Abstract
Geotechnical properties are controlled by factors such as mineralogy, fabric and pore water: dynamic properties which can change in response to the environment and human intervention. Their interactions are difficult to establish by statistical methods alone due to their interdependence. Based on the application of an artificial neural network, a methodology has been developed for predicting geotechnical properties utilising their Relative Strength of Effect (RSE) and Potential Relative Strength of Effect (PRSE). The PRSE reveals the trend in the neighbourhood of the focus point and has been found to be less sensitive to any noise existing within the data set. An application is illustrated using sandstone data published by Hawkins and McConnel (1991) whereby the possible influence of petrological characteristics on their geotechnical properties has been assessed.
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