Developing a New Methodology for Characterization of In-SituSoil Properties

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This research is aimed at improving the Geotechnical Engineer's capability of characterizing in situ soil properties. The potential of artificial neural networks in site characterization is explored using several types of in situ test data from selected National Geotechnical Experimentation Sites (NGES). A new methodology is developed that utilizes both the `unstructured` knowledge generalized with neural networks and the `structured` knowledge is available in the form of local experience, engineering judgment, and expert rules. Comparison is made between the results obtained from the neural network approach and the probabilistic approach. Several issues are further explored using developed neural networks: 1) correlations among design soil parameters, 2) cross-checking design soil parameters obtained from different in situ tests, 3) averaging the profiles of soil parameters from different in situ tests, 4) possible reduction of the number of `hard` data needed, and 5) probabilistic neural network learning. The research findings are then synthesized into a design methodology for site characterization. Guidelines and procedure for applying this design methodology are established and documented. /¥Á ÂÁÁ┤╣À ╝/¥Á¢ Â╝?_ └╣┤Á? ╝Á│?╝┤╣À¢ /┤ ┤ ó/ ┴Á /││┐╝/¥Á%` _Á/¢┐╝Á ║╝Á` ▓Á©/└╣?╝ ┴╣¥© ?┐╝ └╣┤Á? ¢`¢¥Á_ ¼©Á¢Á ╗┐Á¢¥╣?¢ ┴╣%% ▓Á /┤┤╝Á¢¢Á┤ ╣ / ¢Á╝╣Á¢ ? │?¥╝?%%Á┤ Á┬║Á╝╣_Á¥¢ ╣└?%└╣À ¢¥╝╣║Á┤ ▓/¢¢ %/╝└/Á (?╝?Á ¢/┬/¥╣%╣¢ /┤ ¥©Á │¥Á?║©?╝Á (Á_╣?║¢╣¢ %Á╣┤`╣ /¢ ║╝Á┤/¥?╝¢ /┤ ▓╝╣Á ¢©╝╣_║ /┐║%╣╣ á╝¥Á_╣/ ¢║ /┤ │?║Á║?┤╣¥Á¢ ? ñ┐╝¥Á_?╝/ ¢║ /¢ ║╝Á` íÁ©/└╣?╝ ▓/¢Á┤ Á¢¥╣_/¥Á¢ ? Á│?┐¥Á╝ /¥¥/│, /┤ ÂÁÁ┤╣À ╝/¥Á¢ ┴╣%% ▓Á │?_║/╝Á┤ ┴╣¥© ╣┤Á║Á┤Á¥ Á¢¥╣_/¥Á¢ ┤Á╝╣└Á┤ Â╝?_ //%`:Á¢ ? ¥©Á Â┐│¥╣?/% ╝Á¢║?¢Á¢ ? ║╝Á┤/¥?╝¢ ┴╣¥©╣ ¥©Á │©/_▓Á╝