Semiautomatic Determination of the Geological Strength Index Using SfM and ANN Techniques

Zeni, Marilia Abrão; Peroni, Rodrigo de Lemos; Santos, Fabio Augusto Guidotti dos

doi:10.1061/(asce)gm.1943-5622.0002203

Cited by 2 publications

(2 citation statements)

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“…Note that the majority of these correlations combine qualitative parameters from other widely used classification systems like RMR, Q, and RMi [27]. There are at least six additional quantification attempts [28][29][30][31][32][33] using probabilistic and/or computer-based methods (including machine learning), for a total of at least 23 different quantifications of the GSI chart.…”

Section: Evolution Of Gsi and Different Variationsmentioning

confidence: 99%

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Why Engineers Should Not Attempt to Quantify GSI

Yang

Elmo

2022

Geosciences

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In the past decade, there has been an increasing trend of digitalizing rock engineering processes. However, this process has not been accompanied by a critical analysis of the very same empirical methods that many complex numerical and digital methods are founded upon. As engineers, we are taught to use and trust numbers. Indeed, we would not be able to define the factor of the safety of a structure without numbers. However, what happens when those numbers are nothing but numerical descriptions of qualitative assessments? In this paper we present a critical review of the many attempts presented in the literature to quantify GSI (geological strength index). To the authors’ knowledge, this paper represents the first time that all the different GSI tables and quantification methods that have been proposed over the past two decades are collated and compared critically. In our critique, we argue against the paradigm whereby the quantification process adds the experience factor for inexperienced engineers. Furthermore, we discuss the limitations of the notion that GSI quantification methods could transform subjectivity into objectivity since the parameters under considerations are not quantitative measurements. Relying on empirically defined quantitative equivalences raises important questions, particularly when these quantitative equivalences are being used to define so-called accurate rock mass classification input for design purposes.

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Section: Evolution Of Gsi and Different Variationsmentioning

confidence: 99%

“…In recent years, rock engineers have begun to apply machine learning to rock engineering applications. One application is in using machine learning (ML) to automate and quantify the GSI chart, as shown in [29][30][31][32]. However, this practice has several limitations.…”

Section: Machine Learningmentioning

confidence: 99%