Rock mass utilization for the foundation surfaces of high arch dams in medium or high geo-stress regions: a review

Shen, Xiaoming; Niu, Xinjian; Lu, Wenbo; Chen, Ming; Peng, Yan; Wang, Gaohui; Leng, Zhendong

doi:10.1007/s10064-016-0892-4

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…e above loading and calculation are implemented by FLAC 3D . e optimal c k value can be obtained by selection, crossover, and mutation in GA. e stress components of each element in the numerical model can be expressed as equation (2); an approximate stress field may be thus obtained.…”

Section: Approximate Stress Field Obtained By the Geneticmentioning

confidence: 99%

“…In situ stress is the most basic engineering load in geotechnical engineering and is the main basis for underground engineering design, excavation, and stability analysis [1,2]: this stress exerts a great influence on the failure and deformation of the rock mass [3]. erefore, it is essential to simulate the distribution of the in situ stress field, which is influenced by a number of factors, including topography, lithology, geologic structure, gravity, and groundwater [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion

Yao

et al. 2021

Advances in Civil Engineering

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The in situ stress field is the fundamental factor causing deformation and damage in geotechnical engineering, so it is the main basis for underground engineering design and excavation. However, it is difficult to accurately obtain the in situ stress through most existing inversion methods in areas with complex geological conditions. For the problem of a relatively discrete and nonlinear relationship of measured stress in the Yebatan Hydropower Station area, a new in situ stress inversion method called the local stress field correction (LSFC) method combining a genetic algorithm (GA), backpropagation (BP) neural network, and submodel method is proposed. The inverted in situ stress results produced by this method show that the distribution of in situ stress is greatly influenced by tectonic movements in the Yebatan area, there is no obvious linear relationship with depth, and the stress release phenomenon occurs at the faults. By comparison with the multiple regression method, it is found that the method still has high inversion accuracy under complex geological conditions, and the average relative error of LSFC inversion results is 17.05%, which is much lower than the value of 43.58% via the multiple regression method. Therefore, the LSFC method can be used for the inversion of in situ stress in complex geological regions and provide a reference for engineering design and construction.

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Section: Approximate Stress Field Obtained By the Geneticmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion

Yao

et al. 2021

Advances in Civil Engineering

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“…Geostatic stress (geo-stress) is the natural stress existing in rock and soil structure, also known as the initial stress, absolute stress, or original rock stress of rock and soil mass [1], which is the fundamental factor causing deformation and failure of underground engineering [2]. In the numerical analysis of geotechnical engineering, the geostatic stress field procedure is used to verify that the initial geostatic stress field is in equilibrium with applied loads and boundary.…”

Section: Introductionmentioning

confidence: 99%

Research on the Geostatic Stress Field Procedure under Complex Conditions

Wang

Yuan

et al. 2021

Advances in Civil Engineering

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Geostatic stress field procedure is the first and the most important step for the numerical simulation of geotechnical engineering, which greatly influences the simulation results. Traditional methods often fail when the model is complex. In this paper, based on finite element method (FEM) software ABAQUS, failure reasons of four commonly used methods for the geostatic stress field are studied. According to the analysis results, a new set of methods, which can provide reasonable displacement and stress field results under complex conditions, is proposed. The proposed methods follow the principle that the stress of different materials should be obtained separately to avoid stress distortion. Then, the accuracy and applicability of the proposed method are verified through a comparison study and a specific application. This study provides a theoretical basis for the method of geostatic stress field procedure under complex condition and can serve as a reference for relevant studies.

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“…The large-scale hydropower projects in complex geological conditions will be affected such as the in-situ stress field, seepage field, rock mechanical properties. The initial in-situ stress is a difficult and critical point in studying geotechnical and hydraulic engineering [1], [2]. The project practice shows…”

Section: Introductionmentioning

confidence: 99%

Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study

Gan

et al. 2020

IEEE Access

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In mountain and ravine region of southwest China, several hydropower stations have been built in this area. It is the main national base for hydropower energy development, but under the influence of geological structure and surface erosion, the in-situ stress environment in this area is more complex. The main purpose of this paper is to establish the relationship between measured in-situ stress data and numerical calculation by means of neural network. Therefore, it can be used to analyze the distribution characteristics of large-scale in-situ stress and the failure of regional geological bodies under the action of in-situ stress in Xiluodu area. This paper analyzes the geological conditions and the measured data of in-situ stress in Xiluodu area. The results show that there is obvious surface weathering phenomenon in this area, the stress environment is complex. The depth has a positive impact on the level of in-situ stress, but the impact degree is different. The genetic algorithm-BP artificial neural networks (G-P) method is trained by the measured in-situ stress data. Based on the field measurement data, neural network algorithm and numerical simulation technology, the three-dimensional in-situ stress field distribution characteristics of the project area are carried out. The research shows that the scheme of combining the actual measurement, numerical analysis and neural network inversion is reliable; Depth is an important factor affecting the maximum horizontal stress value in Xiluodu area; Under the action of in-situ stress, the fissures of geological body are relatively developed, and three types of original water storage and flow spaces are formed before dam storage period; The flow and storage space of groundwater includes limestone fracture, basalt fracture and slope fracture aquifer; After the impoundment of the reservoir area, the reservoir water is transmitted downward through the vertical fissures, and finally the reservoir water forms a hydraulic connection with the groundwater. After impoundment, the hydrogeological conditions of the reservoir area would be changed. INDEX TERMS In-situ stress, numerical simulation, neural network, in-situ stress inversion, geological rock mass damage, hydraulic connection.

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Rock mass utilization for the foundation surfaces of high arch dams in medium or high geo-stress regions: a review

Cited by 11 publications

References 31 publications

Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion

Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion

Research on the Geostatic Stress Field Procedure under Complex Conditions

Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study

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