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The Geological Strength Index (GSI) is an important parameter for estimating rock mass properties and can be estimated either through direct observations or by using a quantitative GSI chart or equations. This study aims to discuss the applicability of the RMR89 method and the latest version of GSI|2013 by applying them to a case study and to the data published in the earlier papers. The relevant rock mass properties, including blockiness and discontinuity conditions, of the walls of 15 exploratory adits with a total length longer than 1100 m in a hydropower project in China have been assessed to provide data for the analysis. In addition, a repeat analysis of the earlier published data has been conducted to validate the results derived from the case study. The results show that the GSI values calculated with the RMR89 are generally larger than those obtained from the qualitative GSI chart, with an average magnitude of about five. The GSI values predicted from the values of JCond89 and RQD are consistent with those derived through the qualitative observational method for a medium-quality rock mass but are relatively smaller or larger for a poor-quality (i.e., GSI<45) or a high-quality (i.e. GSI>65) rock mass, respectively. By comparing the results of the RMR89 method and the qualitative GSI chart method and analyzing the classification factors considered by the two methods, two new equations based on the RMR89 system have been developed for estimating the GSI. Their application to the case study and the published data prove that these two equations have higher predictive accuracy than the original RMR89 method and the latest version of GSI|2013. It is expected that the acquisition of more engineering examples will enable the two formulas to be further verified and improved. [ABSTRACT FROM AUTHOR]

The nominal group technique (NGT) is a method to elicit healthcare priorities. Yet, there is variability on how to conduct the NGT, and limited guidance on how to analyse a diverse sample of multiple groups. This paper addresses some of this ambiguity, and explores whether different approaches to analysis provide the same outcome/s. Conceptual papers and empirical studies were identified via PubMed and informed an adapted version of the NGT. Twenty-six nominal groups were conducted, which provided in-depth knowledge on how to best conduct this method. Pilot group data were used to compare different analysis methods and to explore how this impacted on reported outcomes. Data analyses for large data-sets are complex; thematic analysis is needed to be able to conduct across group comparisons of participant priorities. Consideration should be given not just to the strength, i.e. sum of votes, or relative importance of the priority, but to the voting frequency, i.e. the popularity of the idea amongst participants; our case study demonstrated that this can affect priority rankings for those ideas with the same score. As a case study, this paper provides practical information on analysis for complex data sets. Researchers need to consider more than one analysis process to ensure that the results truly reflect participant priorities. A priority that has a high score may not necessarily reflect its popularity within the group; the voting frequency may also need to be considered. [ABSTRACT FROM AUTHOR]