CLASSIFICATION OF ROCK MASSES FOR FOUNDATION ENGINEERING: A NEW SYSTEM - ROCK CONDITION NUMBER

Since their development, rock mass classification systems have used and manipulated various populations of geomechanical data to allow a rock mass to be divided into different domains or engineering ‘masses’ with the aim of assisting in the geotechnical design of underground openings, excavations, foundations and ground support systems.

Each of these methods consider different characteristics to generate a material classification; including rock strength, joint weathering, defect spacing, in-situ stress and groundwater. However, none of these systems cater for classification of the rock mass based on whole rock weathering, whole rock strength and incipient defect spacing along a borehole.

This new classification system, the Rock Condition Number (RCN), has been developed to reduce the human factor of variability in interpretation when collecting data to classify the rock mass, as other methods, such as Rock Quality Designation (RQD), are prone to significant variability based on the experience of the person logging the core. RQD provides an indication of rock quality over the length of the cored interval, which varies depending on the drilling equipment and ground conditions. This value may typically be calculated over an interval of 1.0, 1.5 or 3.0 metres. The RQD system does not allow for the rapid identification of thin, though important features in the subsurface.

Using data captured electronically in the field, the RCN calculates an instantaneous classification of the rock mass at any point along the borehole, highlighting variations within the rock mass by assessing a combination of characteristics, allowing rapid identification of potential hazardous zones within the rock mass. This allows for significant improvements in efficiency during the assessment and design process/es. Resolution is greatly improved over RQD, with thin, though important, zones of weak material highlighted using this new process.

Comparison between existing classifications and the RCN using real field data indicates the RCN provides greater resolution when identifying deficient zones within the rock mass.