Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Bayesian Estimation for Probability Distribution of Rock’s Elastic Modulus Based on Compression Wave Velocity and Deformation Warning for Large Underground Cavern
https://orcid.org/0000-0001-6039-9429
Abstract The elastic modulus of rock is the key parameter in excavation deformation prediction, support design, and the stability analysis of underground engineering. However, traditional statistical methods require a large number of laboratory or field tests to obtain its probability distribution form and distribution parameters, which is difficult in some projects. To overcome this problem, a new method based on Bayesian theory is developed to infer the rock elastic modulus probability distribution using the compression wave velocity of rock. The test data collected from the Firuzkoy area of Istanbul are used for method validation, the results of the developed method are compared with a traditional regression model to demonstrate the advantage under small sample conditions. And the effects of different prior ranges and forms on the evaluation results of the elastic modulus are also studied. Furthermore, the developed method is applied to obtain the probability distribution of the elastic modulus at the Yingliangbao hydropower station, and the safety warning indexes in the main powerhouse are formulated based on the displacement probability quantile. Compared with the field monitoring data, the consistency in excavation displacement indicates that the acquired elastic modulus of rock is reasonable for deformation probability estimation and safety warning in underground caverns.
Highlights A method based on Bayesian framework is developed to estimate the distribution of rock’s elastic modulus using the wave velocity, which can effectively improve the evaluation ability under the conditions of small samples.The effects of different input data, different prior ranges, and different prior forms on the evaluation results of elastic modulus are studied, which show that the advantage of the proposed method is general and applicable for different amounts of input data and different types of prior.A probability warning index is proposed based on the occurrence probability of displacement, which can be used to recognize the unstable deformation of surrounding rock in the underground cavern, and the proposed method is verified with the field monitoring data.
Bayesian Estimation for Probability Distribution of Rock’s Elastic Modulus Based on Compression Wave Velocity and Deformation Warning for Large Underground Cavern
https://orcid.org/0000-0001-6039-9429
Abstract The elastic modulus of rock is the key parameter in excavation deformation prediction, support design, and the stability analysis of underground engineering. However, traditional statistical methods require a large number of laboratory or field tests to obtain its probability distribution form and distribution parameters, which is difficult in some projects. To overcome this problem, a new method based on Bayesian theory is developed to infer the rock elastic modulus probability distribution using the compression wave velocity of rock. The test data collected from the Firuzkoy area of Istanbul are used for method validation, the results of the developed method are compared with a traditional regression model to demonstrate the advantage under small sample conditions. And the effects of different prior ranges and forms on the evaluation results of the elastic modulus are also studied. Furthermore, the developed method is applied to obtain the probability distribution of the elastic modulus at the Yingliangbao hydropower station, and the safety warning indexes in the main powerhouse are formulated based on the displacement probability quantile. Compared with the field monitoring data, the consistency in excavation displacement indicates that the acquired elastic modulus of rock is reasonable for deformation probability estimation and safety warning in underground caverns.
Highlights A method based on Bayesian framework is developed to estimate the distribution of rock’s elastic modulus using the wave velocity, which can effectively improve the evaluation ability under the conditions of small samples.The effects of different input data, different prior ranges, and different prior forms on the evaluation results of elastic modulus are studied, which show that the advantage of the proposed method is general and applicable for different amounts of input data and different types of prior.A probability warning index is proposed based on the occurrence probability of displacement, which can be used to recognize the unstable deformation of surrounding rock in the underground cavern, and the proposed method is verified with the field monitoring data.
Bayesian Estimation for Probability Distribution of Rock’s Elastic Modulus Based on Compression Wave Velocity and Deformation Warning for Large Underground Cavern
https://orcid.org/0000-0001-6039-9429
Abstract The elastic modulus of rock is the key parameter in excavation deformation prediction, support design, and the stability analysis of underground engineering. However, traditional statistical methods require a large number of laboratory or field tests to obtain its probability distribution form and distribution parameters, which is difficult in some projects. To overcome this problem, a new method based on Bayesian theory is developed to infer the rock elastic modulus probability distribution using the compression wave velocity of rock. The test data collected from the Firuzkoy area of Istanbul are used for method validation, the results of the developed method are compared with a traditional regression model to demonstrate the advantage under small sample conditions. And the effects of different prior ranges and forms on the evaluation results of the elastic modulus are also studied. Furthermore, the developed method is applied to obtain the probability distribution of the elastic modulus at the Yingliangbao hydropower station, and the safety warning indexes in the main powerhouse are formulated based on the displacement probability quantile. Compared with the field monitoring data, the consistency in excavation displacement indicates that the acquired elastic modulus of rock is reasonable for deformation probability estimation and safety warning in underground caverns.
Highlights A method based on Bayesian framework is developed to estimate the distribution of rock’s elastic modulus using the wave velocity, which can effectively improve the evaluation ability under the conditions of small samples.The effects of different input data, different prior ranges, and different prior forms on the evaluation results of elastic modulus are studied, which show that the advantage of the proposed method is general and applicable for different amounts of input data and different types of prior.A probability warning index is proposed based on the occurrence probability of displacement, which can be used to recognize the unstable deformation of surrounding rock in the underground cavern, and the proposed method is verified with the field monitoring data.
Bayesian Estimation for Probability Distribution of Rock’s Elastic Modulus Based on Compression Wave Velocity and Deformation Warning for Large Underground Cavern
Liu, Jian (Autor:in) / Jiang, Quan (Autor:in) / Chen, Tao (Autor:in) / Yan, Shengcun (Autor:in) / Ying, Jianhui (Autor:in) / Xiong, Xiantao (Autor:in) / Zheng, Hong (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
Deformation behaviour of a large underground cavern
| Springer Verlag | 1985
Deformation behaviour of a large underground cavern
| Online Contents | 1985
Estimation of Deformation Modulus of Rock Mass for an Underground Cavern Based on Back Analysis
| Springer Verlag | 2022
Estimation of Modulus of Deformation by Different Methods for an Underground Cavern—A Case Study
| Springer Verlag | 2023
Rock's Acoustic Emission and Damage Deformation Characteristic in Uniaxial Compression Condition
| British Library Conference Proceedings | 2012