TY - JOUR
T1 - A hybrid predictive model of unstable rock blocks around a tunnel based on estimated volumetric fracture intensity and circular variance from borehole data sets
AU - Hekmatnejad, Amin
AU - Crespin, Benoit
AU - Vallejos, Javier A.
AU - Opazo, Alvaro
AU - Adoko, Amoussou C.
N1 - Funding Information:
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Amin Hekmatnejad has received support from the Agencia Nacional de Investigación y Desarrollo, through grant Basal-CONICYT / N° AFB170001 Center for Mathematical Modeling and project IT17M10005 funded by Fondo de Fomento al Desarrollo Científico y Tecnológico (FONDEF). Javier Vallejos has received support from basal CONICYT/PIA Project AFB180004 of Advanced Mining Technology Center (AMTC) and project IT17M10005 funded by Fondo de Fomento al Desarrollo Científico y Tecnológico (FONDEF).].
Funding Information:
The authors acknowledge the constructive comments of two anonymous reviewers. Amin Hekmatnejad acknowledges the funding of the Agencia Nacional de Investigaci?n y Desarrollo, through grant Basal-ANID / N? AFB170001 Center for Mathematical Modeling as well as the support of CODELCO-El Teniente for providing the data used in this work. Special thanks of mining geology team of the El Teniente: Roberto Gonzales, Paulina Schachter, Diego Diaz, and Italo Molina. Also, Javier Vallejos gratefully acknowledges the financial support from basal CONICYT/PIA project AFB180004 of Advanced Mining Technology Center (AMTC) and project IT17M10005 funded by Fondo de Fomento al Desarrollo Cient?fico y Tecnol?gico (FONDEF). We gratefully acknowledge the support of NVIDIA Corporation with the donation of GPU used for this research.
Funding Information:
The authors acknowledge the constructive comments of two anonymous reviewers. Amin Hekmatnejad acknowledges the funding of the Agencia Nacional de Investigación y Desarrollo, through grant Basal-ANID / N° AFB170001 Center for Mathematical Modeling as well as the support of CODELCO-El Teniente for providing the data used in this work. Special thanks of mining geology team of the El Teniente: Roberto Gonzales, Paulina Schachter, Diego Diaz, and Italo Molina. Also, Javier Vallejos gratefully acknowledges the financial support from basal CONICYT/PIA project AFB180004 of Advanced Mining Technology Center (AMTC) and project IT17M10005 funded by Fondo de Fomento al Desarrollo Científico y Tecnológico (FONDEF). We gratefully acknowledge the support of NVIDIA Corporation with the donation of GPU used for this research.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/5
Y1 - 2021/5
N2 - Several failure modes may occur around underground excavations, one of the most common being block failure. The size distribution and occurrence probabilities of unstable blocks depend on discontinuity parameters such as size, shape, intensity, and orientation. The knowledge about the size and the number of blocks provides critical information to optimize site characterization and design procedures, or at least to accomplish this more efficiently. In this work we introduce a new attribute, Circular Variance, to measure the degree of dispersion of the fracture poles in angular domain based on borehole data. In our study the final objective is to present a predictive model of the mean rate of the formation of unstable rock blocks and their uncertainty. To fulfill this goal, we introduce a hybrid approach based on the combination of probabilistic discrete fracture network modeling, rock block failure instability analysis and supervised vector machine concepts. The present research studies the impact of P32, circular variance, and mean radius of the fractures on the mean generation rate of unstable blocks in a tunnel at El Teniente mine, Chile. A two and three factors supervised Poisson regression models are compared against each other for the prediction of the mean generation rate of unstable blocks. The results showed a good agreement between the real number of unstable blocks and the estimated ones with supervised Poisson regression models.
AB - Several failure modes may occur around underground excavations, one of the most common being block failure. The size distribution and occurrence probabilities of unstable blocks depend on discontinuity parameters such as size, shape, intensity, and orientation. The knowledge about the size and the number of blocks provides critical information to optimize site characterization and design procedures, or at least to accomplish this more efficiently. In this work we introduce a new attribute, Circular Variance, to measure the degree of dispersion of the fracture poles in angular domain based on borehole data. In our study the final objective is to present a predictive model of the mean rate of the formation of unstable rock blocks and their uncertainty. To fulfill this goal, we introduce a hybrid approach based on the combination of probabilistic discrete fracture network modeling, rock block failure instability analysis and supervised vector machine concepts. The present research studies the impact of P32, circular variance, and mean radius of the fractures on the mean generation rate of unstable blocks in a tunnel at El Teniente mine, Chile. A two and three factors supervised Poisson regression models are compared against each other for the prediction of the mean generation rate of unstable blocks. The results showed a good agreement between the real number of unstable blocks and the estimated ones with supervised Poisson regression models.
KW - Circular Variance
KW - Discrete fracture network model
KW - Hybrid approach
KW - Supervised vector machine
KW - Tunnel instability
KW - Volumetric fracture intensity
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U2 - 10.1016/j.tust.2021.103865
DO - 10.1016/j.tust.2021.103865
M3 - Article
AN - SCOPUS:85101172236
SN - 0886-7798
VL - 111
JO - Tunnelling and Underground Space Technology
JF - Tunnelling and Underground Space Technology
M1 - 103865
ER -