A new hybrid copula‐based nonparametric Bayesian model for risk assessments of water inrush

Sun, Jinglai; Wang, Fan; Li, Zequan; Ren, Darui; Yu, Mingyuan

doi:10.1002/qre.3053

Cited by 13 publications

(5 citation statements)

References 56 publications

(132 reference statements)

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“…Seepage instability occurs, presenting as nonlinear. Develi and Babadagli have observed a similar phenomenon in their experiments, and this phenomenon has also widely appeared in water conservancy engineering, oil and gas exploitation, geothermal energy extraction, and tunnel rock engineering [26,33,[39][40][41][42]. Some believe that nonlinear flow is caused by strong solid-liquid interaction in rocks with low permeability, micro-fine fractures, and porous media [18,26,27].…”

Section: Seepage Characteristicsmentioning

confidence: 80%

Nonlinear Seepage Behaviors of Pore-Fracture Sandstone under Hydro-Mechanical Coupling

Zhang

Guo

et al. 2022

Crystals

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This work focused on the nonlinear seepage behaviors of flow in pore-fracture media. Natural sandstones were selected to prefabricate single-fracture specimens with different inclinations (0–90°). Seepage tests of combined media were performed under different confining pressures (8–10 MPa) and different water pressures (3–7 MPa) in a triaxial pressure chamber. The fitting analysis of experimental data showed that Forchheimer’s law described the nonlinear characteristics of flow in the pore-fracture media. Linear term coefficient a and nonlinear term coefficient b of the sandstone samples with different inclinations changed more obviously with the increased inclination. When the fracture inclination was greater than 30°, a and b values had a sudden jump. The nonlinear inertial-parameter equation of fluid flow in pore-fracture media was proposed based on non-Darcy flow coefficient β and inherent permeability k. The applicability of the following methods to evaluate Darcy’s law was discussed, including normalized hydraulic conductivity, pressure gradient ratio, and discharge ratio. The three methods were able to determine critical parameters and distinguish linear and nonlinear flow. Furthermore, it was specified for the first time that when β was negative, critical nonlinear effect E was −0.1, and Forchheimer’s coefficient F0 was −0.091. In the −∇P-Q relationship, the fitting curve was convex to the −∇P axis, and the increase of Q was higher than the linear increase, presenting the nonlinearity of overflow. On the one hand, the fractures and pores were compressed under the confining pressure due to the prefabricated fractures of different shapes and different inclinations. A higher seepage water pressure was needed to stabilize the seepage system with the excessive flow rate. On the other hand, the barrier effect of the fluid inside the rock was completely lost because the fluid expanded the seepage channel. Its permeability was changed, leading to seepage instability.

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Section: Seepage Characteristicsmentioning

confidence: 80%

Nonlinear Seepage Behaviors of Pore-Fracture Sandstone under Hydro-Mechanical Coupling

Zhang

Guo

et al. 2022

Crystals

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“…Due to the advantages in identifying critical nodes, clustering expert knowledge, etc., interpretive structural model (ISM) can be well applied in the hybrid approaches (Sezer et al ., 2022; Sun et al ., 2022; Wu et al ., 2022). An ISM is a structural model that applies a matrix representation of directed graphs and simple logical operations to analyze large complex systems (Warfield and Fitz, 1977).…”

Section: Literature Reviewmentioning

confidence: 99%

A decision support method for credit risk based on the dynamic Bayesian network

Lu,

Wu,

Dong

et al. 2023

IMDS

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PurposeCredit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.Design/methodology/approachFirst, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.FindingsCompared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.Originality/valueThe authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

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“…Pan et al [29] developed a structural health model of underwater tunnels. Sun et al [30] constructed a network model to assess the risk of water inrush for a tunnel. As for the research on the information fusion of vine-BN, to our knowledge, there has been no literature published yet.…”

Section: Introductionmentioning

confidence: 99%

Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network

Cheng

Yang

2023

International Journal of Aerospace Engineering

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The landing phase of an airdrop process is prone to accidents, and thus, it is important to assess the landing reliability for an airdrop system. However, full field tests to assess the reliability are unacceptable due to their cost and the time required. As such, it is necessary to estimate the reliability in the design stage. To address this problem, a method based on vine-Bayesian Network (vine-BN) is proposed to assess the landing reliability by fusing multisource information. First, the network structure is determined by the relationship between data of simulation or ground tests and failure modes. Then, nodes are defined as random variables on [0, 1] based on the definition of the performance metric. Finally, the dependence between nodes is quantified by expert opinions. To illustrate the effectiveness of the method, a particular ground test or simulation is chosen to establish a network for a typical heavy cargo airdrop system (HCADS). Forward and backward propagation is carried out on the network. The forward analysis predicts the landing reliability in the design stage through multisource information fusion. Beta distribution is applied to fit the fusion result, so Bayesian inference is made to perform field test times decision-making. The backward analysis works to identify the key performance metrics related to landing reliability. The results and analysis manifest that vine-BN is feasible for fusing multisource information. Through the network, the reliability of the current design can be predicted effectively, and the field test times can be remarkably reduced. This method plays a crucial role in airdrop system design and reducing test time and labor.

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A new hybrid copula‐based nonparametric Bayesian model for risk assessments of water inrush

Cited by 13 publications

References 56 publications

Nonlinear Seepage Behaviors of Pore-Fracture Sandstone under Hydro-Mechanical Coupling

Nonlinear Seepage Behaviors of Pore-Fracture Sandstone under Hydro-Mechanical Coupling

A decision support method for credit risk based on the dynamic Bayesian network

Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network

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