SUBSURFACE SOIL-GEOLOGY INTERPOLATION USING FUZZY NEURAL NETWORK

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.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/3519342
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Kumar, J K
  • KONNO, M
  • Yasuda, N
• Publication Date: 2000-7

Language

• English

Media Info

• Features: Appendices; Figures; References; Tables;
• Pagination: p. 632-639
• Serial:
  • Journal of Geotechnical and Geoenvironmental Engineering
  • Volume: 126
  • Issue Number: 7
  • Publisher: American Society of Civil Engineers
  • ISSN: 1090-0241
  • Serial URL: http://ojps.aip.org/gto

Subject/Index Terms

• TRT Terms: Boreholes; Case studies; Construction; Cost control; Fuzzy sets; Geology; Geotechnical engineering; Interpolation; Neural networks; Reliability (Statistics); Rockfill dams; Soils; Statistical inference
• Subject Areas: Construction; Data and Information Technology; Geotechnology; Highways; I42: Soil Mechanics;

Filing Info

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