Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Probabilistic Rock Mass Rating Estimation Using Electrical Resistivity
https://orcid.org/http://orcid.org/0000-0002-6953-9456
https://orcid.org/http://orcid.org/0000-0001-7027-0426
https://orcid.org/http://orcid.org/0000-0002-1259-4796
https://orcid.org/http://orcid.org/0000-0001-8582-9237
Understanding exact rock conditions has a significant impact on the success of tunnel construction. Various methods such as Q-systems and rock mass rating (RMR) have been used to estimate the condition of jointed rock mass in fields. However, the evaluation of rock mass characteristics can diverge because of the subjective views of observers. Many researchers that work on the relationship between rock mass classification methods and its physical variables have tried to solve this problem; nevertheless, the accuracy of their studies has become a subject of controversy. Thus, in this study, we tried to evaluate rock mass classification in terms of RMR from the electrical resistivity that is readily affected by the characteristics of jointed rock mass. The relationship between the RMR parameters and the electrical resistivity was deducted using the formula of electrical resistivity analyzed for jointed rock mass. The correlation analyses were performed based on real data (4,689 sets) in order to obtain the degree of correlation among RMR parameters. The results show that some parameters are strongly correlated while other parameters are not. Stochastic analyses show that the theoretical relationship between RMR value and electrical resistivity can be improved by probabilistically removing the irrelevant (low possibility) relation among RMR parameters. Finally, a field test was performed to verify the suggested probabilistic RMR estimation using electrical resistivity.
Probabilistic Rock Mass Rating Estimation Using Electrical Resistivity
https://orcid.org/http://orcid.org/0000-0002-6953-9456
https://orcid.org/http://orcid.org/0000-0001-7027-0426
https://orcid.org/http://orcid.org/0000-0002-1259-4796
https://orcid.org/http://orcid.org/0000-0001-8582-9237
Understanding exact rock conditions has a significant impact on the success of tunnel construction. Various methods such as Q-systems and rock mass rating (RMR) have been used to estimate the condition of jointed rock mass in fields. However, the evaluation of rock mass characteristics can diverge because of the subjective views of observers. Many researchers that work on the relationship between rock mass classification methods and its physical variables have tried to solve this problem; nevertheless, the accuracy of their studies has become a subject of controversy. Thus, in this study, we tried to evaluate rock mass classification in terms of RMR from the electrical resistivity that is readily affected by the characteristics of jointed rock mass. The relationship between the RMR parameters and the electrical resistivity was deducted using the formula of electrical resistivity analyzed for jointed rock mass. The correlation analyses were performed based on real data (4,689 sets) in order to obtain the degree of correlation among RMR parameters. The results show that some parameters are strongly correlated while other parameters are not. Stochastic analyses show that the theoretical relationship between RMR value and electrical resistivity can be improved by probabilistically removing the irrelevant (low possibility) relation among RMR parameters. Finally, a field test was performed to verify the suggested probabilistic RMR estimation using electrical resistivity.
Probabilistic Rock Mass Rating Estimation Using Electrical Resistivity
https://orcid.org/http://orcid.org/0000-0002-6953-9456
https://orcid.org/http://orcid.org/0000-0001-7027-0426
https://orcid.org/http://orcid.org/0000-0002-1259-4796
https://orcid.org/http://orcid.org/0000-0001-8582-9237
Understanding exact rock conditions has a significant impact on the success of tunnel construction. Various methods such as Q-systems and rock mass rating (RMR) have been used to estimate the condition of jointed rock mass in fields. However, the evaluation of rock mass characteristics can diverge because of the subjective views of observers. Many researchers that work on the relationship between rock mass classification methods and its physical variables have tried to solve this problem; nevertheless, the accuracy of their studies has become a subject of controversy. Thus, in this study, we tried to evaluate rock mass classification in terms of RMR from the electrical resistivity that is readily affected by the characteristics of jointed rock mass. The relationship between the RMR parameters and the electrical resistivity was deducted using the formula of electrical resistivity analyzed for jointed rock mass. The correlation analyses were performed based on real data (4,689 sets) in order to obtain the degree of correlation among RMR parameters. The results show that some parameters are strongly correlated while other parameters are not. Stochastic analyses show that the theoretical relationship between RMR value and electrical resistivity can be improved by probabilistically removing the irrelevant (low possibility) relation among RMR parameters. Finally, a field test was performed to verify the suggested probabilistic RMR estimation using electrical resistivity.
Probabilistic Rock Mass Rating Estimation Using Electrical Resistivity
KSCE J Civ Eng
Hong, Chang-Ho (Autor:in) / Ryu, Hee-Hwan (Autor:in) / Oh, Tae-Min (Autor:in) / Cho, Gye-Chun (Autor:in)
KSCE Journal of Civil Engineering ; 24 ; 2224-2231
01.07.2020
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Rock Mass Classification Using Electrical Resistivity - an Analytical Study
| British Library Online Contents | 2006
Electrical resistivity-based rock mass classification for tunnelling
| British Library Online Contents | 2006
Electrical resistivity-based rock mass classification for tunnelling
| Online Contents | 2006
Electrical resistivity-based rock mass classification for tunnelling
| British Library Conference Proceedings | 2006
An Empirical Relationship between Rock Structure Rating and Rock Mass Rating
| Online Contents | 2006