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A platform for research: civil engineering, architecture and urbanism
Einsatz von neuronalen Netzen zur Vorhersage des Materialvolumens von Injektionsbaustellen im Tunnelbau
Use of neural networks for the prediction of the material volume of injection sites in tunnel construction. In this paper, a method is presented that uses digital documentation on injection construction sites to calculate automated, construction-accompanying predictions of the injection quantities still to be expected. In the construction project under investigation, waterproofing injections are being made as part of the Stuttgart 21 project for a 3.2 km long, twin-tube railroad tunnel. The presented method uses a particular form of a neural network, the Feed Forward Network. The network is trained with the injection quantities per tunnelmeter of one tunnel tube to predict the other's injection quantities. After a brief introduction to the operation of neural networks, it is shown that the presented method can forecast the total injection quantities with an accuracy > 5 %. Renesco GmbH has collected the data in cooperation with eguana GmbH.
Einsatz von neuronalen Netzen zur Vorhersage des Materialvolumens von Injektionsbaustellen im Tunnelbau
Use of neural networks for the prediction of the material volume of injection sites in tunnel construction. In this paper, a method is presented that uses digital documentation on injection construction sites to calculate automated, construction-accompanying predictions of the injection quantities still to be expected. In the construction project under investigation, waterproofing injections are being made as part of the Stuttgart 21 project for a 3.2 km long, twin-tube railroad tunnel. The presented method uses a particular form of a neural network, the Feed Forward Network. The network is trained with the injection quantities per tunnelmeter of one tunnel tube to predict the other's injection quantities. After a brief introduction to the operation of neural networks, it is shown that the presented method can forecast the total injection quantities with an accuracy > 5 %. Renesco GmbH has collected the data in cooperation with eguana GmbH.
Einsatz von neuronalen Netzen zur Vorhersage des Materialvolumens von Injektionsbaustellen im Tunnelbau
Backhaus, Jan Onne (author)
2021-10-01
2-s2.0-85106328980
Article (Journal)
Electronic Resource
German
DDC:
690
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