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