基于相关向量机的TBM掘进速度预测模型

TBM具有安全性强、施工效率高等优点,在隧道施工尤其是长距离隧道施工中得到广泛应用。TBM掘进速度受多个因素影响,各因素本身除了具有较强的不确定性以外,还存在着复杂的关联关系,难以建立精准的速度预测模型。文章提出一种基于相关向量机的TBM掘进速度预测模型,该模型通过对样本的学习,可以建立各因素与掘进速度的非线性映射关系,精准预测仅知道影响因素的预测样本。将该模型应用于TBM掘进速度预测,结果表明,该方法具有精度高、容易实现和离散性小等优点,为TBM掘进速度预测提供了一条新途径。

Prediction Model of TBM Advance Rate Based on Relevance Vector Machine

TBM has been widely used in tunnel construction,especially the long distance tunnel attributed to the advantages of strong safety,high construction efficiency and so on.The advance rate of TBM is affected by various factors which have complex correlation besides high uncertainty,and so it is difficult to establish a precise model for predicting the advance rate.A relevance vector machine based TBM advance rate prediction model is proposed by which it can build a nonlinear mapping relationship between various factors and advance rate through learning the samples,precisely predict the samples with only known influential factors.The results obtained from the TBM ad⁃vance rate prediction show that this model has the advantages of high precision,easy operation and small discreteness.