A Knowledge-Based System for Estimation of Geotechnical Properties

Abstract

A knowledge-based system for the estimation of geotechnical properties. The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source• a link is made to the metadata record in Durham E-Theses• the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. STATEMENT OF COPYRIGHTThe copyright of this thesis rests with the author. No quotation from it should be published without his prior written consent and information derived from it should be acknowledged. ABSTRACTGround properties are mainly evaluated from direct measurements obtained from either laboratory or field tests. As an alternative, or in conjunction with test measurements, ground properties can also be estimated from correlations and published summaries of "typical" values. The advantages of their use are that both are simple, easy to use and they provide a cheap, if crude, means for the rapid estimation of ground properties.A knowledge-based system has been developed to provide a tool for storing and using correlations and "typical" values for the estimation of ground properties. The system was implemented in the ProKappa software, running under X windows on a Sun Spark 2 workstation. The system developed is intended to provide geotechnical engineers with a decision-support tool and to demonstrate the applicability of knowledge-based system technology to the ground property evaluation problem.

Grammatical Inference Techniques and Their Application in Ground Investigation

“…The use of artificial intelligence (AI) techniques in the interpretation of information is well established and much research has been carried out in the area. Knowledge-based systems have been developed to interpret geotechnical information and properties (Toll, 1995;Toll and Giolas, 1995;Oliver and Toll, 1996) in addition to practical applications to assist in foundation design (Toll, 1996). Further techniques involving the development of neural networks have been used in modeling (Ellis et al, 1995;Basheer, 2001) and also in predictive applications (Shi et al, 1998;De Stefano et al, 1999;Shi, 1999).…”