A Developed Computational Code to Build a 3D Fracture Network to Reduce the Uncertainty of Fracture Parameter Generation (A Case Study of the Emamzadeh Hashem Tunnel)
Rock masses comprise intact rock and discontinuities, such as fractures, which significantly influence their mechanical and hydraulic properties. Uncertainty in constructing the fracture network can notably affect the outcomes of sensitive analyses, including tunnel stability simulations. Thus, accu...
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2025-01-01
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| author | Mahin Etemadifar Gholamreza Shoaei Morteza Javadi Arash Hashemnejad |
| author_facet | Mahin Etemadifar Gholamreza Shoaei Morteza Javadi Arash Hashemnejad |
| author_sort | Mahin Etemadifar |
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| description | Rock masses comprise intact rock and discontinuities, such as fractures, which significantly influence their mechanical and hydraulic properties. Uncertainty in constructing the fracture network can notably affect the outcomes of sensitive analyses, including tunnel stability simulations. Thus, accurately determining specific parameters of rock joints, including orientation and trace length, is essential. A discrete fracture network (DFN) is one technique used to simulate jointed rock. However, engineers often face challenges due to the inherent uncertainty in building a fracture network using statistical distribution functions. This study analyzed the fracture network of the Emamzadeh Hashem tunnel using MATLAB-developed code and 3DEC software. It focused on the impact of statistical distribution functions on the uncertainty of fracture network construction. The results reveal that using a negative exponential distribution can introduce significant errors in constructing the fracture network, especially when generating the dip direction. The parametric study shows that employing statistical distribution functions that account for data variance in the Probability Distribution Function (PDF) can enhance the accuracy of generating fracture parameters, such as dip, dip direction, and trace length, thereby reducing uncertainty in fracture network construction. |
| format | Article |
| id | doaj-art-952da1333af44577bf3214af5fb30cc6 |
| institution | Kabale University |
| issn | 2076-3263 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Geosciences |
| spelling | doaj-art-952da1333af44577bf3214af5fb30cc62025-01-24T13:34:06ZengMDPI AGGeosciences2076-32632025-01-01151610.3390/geosciences15010006A Developed Computational Code to Build a 3D Fracture Network to Reduce the Uncertainty of Fracture Parameter Generation (A Case Study of the Emamzadeh Hashem Tunnel)Mahin Etemadifar0Gholamreza Shoaei1Morteza Javadi2Arash Hashemnejad3Engineering Geology Group, Geology Department, Faculty of Basic Sciences, Tarbiat Modares University, Tehran 1411713116, IranEngineering Geology Group, Geology Department, Faculty of Basic Sciences, Tarbiat Modares University, Tehran 1411713116, IranFaculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood 3619995161, IranEngineering Geology, FATJ-Consulting Engineers, Tehran 1967773314, IranRock masses comprise intact rock and discontinuities, such as fractures, which significantly influence their mechanical and hydraulic properties. Uncertainty in constructing the fracture network can notably affect the outcomes of sensitive analyses, including tunnel stability simulations. Thus, accurately determining specific parameters of rock joints, including orientation and trace length, is essential. A discrete fracture network (DFN) is one technique used to simulate jointed rock. However, engineers often face challenges due to the inherent uncertainty in building a fracture network using statistical distribution functions. This study analyzed the fracture network of the Emamzadeh Hashem tunnel using MATLAB-developed code and 3DEC software. It focused on the impact of statistical distribution functions on the uncertainty of fracture network construction. The results reveal that using a negative exponential distribution can introduce significant errors in constructing the fracture network, especially when generating the dip direction. The parametric study shows that employing statistical distribution functions that account for data variance in the Probability Distribution Function (PDF) can enhance the accuracy of generating fracture parameters, such as dip, dip direction, and trace length, thereby reducing uncertainty in fracture network construction.https://www.mdpi.com/2076-3263/15/1/6discrete fracture networkjointed rockuncertainty3DECstatistical distributiongeohazard |
| spellingShingle | Mahin Etemadifar Gholamreza Shoaei Morteza Javadi Arash Hashemnejad A Developed Computational Code to Build a 3D Fracture Network to Reduce the Uncertainty of Fracture Parameter Generation (A Case Study of the Emamzadeh Hashem Tunnel) Geosciences discrete fracture network jointed rock uncertainty 3DEC statistical distribution geohazard |
| title | A Developed Computational Code to Build a 3D Fracture Network to Reduce the Uncertainty of Fracture Parameter Generation (A Case Study of the Emamzadeh Hashem Tunnel) |
| title_full | A Developed Computational Code to Build a 3D Fracture Network to Reduce the Uncertainty of Fracture Parameter Generation (A Case Study of the Emamzadeh Hashem Tunnel) |
| title_fullStr | A Developed Computational Code to Build a 3D Fracture Network to Reduce the Uncertainty of Fracture Parameter Generation (A Case Study of the Emamzadeh Hashem Tunnel) |
| title_full_unstemmed | A Developed Computational Code to Build a 3D Fracture Network to Reduce the Uncertainty of Fracture Parameter Generation (A Case Study of the Emamzadeh Hashem Tunnel) |
| title_short | A Developed Computational Code to Build a 3D Fracture Network to Reduce the Uncertainty of Fracture Parameter Generation (A Case Study of the Emamzadeh Hashem Tunnel) |
| title_sort | developed computational code to build a 3d fracture network to reduce the uncertainty of fracture parameter generation a case study of the emamzadeh hashem tunnel |
| topic | discrete fracture network jointed rock uncertainty 3DEC statistical distribution geohazard |
| url | https://www.mdpi.com/2076-3263/15/1/6 |
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