Characterization Of Uncertainty Research Articles

Cross-section induced uncertainties in neutron source mission rate calculations

Analysis presented in the paper is focused on the characterization of uncertainties induced by cross-section data, which contributing to the overall uncertainty of the correction factors. Advances in computational methods and computational power shifted the calculation of correction factors among the standard steps of the manganese bath method what decrease an interest about this part of the method. Anyway, with the development of computational tools, the cross-section data were also improved, and new evaluations of nuclear data libraries include more information about the cross-section data covariances. Therefore, the propagation of uncertainties induced by neutron cross-sections can be carried out within standard transport calculation of the manganese sulphate bath model. In this paper, the super-sequence SAMPLER module that implements stochastic techniques is used to assess the uncertainty in computed results. Reaction rates on all nuclides of the solution are computed in 400 cases with uncertain parameters and the results are evaluated by an auxiliary tool. Consideration of nuclear data uncertainty in calculations is a general trend that requires the attention of nuclear researchers and should draw attention in metrology. Additional 1.5 % (one-sigma) uncertainty is introduced to the overall uncertainty through correction factor.

Pushing the high count rate limits of scintillation detectors for challenging neutron-capture experiments

One of the critical aspects for the accurate determination of neutron capture cross sections when combining time-of-flight and total energy detector techniques is the characterization and control of systematic uncertainties associated to the measuring devices. In this work we explore the most conspicuous effects associated to harsh count rate conditions: dead-time and pile-up effects. Both effects, when not properly treated, can lead to large systematic uncertainties and bias in the determination of neutron cross sections. In the majority of neutron capture measurements carried out at the CERN n_TOF facility, the detectors of choice are the C6D6 liquid-based either in form of large-volume cells or recently commissioned sTED detector array, consisting of much smaller-volume modules. To account for the aforementioned effects, we introduce a Monte Carlo model for these detectors mimicking harsh count rate conditions similar to those happening at the CERN n_TOF 20 m flight path vertical measuring station. The model parameters are extracted by comparison with the experimental data taken at the same facility during 2022 experimental campaign. We propose a novel methodology to consider both, dead-time and pile-up effects simultaneously for these fast detectors and check the applicability to experimental data from 197Au(n, γ), including the saturated 4.9 eV resonance which is an important component of normalization for neutron cross section measurements.

Artificial intelligence for risk analysis-A risk characterization perspective on advances, opportunities, and limitations.

Artificial intelligence (AI) has seen numerous applications for risk analysis and provides ample opportunities for developing new and improved methods and models for this purpose. In the present article, we conceptualize the use of AI for risk analysis by framing it as an input-algorithm-output process and linking such a setup to three tasks in establishing a risk description: consequence characterization, uncertainty characterization, and knowledge management. We then give an overview of currently used concepts and methods for AI-based risk analysis and outline potential future uses by extrapolating beyond currently produced types of output. We end with a discussion of the limits of automation, both near-term limitations and a more fundamental question related to allowing AI to automatically prescribe risk management decisions. We conclude that there are opportunities for using AI for risk analysis to a greater extent than is commonly the case today; however, critical concerns about proper uncertainty representation and the need for risk-informed rather than risk-based decision-making also lead us to conclude that risk analysis and decision-making processes cannot be fully automated.

The NuSTAR View of Perseus: The Intracluster Medium and a Peculiar Hard Excess

As the brightest galaxy cluster in the X-ray sky, Perseus is an excellent target for studying the intracluster medium (ICM), but until recently, its active galactic nucleus (AGN) made studies of the diffuse emission near its center nearly impossible to accomplish with NuSTAR, due to the extended wings of NuSTAR's point-spread function. The development of a new open-source software package—nucrossarf—now allows the contribution from point and diffuse sources to be modeled so that scattered light from the AGN can be accounted for. Using this technique, we present an analysis of diffuse hard X-ray (3–25 keV) emission from the ICM using three archival NuSTAR observations of the Perseus cluster. We find a ∼10% excess of emission beyond 20 keV not describable by purely thermal models. By performing similar analyses of AGNs in archival observations, we have characterized the systematic uncertainty of the modeled AGN contribution to be 3.4%. However, in order to explain the excess, the total scattered AGN emission would have to be 39% stronger than we have measured. We test physical explanations for the excess, such as diffuse inverse-Compton emission potentially originating from the radio mini-halo, but we determine that none of the models are compelling. An upper limit on the inverse-Compton flux (≤1.5 × 10−11 erg s−1 cm−2) and a corresponding lower limit on the global magnetic field strength (≥0.35 μG) are derived. We discuss the potential origin and implications of the excess and present our characterization of the nucrossarf systematic uncertainty, which should be useful for future work.

Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

This article focuses on model-based clustering of subjects based on the shared relationships of subject-specific networks and covariates in scenarios when there are differences in the relationship between networks and covariates for different groups of subjects. It is also of interest to identify the network nodes significantly associated with each covariate in each cluster of subjects. To address these methodological questions, we propose a novel nonparametric Bayesian mixture modeling framework with an undirected network response and scalar predictors. The symmetric matrix coefficients corresponding to the scalar predictors of interest in each mixture component involve low-rankness and group sparsity within the low-rank structure. While the low-rank structure in the network coefficients adds parsimony and computational efficiency, the group sparsity within the low-rank structure enables drawing inference on network nodes and cells significantly associated with each scalar predictor. Being a principled Bayesian mixture modeling framework, our approach allows model-based identification of the number of clusters, offers clustering uncertainty in terms of the co-clustering matrix and presents precise characterization of uncertainty in identifying network nodes significantly related to a predictor in each cluster. Empirical results in various simulation scenarios illustrate substantial inferential gains of the proposed framework in comparison with competitors. Analysis of a real brain connectome dataset using the proposed method provides interesting insights into the brain regions of interest (ROIs) significantly related to creative achievement in each cluster of subjects. Supplementary material shows the convergence rate for the posterior predictive density of the proposed model, additional simulation examples with model misspecification, full conditional distributions to run the Markov chain Monte Carlo (MCMC) algorithm and also presents traceplots for various model parameters to demonstrate convergence of the MCMC algorithm.

Robust design optimization of a multi-body system with aleatory and epistemic uncertainty

There are several issues in robust design optimization of the multi-body system with aleatory and epistemic uncertainty, such as the difficulty in the multi-source uncertainty characterization and low efficiency in dynamic modeling, analysis and the corresponding solving strategy for its robust design optimization. To address these problems, this paper firstly constructs a unified analysis framework for aleatory and epistemic uncertainty for the multi-body system based on probability theory and evidence theory. Taking a multi-body system of a shipborne artillery model as an example, the transfer matrix method is next employed to rapidly calculate the dynamic response of the muzzle. The results are compared with those by the finite element method to verify the accuracy and efficiency of the transfer matrix method. A decision model of the robust design optimization with both aleatory and epistemic uncertainty is proposed. Based on the polynomial chaos expansion-Kriging model, subset simulation optimization method and a weighted single-objective solving strategy, the multi-objective robust design optimization problem involving aleatory and epistemic uncertainty is then transformed into a single-objective optimization one. Finally, results of both the multi-objective and weighted single-objective robust design optimization methods for the shipborne artillery model are compared to demonstrate the performance of the proposed method.

Reference materials: A review

Several factors have increased the use of reference materials in laboratories. This can be explained by the fact that the reference materials have several roles, namely: the confirmation of the identity of unknown materials and/or the determination of their properties; the calibration of measuring equipment; the validation of methods; the realization of proficiency tests; etc. To be able to produce and use them, a set of standards and guidelines concerning the subject of reference materials has been established. There are several producers of reference materials in many fields, but finding the right choice is sometimes considered difficult given the multitude of materials to be analyzed that do not correspond perfectly to the reference material, especially in the case of matrices. This makes the market always seek new materials. To develop them, five steps are essential: material preparation, homogeneity study, stability study, characterization, and evaluation of measurement uncertainties. These steps are equally important; the fact of highlighting less than one of them will imply a significant decrease in the quality of the reference material developed. This review seeks to furnish the scientific community with a paper elucidating the functions of these materials in research laboratories, the normative references devised to standardize their production and utilization, the factors influencing their production, and the essential steps for their development.

Coupling state‐of‐the‐art modelling tools for better informed Red List assessments of marine fishes

Abstract In the face of biodiversity loss worldwide, it is paramount to quantify species' extinction risk to guide conservation efforts. The International Union for the Conservation of Nature (IUCN)'s Red List is considered the global standard for evaluating extinction risks. IUCN criteria also inform national extinction risk assessments. Bayesian models, including the state‐of‐the‐art JARA (‘Just Another Red List Assessment’) tool, deliver probabilistic statements about species falling into extinction risk categories, thereby enabling characterisation and communication of uncertainty in extinction risk assessments. We coupled the state‐of‐the‐art VAST (‘Vector Autoregressive Spatio‐Temporal’) modelling tool and JARA, for better informed Red List assessments of marine fishes. In this framework, VAST is fitted to scientific survey catch rate data to provide indices to JARA whose uncertainty is propagated to JARA outcomes suggesting extinction risk categories (under the population reduction criterion). In addition, VAST delivers a valuable habitat assessment to better understand what may be driving extinction risk in the study region. Here, we demonstrate the coupled VAST‐JARA modelling framework by applying it to five contrasting North Sea species, with or without a quantitative stock assessment and with different conservation statuses according to the latest global Red List assessments. The North Sea application coupled with previous assessments and studies suggest that, among the three elasmobranchs, starry ray is in most need of urgent research (and conservation actions where appropriate), followed by spurdog, while lesser‐spotted dogfish is increasing in biomass. Moreover, both the VAST‐JARA modelling framework and previous research indicate that, while European plaice is not of conservation concern, cod has likely met the IUCN criteria for being listed as Endangered recently. Synthesis and applications. The predictions of the VAST‐JARA modelling framework for North Sea species, including JARA output and VAST habitat assessment, constitute valuable supporting information to make interpretations based on Red List guidelines, which will help decision‐makers in their next North Sea Red List assessment. We foresee applications of the modelling framework to assist Red List assessments of numerous marine fishes worldwide. Our modelling framework has many potential advantageous uses, including informing resource management about climate change impacts on species' extinction risks.

Evaluating the performance of Bayesian geostatistical prediction with physical barriers in the Chesapeake Bay.

The Chesapeake Bay is one of the most widely studied bodies of water in the United States and around the world. Routine monitoring of water quality indicators (e.g., salinity) relies on fixed sampling stations throughout the Bay. Utilizing this rich monitoring data, various methods produce surface predictions of water quality indicators to further characterize the health of the Bay as well as to support wildlife and human health research studies. Bayesian approaches for geostatistical modelling are becoming increasingly popular and can be preferred over frequentist approaches because full and exact inference can be computed, along with more accurate characterization of uncertainty. Traditional geostatistical prediction methods assume a Euclidean distance between two points when characterizing spatial dependence as a function of distance. However, Euclidean approaches may not be appropriate in estuarine environments when water-land boundaries are crossed during the modelling process. In this study, we compare stationary and barrier INLA geostatistical models with a classic kriging geostatistical model to predict salinity in the Chesapeake Bay during 4 months in 2019. Cross-validation is conducted for each approach to evaluate model performance based on prediction accuracy and precision. The results provide evidence that the two Bayesian-based models outperformed ordinary kriging, especially when examining prediction accuracy (most notably in the tributaries). We also suggest that the non-Euclidean model accounts for the appropriate water-based distances between sampling locations and is likely better at characterizing the uncertainty. However, more complex bodies of water may better showcase the capabilities and efficacy of the physical barrier INLA model.

Disinfection scheduling in water distribution networks considering input time-delay uncertainty

Abstract A significant challenge when attempting to regulate the spatial-temporal concentration of a disinfectant in a water distribution network is the large and uncertain delay between the time that the chemical is injected at the input node and the time that the concentration is measured at the monitoring output nodes. Uncertain time delays are due to varying water flows, which depend mainly on consumer water demands. Existing approaches cannot guarantee that the concentration of the disinfectant will remain within a specified range at the output, even though bounds on time-delay uncertainty may be known. In this work, given bounded water-flow uncertainty, we use the input–output modeling approach to develop a disinfectant scheduling methodology that guarantees a bounded output disinfectant concentration. The proposed methodology creates an input–output model uncertainty characterization by utilizing estimated bounds on water-quality states using the backtracking approach. An optimization problem is formulated and solved to find an input schedule that keeps the disinfectant concentration within predefined bounds for a specified time horizon. Simulation results in two case studies where water demands varied between ±20% of their nominal value show that the proposed scheduler is able to avoid lower bound violations of disinfectant concentration.

Geotechnical reliability‐based design optimization updating under changing design scenarios

AbstractIn the context of geotechnical uncertainty characterization, the reliability‐based design optimization (RBDO) provides an effective means to achieve the most balanced design outcome between the targeted safety and the risk‐based cost planning. As a common practice, the geotechnical RBDO is conducted on the premise of prescribed statistical information of those uncertain parameters. Nevertheless, the uncertain geotechnical parameters are usually subject to amendment resulting from further site investigation. In other words, changing design scenarios must be properly handled in advanced geotechnical RBDO. Thus, in this study, a novel reliability updating method using a sample reweighting technique that requires only a single simulation run is developed for RBDO. Simultaneously, the RBDO is readily extendable to RBDO updating under changing design scenarios while avoiding any further re‐evaluation of the performance functions. The proposed RBDO updating method allows for the consideration of a continuous design space encompassing an infinite number of feasible designs, whereas the existing simulation‐based method can only consider a finite number of feasible designs in a discrete design space. Moreover, the efficiency and flexibility of the proposed RBDO updating method are illustrated through a standard RBDO test problem and a practical rock slope design example, where the changing design scenarios of different means, standard deviations, correlation coefficient, and distributions are respectively studied.

Linking hazard intensity to impact severity: mini review of vulnerability models for volcanic impact and risk assessment

Volcanic eruptions can cause significant impacts on communities and infrastructure. There is an increasing need for effective risk assessments to inform decision-making and minimise the impact of volcanic hazards. Vulnerability models play a crucial role in these assessments, connecting the intensity of the hazard with the elements that are exposed to it, allowing for the calculation of potential impact or risk. There has been a large increase in the number of vulnerability models being developed for volcanic risk applications, and there is now a need to identify knowledge gaps for the field to take a strategic approach moving forward. This review aims to provide a high-level overview of the current state of volcanic vulnerability modelling and identify areas for future development. We evaluated 594 vulnerability models covering a range of elements and sectors, including buildings, critical infrastructure, transportation networks, agriculture, and human vulnerability. We reviewed the types of hazard intensity metrics and impact/risk metrics used in the models, modelling methodologies, underpinning data requirements, and uncertainty characterisation. A global clearinghouse for volcanic vulnerability models would be advantageous for the volcanic risk community to identify appropriate vulnerability models quickly and efficiently for their needs. As a first step towards such a clearinghouse, we have uploaded this volcano vulnerability model compilation to a repository and encourage additions/suggestions from the community on its future development. The results of this study will contribute to the advancement of the field and provide valuable insights for future research and development in volcanic risk assessment.

Characterization of model uncertainty of the p-y method for reliability-based design of offshore wind turbines under the serviceability limit state

Recent studies show that the API p-y method may underestimate the pile-soil initial stiffness of offshore wind turbine (OWT) monopiles in clay while overestimate that in sand. To facilitate the reliability-based design of OWT monopiles with the API p-y design method, a two-stage evaluation method of the model bias factor for the p-y method (εpy) of monopiles under the serviceability limit state (SLS) was presented. The model bias factor εpy is derived by comparing field test data with high-fidelity 3D finite element model (FEM) results, and the p-y method predictions, addressing the issues of limited field test data for OWT monopiles. Results show that the mean of the model bias factor εpy depends on the characteristics of piles, the soil properties, and the composition of the layered ground. The systematic dependency is expressed by a trend function and the residual model bias factor follows a lognormal distribution with a mean of 1.01 and a COV of 0.14. The significance of the model uncertainty is illustrated using a case study. The results of the field example show that the design in clay sites may be considerably conservative and may be less conservative in sand sites if ignoring the model uncertainty. The result of this work can provide a basis for promoting reliability-based design for OWT monopiles.

Capturing Directivity in Probabilistic Seismic Hazard Analysis for New Zealand: Challenges, Implications, and a Machine Learning Approach for Implementation

ABSTRACT The proximity of fast-slipping crustal faults to urban areas may result in pulse-like ground motions from rupture directivity, which can contribute to increased levels of damage even for engineered structures. Systematic modeling of directivity within probabilistic seismic hazard analysis (PSHA) remains challenging to implement at the regional scale, despite the availability of directivity models in the literature. In the process of developing the 2022 National Seismic Hazard Model for New Zealand (2022 NSHM), we explored the feasibility and impact of modeling directivity for PSHA at a national scale using the previous generation 2010 NSHM. The results of this analysis allowed us to quantify the impact of directivity on the resulting seismic hazard maps for New Zealand and gain insights into the factors that contribute to the expected increases (and decreases) in ground-motion level. For the 2022 NSHM, the earthquake rupture forecast (ERF) seismogenic source models introduced enormous challenges for directivity modeling due to the abundance of large multisegment or multifault ruptures with complex geometries. To overcome these challenges, we applied a machine learning-based strategy to “overfit” an artificial neural network to capture the distributions of directivity amplification and its variability for each unique rupture in the earthquake rupture forecast. This produces a compact representation of the spatial fields of amplification that are computationally efficient to generate within a complete PSHA calculation for the 2022 NSHM. This flexible and reproducible framework facilitates the implementation of directivity in PSHA at a regional scale for complex ERF source models and opens up the possibility of more complex characterization of epistemic uncertainties for near-source ground motion in practice.

Characterization of geological uncertainties from limited boreholes using copula-based coupled Markov chains for underground construction

This paper proposes an efficient method for quantifying the stratigraphic uncertainties and modeling the geological formations based on boreholes. Two Markov chains are used to describe the soil transitions along different directions, and the transition probability matrices (TPMs) of the Markov chains are analytically expressed by copulas. This copula expression is efficient since it can represent a large TPM by a few unknown parameters. Due to the analytical expression of the TPMs, the likelihood function of the Markov chain model is given in an explicit form. The estimation of the TPMs is then re-casted as a multi-objective constrained optimization problem that aims to maximize the likelihoods of two independent Markov chains subject to a set of parameter constraints. Unlike the method which determines the TPMs by counting the number of transitions between soil types, the proposed method is more statistically sound. Moreover, a random path sampling method is presented to avoid the directional effect problem in simulations. The soil type at a location is inferred from its nearest known neighbors along the cardinal directions. A general form of the conditional probability, based on Pickard's theorem and Bayes rule, is presented for the soil type generation. The proposed stratigraphic characterization and simulation method is applied to real borehole data collected from a construction site in Wuhan, China. It is illustrated that the proposed method is accurate in prediction and does not show an inclination during simulation.

AutoSourceID-FeatureExtractor

Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources’ features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data. Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment. Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.

OP46 The Decision Uncertainty Toolkit: Risk Measures And Visual Outputs To Support Health Technology Decision-making During Public Health Crises

IntroductionDuring public health crises such as the COVID-19 pandemic, decision-makers have relied on infectious disease models to predict and estimate the impact of various health technologies. The difficulties associated with capturing and representing uncertainty using infectious disease models leads to a high risk of making decisions that are misaligned to policy objectives. Even when uncertainty is adequately captured in the analysis, the tools for communicating the risks and harms of making wrong decisions have proved inadequate, which can lead to the suboptimal adoption of critical health technologies including vaccines and antivirals. We aim to adapt and extend health economic methods for the characterization, estimation, and communication of uncertainty to infectious disease modeling.MethodsEconomic and infectious disease models share many features, including the comparison of policy alternatives on outcomes important to decision-makers (such as hospital census, total infections), but each takes a different approach to analysis of uncertainty. We extend best practices from health economics to infectious disease modeling and develop a suite of tools and visualization techniques which represent parameter uncertainty and the risk these unknowns present to decision-makers.ResultsIn consultation with decision-makers and infectious disease modeling experts we developed the ‘Decision Uncertainty Toolkit’ of model outputs and visuals. Visual tools for uncertainty are developed to: (i) accurately capture uncertainty in key infectious disease model outputs, and (ii) support intuitive and direct interpretation by infectious disease modelers and decision-makers. We also developed quantitative measures for the downside risk of policy alternatives, specified to capture both the probability and magnitude of losses relative to policy targets for a range of infectious disease model outputs. Together, these outputs can support decision-making by quantifying outcome uncertainty and the risks associated with policy alternatives.ConclusionsWe developed the toolkit visuals and risk measures alongside infectious disease modelers and decision makers. The toolkit is designed to improve decision-maker understanding of decision risk in order to improve outcomes during future public health crises.

Objective satellite methods including AI algorithms reviewed for the tenth International workshop on tropical cyclones (IWTC-10)

Here we explore the latest four years (2019–2022) of using satellite data to objectively analyze tropical cyclones (TC) and issue recommendations for improved analysis. We first discuss new methods of direct retrieval from SAR and geostationary imagers. Next, we survey some of the most prominent new techniques in AI and discuss their major capabilities (especially accuracy in nonlinear TC behavior, characterization of model uncertainty and creation of synthetic satellite imagery) and limitations (especially lack of transparency and limited amount of training data). We also identify concerns with biases and unlabeled uncertainties in the Best Track records as being a first-order limitation for further progress in objective methods. The article concludes with recommendations to improve future objective methods, especially in the area of more accurate and reliable training data sets.

Identifying representative days of solar irradiance and wind speed in Brazil using machine learning techniques

The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades, following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation mix. From a long-term perspective, the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years. In this context, the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of Brazil. Leveraging on unsupervised Machine Learning methods, we focus on identifying similar days over the years, Representative Days, that can depict the fundamental underlying behaviour of each source. The analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country: three in the Northeast and one in the South Regions, for wind speed; and three in the Northeast and one in the Southeast Regions, for solar irradiance. We use two Partitioning Methods (K-Means and K-Medoids), the Hierarchical Ward’s Method, and a Model-Based Method (Self-Organizing Maps). We identified that wind speed and solar irradiance can be effectively represented, respectively, by only two representative days, and two or three days, depending on the region and method (segments data with respect to the intensity of each source). Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.

Dynamic prediction of over-excavation gap due to posture adjustment of shield machine in soft soil

The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling. The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil. More specifically, ω, which is a gap parameter component defined as the over (or insufficient) excavation due to the change in the posture of the shield machine, may contribute more to the uncertainty of the ground loss. However, the existing uncertainty characterization methods for ω have several limitations and cannot explain the uncertain correlations between the relevant parameters. Along these lines, to better characterize the uncertainty of ω, the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it. To attain this goal, 1 523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction, stratigraphic, and posture parameters were collected to form the database. According to the variance filter method, the mutual information method, and the value of the correlation coefficients, the original 44 parameters were reduced to 10 main parameters, which were unit weight, the stoke of the jacks (A, B, C, and D groups), the pressure of the pushing jacks (A, C groups), the chamber pressure, the rotation speed, and the total force. The multivariate probability distribution was constructed based on the Johnson system of distributions. Moreover, the distribution was satisfactorily verified in explaining the pairwise correlation between ω and other parameters through 2 million simulation cases. At last, the distribution was used as a prior distribution to update the marginal distribution of ω with any group of the relevant parameters known. The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.