Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine Learning-Aided Prediction of the Mechanical Properties of Frozen Fractured Rocks
https://orcid.org/0000-0002-5866-7858
Abstract The complexity of fracture geometries impedes reliable prediction of the mechanical properties of frozen fractured rocks. Here, we combine the experimental, numerical, and machine learning methods to predict the uniaxial compressive strength and the Young’s modulus of frozen fractured rocks with five fracture geometries, including persistence factor of ice-filled fractures, spacing between the fractures, as well as inclination angle, thickness, and number of the fractures. We use the results of laboratory uniaxial compression tests to validate the numerical model and the results of two-dimensional particle flow code simulations to train the random forest (RF) models. Our study demonstrates reliable prediction of the uniaxial compressive strength and the Young’s modulus of frozen fractured rocks and compares the prediction performance with the Ramamurthy criterion. We also conduct a sensitivity analysis to reveal dominant geometries and obtain the simplified RF models with three fracture geometries (i.e., persistence factor, inclination angle, and fracture number) for similar prediction accuracy. We finally use additional experimental results to further test the reliability of the simplified RF models. The combined method can be further applied to study other mechanical properties of complex fractured rocks and is particularly suitable for the cases with limited and scattered data from the fractured rocks in experimental and field investigations.
Highlights The experimental and numerical studies show how the complexity of fracture geometries influences the mechanical properties of frozen fractured rocks.The combined experimental, numerical, and machine learning method is developed to predict the mechanical properties of frozen rocks with complex fracture geometries.The sensitivity analysis is used to determine the dominant geometries to simplify the machine learning models for similar prediction accuracy.
Machine Learning-Aided Prediction of the Mechanical Properties of Frozen Fractured Rocks
https://orcid.org/0000-0002-5866-7858
Abstract The complexity of fracture geometries impedes reliable prediction of the mechanical properties of frozen fractured rocks. Here, we combine the experimental, numerical, and machine learning methods to predict the uniaxial compressive strength and the Young’s modulus of frozen fractured rocks with five fracture geometries, including persistence factor of ice-filled fractures, spacing between the fractures, as well as inclination angle, thickness, and number of the fractures. We use the results of laboratory uniaxial compression tests to validate the numerical model and the results of two-dimensional particle flow code simulations to train the random forest (RF) models. Our study demonstrates reliable prediction of the uniaxial compressive strength and the Young’s modulus of frozen fractured rocks and compares the prediction performance with the Ramamurthy criterion. We also conduct a sensitivity analysis to reveal dominant geometries and obtain the simplified RF models with three fracture geometries (i.e., persistence factor, inclination angle, and fracture number) for similar prediction accuracy. We finally use additional experimental results to further test the reliability of the simplified RF models. The combined method can be further applied to study other mechanical properties of complex fractured rocks and is particularly suitable for the cases with limited and scattered data from the fractured rocks in experimental and field investigations.
Highlights The experimental and numerical studies show how the complexity of fracture geometries influences the mechanical properties of frozen fractured rocks.The combined experimental, numerical, and machine learning method is developed to predict the mechanical properties of frozen rocks with complex fracture geometries.The sensitivity analysis is used to determine the dominant geometries to simplify the machine learning models for similar prediction accuracy.
Machine Learning-Aided Prediction of the Mechanical Properties of Frozen Fractured Rocks
https://orcid.org/0000-0002-5866-7858
Abstract The complexity of fracture geometries impedes reliable prediction of the mechanical properties of frozen fractured rocks. Here, we combine the experimental, numerical, and machine learning methods to predict the uniaxial compressive strength and the Young’s modulus of frozen fractured rocks with five fracture geometries, including persistence factor of ice-filled fractures, spacing between the fractures, as well as inclination angle, thickness, and number of the fractures. We use the results of laboratory uniaxial compression tests to validate the numerical model and the results of two-dimensional particle flow code simulations to train the random forest (RF) models. Our study demonstrates reliable prediction of the uniaxial compressive strength and the Young’s modulus of frozen fractured rocks and compares the prediction performance with the Ramamurthy criterion. We also conduct a sensitivity analysis to reveal dominant geometries and obtain the simplified RF models with three fracture geometries (i.e., persistence factor, inclination angle, and fracture number) for similar prediction accuracy. We finally use additional experimental results to further test the reliability of the simplified RF models. The combined method can be further applied to study other mechanical properties of complex fractured rocks and is particularly suitable for the cases with limited and scattered data from the fractured rocks in experimental and field investigations.
Highlights The experimental and numerical studies show how the complexity of fracture geometries influences the mechanical properties of frozen fractured rocks.The combined experimental, numerical, and machine learning method is developed to predict the mechanical properties of frozen rocks with complex fracture geometries.The sensitivity analysis is used to determine the dominant geometries to simplify the machine learning models for similar prediction accuracy.
Machine Learning-Aided Prediction of the Mechanical Properties of Frozen Fractured Rocks
Meng, Wenzhao (Autor:in) / Wu, Wei (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BKL:
38.58
Geomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB41
Homogenization of mechanical properties of fractured rocks using DEM modeling
| British Library Conference Proceedings | 2001
Fabric-Dependent Hydro-Mechanical Behavior of Pre-Fractured Rocks
| British Library Conference Proceedings | 2020
NAPL Migration in Fractured Rocks
| British Library Conference Proceedings | 2002