Structural Uncertainty Research Articles

Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

Evaluating the impact of multi-source uncertainties in complex forecasting systems is essential to understanding and improving the systems Previous studies have paid little attention to the influence of multi-source uncertainties in complex meteorological and hydrological forecasting systems. In this study, we developed a general ensemble framework based on Bayesian model averaging (BMA) for evaluating the impact of multi-source uncertainties in complex forecast systems. Based on this framework, we used eight numerical weather prediction products from the International Grand Global Ensemble (TIGGE) dataset, four hydrological models with different structures, and 1000 sets of parameters to comprehensively account for the input, structure, and parameter uncertainties. The framework’s application to the Chitan Basin in China revealed that the numerical weather prediction input uncertainty in the forecasting system was more significant than the hydrological model uncertainty. The hydrological model structure uncertainty was more prominent than the parameter uncertainty. The accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows. In addition, the structures and parameters of the hydrological model and their interactions contributed to the main uncertainty of the low flow forecasts. The streamflow was more realistically represented when the three uncertainty sources were considered jointly. By accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting produces more realistic and reliable predictions and reduces the influences of other incomplete considerations. The developed multi-source uncertainty assessment framework improves our understanding of the complex meteorological and hydrological forecasting system. Therefore, the framework is promising for improving the accuracy and reliability of complex forecasting systems.

Quantification and propagation of Aleatoric uncertainties in topological structures

Quantification and propagation of Aleatoric uncertainties in topological structures

Stochastic velocity modeling for assessment of imaging uncertainty during seismic migration: Application to salt bodies

Variations in the migration velocity model directly affect the position of the imaged reflectors in the subsurface, leading to structural imaging uncertainties. These uncertainties are not explicitly addressed when trying to deterministically build an adequate velocity model. This paper presents a new stochastic geology-controlled velocity modeling method that handles the possible presence of a salt weld. This permits the generation of a large set of geologic scenarios and associated velocity models. Each model is used to remigrate the seismic data. Then, a statistical analysis of the resulting seismic images is performed to quantify the local variability of the seismic responses. The approach is applied to the imaging of salt diapirs in an iterative scheme (migrate, pick, and update). The results indicate that similar to stacking common-midpoint gathers, the statistical analysis preferentially preserves recurrent features from one image to another. In particular, this analysis permits the distinction between the connected and detached diapirs without prior knowledge about their connectivity, highlighting the potential of the method to resolve important aspects of the basin and reservoir architecture. More generally, it provides quantitative information on the parts of the seismic image most sensitive to migration velocity variations, which opens an interesting perspective to quantitative interpretation uncertainty assessment. Finally, the presented application also suggests that it is possible to significantly improve the quality of the generated seismic images by sampling many possible geologic scenarios.

Uncovering the Hidden Information: A Novel Approach to Modeling Physical Phenomena Through Information Theory

The growing need to study more complex physical phenomena and technological processes determines the importance of reducing the uncertainty of formulated models. However, measurement theory does not provide a clear answer to the question of how to calculate and use model structure uncertainty: the presence of certain base quantities and derived variables. The key novelty of this research lies in the informational method, which allows you to find the value of the uncertainty of the model of the phenomenon that has a certain structure. This uncertainty is initial and precedes the definition of uncertainties associated with the implemented computer algorithms, subsequent experiments, data processing, and the people involved in the study. This article aims to provide a detailed explanation of the informational method and its application for the selection of a model that satisfies the chosen universal criterion of comparative uncertainty. This criterion allows for solving the problem of identifying the preferred model that meets the requirements and philosophical outlook of the observer. So far, for many decades, no efforts have been made to take this uncertainty into account in scientific and technical practice. We applied the information method to analyze the attainable accuracy or perfection of established physical laws in this paper.

Seismic performance of an assembled self-centering buckling-restrained brace with controllable initial stiffness

An innovative assembled self-centering buckling-restrained brace (ASC-BRB) with controllable initial stiffness is introduced. The brace consists of a self-centering system and an energy dissipation system. Unlike existing post-tensioned self-centering energy dissipation braces, whose initial stiffness has high uncertainty and is sensitive to machining errors, the proposed ASC-BRB can obtain highly controllable initial stiffness because of its unique working mechanism. Therefore, structural seismic response uncertainties can be reduced significantly. The configuration, working mechanism, and theoretical restoring force model of the ASC-BRB are first presented. Quasi-static tests are then conducted to examine the feasibility of the brace, and typical flag-shaped hysteretic behavior and stable energy dissipation capability are observed. The main components of the ASC-BRB keep intact after repeated loadings, except for the core plates, which are damaged; however, the core plates can be easily replaced. This observation highlights the superiority of the ASC-BRB in terms of damage concentration and high recoverability. The proposed theoretical restoring force model can accurately predict the hysteretic response of the brace, especially for the initial stiffness, with the predicted value being within 5 % of the experimental value. A simplified numerical model is developed and used in a parametric study conducted to examine the effects of two key design parameters on the brace behavior. Furthermore, the effect of machining errors on the initial stiffness of the ASC-BRB is studied. An analysis of steel frames with ASC-BRBs indicates that the ASC-BRB can effectively reduce the residual inter-story drift and help achieve a peak floor acceleration response comparable to that of the frame with BRBs. However, the peak inter-story drift is amplified owing to the lower initial stiffness of the proposed brace. On the basis of a system-level parametric analysis, recommended optimized design parameters of the ASC-BRB are presented to facilitate efficient control of structural seismic responses.

Greasing the wheel through bribes: interaction of national culture and local business conditions

Purpose: Corruption, which includes payments of bribes to government officials, poses a serious impediment to the advancement of developing countries. In this study, a model is developed based on anomie theory and research on ethical behaviour, culture and decision making to predict whether and to what extent small business owners bribe government officials.Design/methodology/approach: Using hierarchical linear modelling on a large World Bank data set, the study hypothesises that the business environmental conditions of crime prevalence, theft and disorder, transportation difficulties, and time-resource requirements for dealing with the government, function as obstacles that enhance small business owners’ informal payments to government officials. The study further proposes that the national culture constructs of masculinity and power distance moderate the relationship between business environment conditions and small business owners’ bribes.Findings/results: The study found support for the role of the proposed business environment conditions, as well as partial support for the role of masculinity and power distance as factors that strengthen the positive relationship between challenging business environments for small business owners and corrupt behaviour.Practical implications: The study proposes ways governments can address structural uncertainties which are impediments to small business survival. It also offers ways small businesses can counteract cultural and economic challenges that influence corrupt behaviour.Value: This study improves the understanding of the role of the supply-side of corruption. It also explains how the lens of anomie theory leads to a better understanding of the mechanism of action of inducements to deviate from the norm, as happens in corrupt behaviour practices.

Building on Entrepreneurial Team Theory: A Women Context

Using literature on entrepreneurial teams, we examine ways in which technology can be used as a connecting link to build women entrepreneurial teams. The study theorizes that use of technology creates a new realm (structural uncertainty) that gives rise to entrepreneurial women networks and women entrepreneurial opportunities (common interests) which in turn creates strong interdependence that provides a suitable environment for women entrepreneurial teams. We conceptualize that women use of technology for networking and mentoring purposes can be a first step in building the teams.

Robust unified dissipativity vibration control design for offshore steel jacket platform in ocean environments under self-excited nonlinear wave force

This paper addresses the challenge of delayed proportional integral control (DPIC) of an offshore steel jacket platform (OSJP) subjected to a self-excited nonlinear wave force and structural uncertainty using the unified criteria. By introducing discrete and distributed state delays in the control input, a DPIC was established to stabilize the OSJPs. The goal of this study is to design a proper controller that will stabilize the dynamic of an OSJP while subjected to structural uncertainty and nonlinear wave force. The OSJP is investigated as a nonlinear dynamics with mixed state delays, allowing us to study its robust asymptotic stability using the Lyapunov-Krasovskii function (LKF) in the context of the extended dissipativity performance index. A novel closed-loop system-based stability criterion is derived as a result of using tighter integral inequalities to estimate the upper bounds of the delay and influential control gains can be achieved if a set of linear matrix inequalities (LMIs) is checked by simulation results, that the proposed control scheme can significantly improve the control’s performance. Finally, it was demonstrated that the proposed control approach is more effective and multi-dynamic performances have been illustrated through the comparisons to previously published results in the literature.

Bayesian cluster analysis.

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering structure and patterns within each cluster. An overview of Bayesian cluster analysis is provided, including both model-based and loss-based approaches, along with a discussion on the importance of the kernel or loss selected and prior specification. Advantages are demonstrated in an application to cluster cells and discover latent cell types in single-cell RNA sequencing data to study embryonic cellular development. Lastly, we focus on the ongoing debate between finite and infinite mixtures in a model-based approach and robustness to model misspecification. While much of the debate and asymptotic theory focuses on the marginal posterior of the number of clusters, we empirically show that quite a different behaviour is obtained when estimating the full clustering structure. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

A fast design technique for robust industrial controllers

This paper provides a new fast design method for robust industrial controllers via majorant systems in the frequency domain. The proposed methodology allows to establish several fast design techniques for a broad class of industrial controllers of plants with internal and/or external delays, parametric and/or structural uncertainties, and subject to disturbances, when an analytical model of the plant or data acquired from simple experimental tests are available. The provided design and control techniques are more general with respect to the Ziegler-Nichols ones and their numerous variants, which, in some cases, do not guarantee the control system stability.The used key idea consists in increasing the frequency response of the process to be controlled with the frequency response of a simpler system, also of order greater than one, with external delay, which allows designing, using simple formulas, controllers of PI, PID, PIDR, PI2, PI2D, PI2DR, PI2D2, and PI2D2R types. The designed controllers always guarantee stability margins larger than those of appropriate reference systems. Therefore, good performance of robustness of the stability and tracking precision of smooth references, with respect to parametric and/or structural uncertainties and/or smooth disturbances, are always guaranteed.The stated general methodology and various performance comparisons, also about the tracking precision of references with bounded first or second derivative, are illustrated and validated in several case studies, experimentally too.

Comparison of Deterministic and Probabilistic Variational Data Assimilation Methods Using Snow and Streamflow Data Coupled in HBV Model for Upper Euphrates Basin

The operation of upstream reservoirs in mountainous regions fed by snowmelt is highly challenging. This is partly due to scarce information given harsh topographic conditions and a lack of monitoring stations. In this sense, snow observations from remote sensing provide additional and relevant information about the current conditions of the basin. This information can be used to improve the model states of a forecast using data assimilation techniques, therefore enhancing the operation of reservoirs. Typical data assimilation techniques can effectively reduce the uncertainty of forecast initialization by merging simulations and observations. However, they do not take into account model, structural, or parametric uncertainty. The uncertainty intrinsic to the model simulations introduces complexity to the forecast and restricts the daily work of operators. The novel Multi-Parametric Variational Data Assimilation (MP-VarDA) uses different parameter sets to create a pool of models that quantify the uncertainty arising from model parametrization. This study focuses on the sensitivity of the parametric reduction techniques of MP-VarDA coupled in the HBV hydrological model to create model pools and the impact of the number of parameter sets on the performance of streamflow and Snow Cover Area (SCA) forecasts. The model pool is created using Monte Carlo simulation, combined with an Aggregated Distance (AD) Method, to create different model pool instances. The tests are conducted in the Karasu Basin, located at the uppermost part of the Euphrates River in Türkiye, where snowmelt is a significant portion of the yearly runoff. The analyses were conducted for different thresholds based on the observation exceedance probabilities. According to the results in comparison with deterministic VarDA, probabilistic MP-VarDA improves the m-CRPS gains of the streamflow forecasts from 57% to 67% and BSS forecast skill gains from 52% to 68% when streamflow and SCA are assimilated. This improvement rapidly increases for the first additional model parameter sets but reaches a maximum benefit after 5 parameter sets in the model pool. The improvement is notable for both methods in SCA forecasts, but the best m-CRPS gain is obtained for VarDA (31%), while the best forecast skill is detected in MP-VarDA (12%).

Reliability-based structural assessment of historical masonry arch bridges: The case study of Cernadela bridge

Nowadays, several historical masonry arch bridges present a deficient state of conservation due to degradation processes induced by natural or human actions. Usually, these constructions have significant economic, cultural, and heritage value. Therefore, they shall be thoroughly assessed to verify their structural integrity and safety condition. For this purpose, reliability-based structural assessments are typically performed. However, the associated outcomes (i.e., reliability index and probability of failure) highly rely on the accuracy of the structural parameters uncertainty quantification. This work presents a study regarding the influence of the scattering of the arches' thickness dimensions in the load-carrying capacity assessment of the Cernadela Bridge, a historical stone bridge located in Galicia, Spain. The study first involved a comprehensive experimental campaign to characterize the outer and inner bridge geometry through geomatic techniques, i.e., terrestrial laser scanning and ground penetrating radar. Subsequently, a limit analysis model was developed, considering only the arches' outer (visible) data. From this initial structural assessment, a reliability index of 2.38 was obtained. The influence of the uncertain structural parameters, both geometric features and material properties, in the collapse load was investigated through a global variance-based sensitivity analysis (i.e., Sobol' indices) complemented by a surrogate modeling strategy based on the Kriging approach. Finally, the measured inner geometry of the arches was introduced in the computational model through Bayesian inference procedures. Thus, two new structural assessments were performed: first, by considering the updated distributions of all arches thicknesses, and second, by considering only the updated distributions of the non-influential ones. Reliability indexes of 1.51 and 2.33 were derived, thus highlighting the importance of a proper uncertainty quantification process and the relevance of the sensitivity analysis outcomes to identify the critical parameters on the bridge mechanical response.

Capturing and Attributing the Rainfall Regime Intensification in the West African Sahel with CMIP6 Models

Abstract Rainfall in the Sahel is extremely variable on daily to multidecadal time scales, challenging climate models to realistically simulate its past and future evolution and questioning their relevance for defining suitable climate change adaptation strategies. Improving confidence in climate models may be achieved by (i) evaluating their capacity for reproducing observed climatic evolution and (ii) attributing these evolutions. Moreover, there is a need to consider relevant climatic indicators, from an end-user point of view. Fully coupled (CMIP6-AOGCM) models with idealized detection and attribution forcings (DAMIP) as well as atmosphere-only simulations (AMIP) are used to investigate the respective roles of external forcing factors and internal climate variability in the observed intensification of the Sahelian rainfall regime. We show that CMIP6 models contain signs of the intensification of the rainfall regime as detected over the past 35 years from a regional daily observations network. Both the increase in intensity and occurrence of wet days, as well as that of extreme daily rainfall, are remarkably well reproduced by historical simulations incorporating anthropogenic forcing factors, with anthropogenic aerosols contributing the largest share of this trend. Though more strongly affected by model structure uncertainty, the greenhouse gas forcing also displays noticeably robust features. Models are shown to fail at simulating the observed dry extreme evolution. These findings give incentive for further investigating the underlying physical mechanisms that drive the Sahelian rainfall regime evolution at regional to subregional scales. Furthermore, future hydroclimatic trajectories in the Sahel should be explored, though particular caution is required as to which rainfall indicator to consider. Significance Statement The rainfall regime at a particular location is crucial to human and ecosystem livelihoods. Changes in rainfall regime characteristics on multidecadal time scales result from both the effects of external forcing factors on the climate and of its internal variability, with this latter aspect becoming more prominent on small spatial scales. In this study, several state-of-the-art climate simulations are used to document the rainfall regime evolution of the past 65 years in the Sahel, in terms of amplitude, timing, and causes. It is shown that large-scale anthropogenic factors have a substantial imprint, modulated to some extent by internal variability. These findings demonstrate that coarse-resolution climate models are a well-suited tool to investigate the recent intensification of rainfall in the Sahel, and may provide valuable information for climate change adaptation planning.

The Shape of Jupiter and Saturn Based on Atmospheric Dynamics, Radio Occultations and Gravity Measurements

AbstractThe shape of the two gas giants, Jupiter and Saturn, is determined primarily by their rotation rate, and interior density distribution. It is also affected by their zonal winds, causing an anomaly of O(10 km) at low latitudes. However, uncertainties in the observed cloud‐level wind and the polar radius, translate to an uncertainty in the shape with the same order of magnitude. The Juno (Jupiter) and Cassini (Saturn) missions gave unprecedented accurate gravity measurements, constraining better the uncertainty in the wind structure. Using an accurate shape calculation, and a joint optimization, given both gravity and radio‐occultation measurements, we calculate the possible range of dynamical height for both planets. We find that for Saturn there is an excellent match to the radio‐occultation measurements, while at Jupiter such a match is not achieved. This may point to deviations from a barotropic flow above the cloud level, which might be tested with the forthcoming radio‐occultation measurements by Juno.

Adaptive neural network sliding mode control for reliable stabilization of uncertain Takagi–Sugeno fuzzy systems regulated by switching rules

The problem of adaptive sliding mode control for a class of continuous-time Takagi–Sugeno fuzzy systems regulated by the event of switching rules relying on neural network estimation method is put forward in this paper, where the plant suffers from state delay, internal structure uncertainty, and unknown nonlinearity. By proposing a switching surface in integral type, it obtains a sliding motion with desired property. In addition, to compensate the plant unknown nonlinearity and to meet the reaching condition, a radial basis function neural-network-based adaptive law is designed to ensure the existence of sliding motion in finite time. Furthermore, for the purpose of exponential stabilization of the sliding motion, a linear matrix inequality condition accompanied with switching signal characterized by an average dwell time is put forward. Finally, two numerical examples, one with all subsystems unstable and the other with stable subsystems and unstable systems, are shown to confirm the validity.

Flutter analysis including structural uncertainties using a relaxed LMI-based approach

Flutter analysis including structural uncertainties using a relaxed LMI-based approach

Budyko-based approach for modelling water balance dynamics considering environmental change drivers in the Vistula River basin, Poland

ABSTRACT The spatiotemporal variations in the water balance caused by environmental changes lead to unsteady-state conditions with long- and short-term implications on the social and economic conditions. The aim of this study is to extend the current knowledge of the Budyko approach in unsteady-state conditions and the importance of model structural uncertainties. We used three Budyko-based models (Turc-Pike, Zhang, Fu) with and without the concept of effective precipitation scenarios. The effective precipitation and model parameters were determined together during the calibration processes. The effective precipitation-based approach improved the model performance in predicting the annual water balance in most of the River Vistula catchment. We also discuss the importance of considering structural uncertainties in water balance modelling. Climate change is the main driver for the changes in the water balance of the River Vistula. The estimation of water balance dynamics, considering environmental changes, is essential to understand the changes in hydrological processes.

Iterative-AMC: a novel model compression and structure optimization method in mechanical system safety monitoring

With the rapid development of artificial intelligence, various fault diagnosis methods based on the deep neural networks have made great advances in mechanical system safety monitoring. To get the high accuracy for the fault diagnosis, researchers tend to adopt the deep network layers and amount of neurons or kernels in each layer. This results in a large redundancy and the structure uncertainty of the fault diagnosis networks. Moreover, it is hard to deploy these networks on the embedded platforms because of the large scales of the network parameters. This brings huge challenges to the practical application of the intelligent diagnosis algorithms. To solve the above problems, an iterative automatic machine compression method, named Iterative-AMC, is proposed in this paper. The proposed method aims to automatically compress and optimize the structure of the large-scale neural networks. Experiments are carried out based on two test benches. With the proposed Iterative-AMC method, the problems of the parameter redundancy and the structure uncertainty can be addressed. The scale of the original network can be greatly compressed, and the compressed fault diagnosis network is successfully deployed on a small-scale FPGA chip.

Uncertainties and their treatment in the quantitative risk assessment of domino effects: Classification and review

Domino accidents are typical low-frequency and high-consequence events in chemical process industries. Applying quantitative risk assessment (QRA) in domino accident assessment is challenging due to the uncertainties in the escalation process. Meanwhile, the outcomes of QRA are subject to a certain degree of unreliability due to the inappropriate representation of uncertainty. This paper reviews the literature in the field of QRA of domino accidents that may happen in the chemical process industries. Firstly, the sources of uncertainty in risk assessment of domino effects are identified and categorized based on a fundamental structure of uncertainty and a QRA framework. Furthermore, the current methodologies and approaches applied for handling various uncertainties (input uncertainty, model parameter uncertainty, and model structure uncertainty) in the QRA related to domino effects are reviewed. Based on the literature review results, current challenges with respect to uncertainty handling in QRA of domino accidents are discussed, and recommendations for future research are given before the conclusions are presented. This study helps researchers to get insights into the interface between uncertainty fundamentals and the QRA framework and the current status of uncertainty handling in the QRA of domino effects. Furthermore, this study promotes the development of new approaches for handling uncertainty in domino accident analysis.

A system for in-situ, wave-by-wave measurements of the speed and volume of coastal overtopping

Wave overtopping of sea defences poses a hazard to people and infrastructure. Rising sea levels and limited resources mean accurate prediction tools are needed to deliver cost-effective shoreline management plans. A dearth of in-situ data means that the numerical tools used for flood forecasting and coastal scheme design are based largely on data from idealised flume studies, and the resulting overtopping predictions may have orders of magnitude uncertainty for complicated structures and some environmental conditions. Furthermore, such studies usually only provide data on the total volume of overtopping water, and no data on the speed of the water. Here we present WireWall, an array of capacitance-based sensors which measure the speed and volume of overtopping water on a wave-by-wave basis. We describe the successful validation of WireWall against traditional flume methods and present results from the first trial deployments at a sea wall in the UK. WireWall results are also compared with numerical predictions based on EurOtop guidance. WireWall technology offers an approach for reliable acquisition of the data needed to develop resilient coastal protections schemes.