,and the grease consumption in construction section have been predicted and analyzed.As the results suggest,the IPSO-ANN model is effective in finding the neural network model that has the optimum neuron hyper-parameters;under different training strategies,the average prediction accuracy of optimum model is above 80%,the prediction accuracy under the hybrid training strategy is 85.142%,and there is also good stability,so this model is useful for predicting tail seal grease consumption.