Soil geology plays an important role in selection of core soil for constructing rock-fill dams and in geotechnical evaluation while constructing major structures. Inferring the geology formations in the region between one borehole and another (cross-borehole region) is a human-intensive process of only moderate reliability. Improved operation planning and better geological assessment contributing to cost reduction can be achieved if reliability of inference can be improved. Cross-borehole interpolation using neural networks, such as the multilayer perceptron (MLP), is a relatively recent development and offers many advantages in dealing with the nonlinearity inherent in such a problem. However, neural networks alone are not sufficient to accommodate the fuzzy nature of the geological information. Cross-borehole soil-geology interpolation was investigated using a fuzzy-MLP neural network and is summarized in this paper. To train this network, data from borehole investigations were supplemented with artificial data created using human knowledge, which is termed "data-based knowledge incorporation." The fuzzy-MLP neural network takes advantage of MLP neural networks and fuzzy set theory. Because of this, fuzzy-MLP not only interpolates but also provides an indication about the interpolation accuracy.


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  • Accession Number: 00795678
  • Record Type: Publication
  • Contract Numbers: CSM 9115316, CES 8711764, MMS-8817900
  • Files: TRIS
  • Created Date: Jul 17 2000 12:00AM