GAN inversion method of an initial in situ stress field based on the lateral stress coefficient

Li, Qian; Yao, Tianzhi; Mo, Zuguo; Zhang, Jianhai; Li, Yonghong; Zhang, Ru; Xu, Nuwen; Li, Zhiguo

doi:10.1038/s41598-021-01307-1

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…The results show that the GMDH algorithm has better nonlinear data predictability than the BP neural network. Qian et al [37] simulated the evolution process of the river valley during excavation by establishing a three-dimensional numerical model of the Shuangjiangkou underground hydropower station. They inputted coordinates, current buried depth, and current lateral stress coefficient into the GAN model as training samples.…”

Section: Intelligent Inversion Analysis Methodsmentioning

confidence: 99%

Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review

et al. 2023

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With the rise of the fourth industrial revolution, traditional methods of analyzing investment have been transformed into intelligent methods under big data and the Internet of Things. This has created a new approach to solving practical engineering problems. This paper examines the formation and evolution of the application of inversion theory in tunnel and underground engineering, summarizing research progress using traditional and intelligent inversion analysis methods to identify three types of target unknown quantities in tunnels and underground projects: initial ground stress, support structure load, and tunnel characteristic parameters. It also offers an outlook on how to optimize inversion analysis methods to solve more challenging and complex tunneling problems in the context of informatization, digitalization, and intelligence. In the current research process of tunnel and underground space engineering problems, the inversion theory system has been improved, but inversion analysis methods still face many challenges. These include the low reliability of initial ground stress inversion under complex geological conditions, the lack of indicators to objectively evaluate the accuracy of inversion analysis, and the high costs of intelligent inversion analysis means. Moving forward in the context of big data and the information era, the future development direction for inversion theory and inversion methods in tunnel and underground space engineering is to combine new monitoring technology, computer vision technology, and simulation analysis technology to establish multifaceted intelligent inversion analysis models.

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Section: Intelligent Inversion Analysis Methodsmentioning

confidence: 99%

Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review

et al. 2023

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“…and local topography [57]. From the top to the bottom of the surface, σ x , σ y and σ z increase with depth.…”

Section: Plos Onementioning

confidence: 97%

The criterion for dividing the surrounding rock EDZs of underground caverns based on the energy dissipation degree

Wang

Zhang

et al. 2023

PLoS ONE

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An energy calculation parameter named the energy dissipation degree (RUd) is introduced based on the analysis of the energy dissipation mechanism and energy evolution characteristics during conventional triaxial tests of the granite of Shuangjiangkou. The deviatoric stress‒strain curve of rock can be divided into five stages using four stress thresholds (crack closure stress σcc, crack initiation stress σci, damage stress σcd and peak stress σp), which also correspond to the four RUd thresholds (RUdc, RUdi, RUdd and RUdp) on the energy dissipation degree–strain curve. A given stress threshold increases with increasing confining pressure; however, a given RUd threshold is basically stable under different confining pressures. Then, a new criterion for dividing the excavation damaged zones (EDZs) in the rock surrounding underground caverns based on the monotonically increasing characteristics of the energy dissipation degree‒axial strain relationship curve is proposed, and it allows for the classification of the surrounding rock into five types of zones through quantitative analysis of the RUd thresholds. Based on the criterion for dividing the EDZs of the surrounding rock mass of the underground cavern, the EDZs of the surrounding rocks of the underground cavern group of the Shuangjiangkou Hydropower Station are analyzed. The distribution characteristics of the EDZs of the rock surrounding underground caverns obtained by numerical simulation calculations based on RUd are basically the same as those obtained by in situ elastic wave tests. However, the RUd-based method for classifying the EDZs of the surrounding rock has the obvious advantage of being able to probe the boundaries of the undamaged zone (UDZ) of the surrounding rock more explicitly, while the method based on wave velocity testing is not sufficiently explicit. The damage zoning of the surrounding rock based on RUd can provide support design advice for the excavation of the surrounding rock, such as the support method, the length of the free section and anchor section of the prestressing anchor, etc.

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“…[33][34][35] Some researchers obtained dataset through numerical simulation based on the multi-scale hydrogel fracture model, for prediction of the fracture behavior of hydrogel by CNN. [36,37] GAN, which trained two neural network models simultaneously, namely the generator and the discriminator, [38] was implemented for composites, such as generating new structure with targeted mechanical properties, [39,40] predicting the microscale elastic strain field, [33] and topology optimization. [41] In this paper, an inverse structure design strategy is proposed through a DL approach for meta-fiber reinforced hydrogel composites with targeted mechanical field.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

Liu,

Zhang,

Liu

et al. 2024

Advanced Science

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Fiber‐reinforced hydrogel composites are widely employed in many engineering applications, such as drug release, and flexible electronics, with more flexible mechanical properties than pure hydrogel materials. Comparing to the hydrogel strengthened by continuous fiber, the meta‐fiber reinforced hydrogel provides stronger individualized design ability of deformation patterns and tunable stiffness, especially for the elaborate applications in joint, cartilage, and organ. In this paper, a novel structure design strategy based on deep learning algorithm is proposed for hydrogel reinforced by meta‐fiber to achieve targeted mechanical properties, such as stress and displacement fields. A solid mechanic model for meta‐fiber reinforced hydrogel is first developed to construct the dataset of fiber distribution and the corresponding mechanical properties of the composite. Generative adversarial network (GAN) is then trained to characterize the relationship between stress or displacement field, and meta‐fiber distribution. The well‐trained GAN is implemented to design meta‐fiber reinforced hydrogel composite structure under specific operation conditions. The results show that the deep learning method may efficiently predict the structure of the hydrogel composite with satisfied confidence, and has great potential for applications in drug delivery and flexible electronics.

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GAN inversion method of an initial in situ stress field based on the lateral stress coefficient

Cited by 8 publications

References 33 publications

Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review

Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review

The criterion for dividing the surrounding rock EDZs of underground caverns based on the energy dissipation degree

Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

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