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Estimation of air losses in compressed air tunneling using neural network

机译：使用神经网络估算压缩空气隧道中的空气损失

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This paper explores the capabilities of neural networks to predict the air losses in compressed air tunneling. Field data from the Feldmoching tunnel in Munich were used in this study. In this project, compressed air was used to retain the groundwater and shot-crete was used as temporary support. The final permanent lining was installed in free air. The tunnel passed through variable ground conditions ranging from coarse gravel to sand and clay. Grouting, an additional layer of shotcrete and a layer of mortar were occasionally used to control the air losses. A back-propagation feed forward neural network was trained and used to predict the air losses from the Feldmoching tunnel. The results of the prediction of the air losses from the tunnel using a neural network were compared with the field measurements. Data from different tunnel lengths were used for training. In each case, the trained network was used to predict the air losses during the excavation of the rest of the tunnel. It is shown that, not only can a neural network learn the relationship between appropriate soil and tunnel parameters and air losses, it can also generalize the learning to predict air losses for very different geological and geometric conditions. It is also shown that data from a very short length (50 m in one case) of the tunnel (five data point only, in this case) may contain enough information for the neural network to learn and predict the air losses in the remaining (585 m) length of the tunnel with a good degree of accuracy. This can be of considerable value to tunnel engineers in control of tunneling operations and help them in preparation for possible changes in air losses with tunnel advance, with changes in ground conditions and tunnel geometry and with time.
机译：本文探讨了神经网络预测压缩空气隧道中的空气损失的能力。这项研究使用了慕尼黑Feldmoching隧道的现场数据。在该项目中，压缩空气被用来保留地下水，喷浆被用作临时支撑。最后的永久衬里安装在自由空气中。隧道通过了从粗砾石到沙子和粘土的各种地面条件。灌浆，偶尔使用一层喷射混凝土和一层砂浆来控制空气损失。训练了反向传播前馈神经网络，并将其用于预测从Feldmoching隧道流失的空气。使用神经网络预测隧道漏风的结果与现场测量结果进行了比较。来自不同隧道长度的数据用于训练。在每种情况下，都使用训练有素的网络来预测隧道其余部分的开挖过程中的空气损失。结果表明，神经网络不仅可以学习适当的土壤和隧道参数与空气损失之间的关系，还可以推广这种学习方法，以预测在非常不同的地质和几何条件下的空气损失。还表明，来自隧道非常短的长度（在一种情况下为50 m）（在这种情况下，仅五个数据点）的数据可能包含足够的信息，供神经网络学习和预测其余管道中的空气损失（ 585 m）的隧道长度，精度很高。对于控制隧道作业的隧道工程师来说，这可能具有巨大的价值，并帮助他们为随着隧道前进，地面条件和隧道几何形状以及时间的变化而可能发生的空气损失变化做好准备。

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