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TBM Apparatus for predicting advance rate of shield tunnel boring machine and method thereof

机译:盾构隧道掘进机掘进速度预测的TBM装置及其方法

摘要

The present invention relates to an apparatus for predicting an actual drilling speed of a shield TBM and a method thereof. According to the present invention, the apparatus for predicting an actual drilling speed of a shield TBM comprises: a DB for storing a plurality of ground factors measured in correspondence to a site, and a site penetration index calculated based on an actual drilling speed of a site for a plurality of pre-constructed sites, respectively; a data correction unit for correcting a rock grade which is one of ground factors and reflecting the same to update the DB; a clustering unit for projecting coordinate points corresponding to the ground factors after updating onto an input vector space, and fuzzy-clustering coordinate points of each site into a plurality of clusters; a neural network learning unit for applying the ground factors and the site penetration index to each of an input node and an output node of an adaptive neuro-fuzzy neural network (ANFIS) constructed based on a membership function by fuzzy clustering to train neural networks; and a prediction unit for inputting the ground factors obtained with respect to a predetermined construction target site in the same method as a pre-constructed site, and predicting a site penetration index of the construction target site. According to the present invention, ground factors and a site penetration index obtained in a previous construction site are trained in neural networks to predict a site penetration index only with ground data of a construction target site, and to predict an actual drilling speed reliably based on the prediction of the site penetration index, thereby dynamically reducing prediction error of construction costs and construction time.
机译:本发明涉及一种用于预测盾构TBM的实际钻速的设备及其方法。根据本发明,用于预测盾构TBM的实际钻速的设备包括:DB,用于存储与现场相对应地测量的多个地面因素,以及基于钻探的实际钻速来计算的现场穿透指数。站点分别用于多个预先构建的站点;数据校正单元,用于校正作为地面因素之一的岩石坡度并反映该岩石坡度以更新DB;聚类单元,用于将更新后的与地面因子相对应的坐标点投影到输入向量空间上,并且将每个站点的坐标点模糊聚类为多个聚类;神经网络学习单元,用于通过隶属函数通过模糊聚类构建的自适应神经模糊神经网络(ANFIS)的输入节点和输出节点分别应用地面因素和站点渗透指数来训练神经网络;预测单元,其以与预建现场相同的方法输入相对于预定的施工目标现场而获得的地面因子,并预测施工目标现场的现场穿透指数。根据本发明,在神经网络中训练在先前的施工现场中获得的地面因素和现场穿透指数,以仅利用施工目标现场的地面数据来预测现场穿透指数,并基于该目标可靠地预测实际钻速。现场渗透指数的预测,从而动态减少了施工成本和施工时间的预测误差。

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