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Publications tagged with [artificial neural networks]

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<p>Increasing demand on infrastructures increases attention to shallow soft ground tunneling methods in urbanized areas. Especially in metro tunnel excavations, due to their large diameters, it is important to control the surface settlements observed before ...
Reference: Environ Earth Sci., DOI 10.1007/s12665-012-2214-x
<p>During the design phase of a tunnel, one of the concerns of tunnel engineers is estimating ground movements induced by tunneling, in order to take steps to prevent or minimize possible damage caused by these movements, to adjacent structures. In this work, ...
Reference: Geotechnical Aspects of Underground Construction in Soft Ground – Ng, Huang & Liu (eds), © 2009 Taylor & Francis Group, London
<p>Modelling tunnel boring machine (TBM) performance is an important aspect in tunnel operations. The use of artificial intelligence techniques such as artificial neural networks has been recently introduced to this subject and the results from such applications ...
Reference: Underground Spaces I, WIT Transactions on the Built Environment, Vol 102
<p>This paper presents an Artificial Neural Network approach for a quick displacement prediction using the results of field measurement for NATM tunnels.</p>
Reference: International Journal of the JCRM vol.5 (2008) pp.53-62
In the present study, a data dependent approach for the prediction of the wave-induced liquefaction depth in a porous seabed is proposed, based on a multi-artificial neural network (MANN) method. This study demonstrates the capacity of the proposed MANN model ...
Reference: Department of Civil Engineering, Research Report R854