Structural Uncertainty Research Articles

Unravelling the Association Between Uncertainties in Model-based Economic Analysis and Funding Recommendations of Medicines in Australia.

Health technology assessment is used extensively by the Pharmaceutical Benefits Advisory Committee (PBAC) to inform medicine funding recommendations in Australia. The PBAC often does not recommend medicines due to uncertainties in economic modelling that result in delaying access to medicines for patients. The systematic identification of which uncertainties can be reduced with alternative evidence or the collection of additional data can help inform recommendations. This study aims to characterise different types of uncertainty in economic models and empirically assess their association with the PBAC recommendations. A framework was developed to characterise four types of uncertainties: methodological, structural, generalisability and parameter uncertainty. The first two types were further subcategorised into parameterisable and unparameterisable uncertainty. Data on uncertainty and other factors were extracted from PBAC's Public Summary Documents of first submissions for 193 medicine (vaccine)-indication pairs including economic modelling between 2014 and 2021. Logistic regression was used to estimate the average marginal effect of each type of uncertainty on the probability of a positive recommendation. The PBAC more often raised issues regarding parameter uncertainty (95%) and parameterisable structural uncertainty (83%) than generalisability uncertainty (48%) and unparameterisable methodological uncertainty (56%). The logistic regression results suggested that the PBAC was more likely to recommend a medicine without unparameterisable methodological, generalisability, and parameterisable structural uncertainty by 15.0%, 10.2 %, and 17.6%, respectively. Parameterisable methodological, unparameterisable structural and parameter uncertainty were not significantly associated with the PBAC recommendations. This study identified the uncertainties that had significant associations with PBAC recommendations based on the first submission. This may help improve model quality and reduce resubmissions in the future, thus improving patients' access to medicines.

Identifying Quantum Interference Effects from Joint Conductance-Thermopower Statistics.

Although quantum effects are thought to dominate the heat and charge transport through molecular junctions, large uncertainties in chemical structure, lead-molecule coupling strengths, and energy levels make it difficult to definitively identify these effects from the measured thermopower S and conductance G distributions alone. Here, we develop a simple statistical method to identify destructive quantum interference features (nodes) through the anticorrelation between simultaneously measured G and S values. We find these correlations can be used to unambiguously identify far-detuned nodes, even when G and S distributions alone cannot. As an example, we consider several para- and meta-configured systems, including benzenediamine and diiodo-terphenyl-based junctions, finding that nodes can be identified in ensembles with broad level-alignment and lead-molecule coupling distributions, and with significant anodal transport contributions, including from vacuum tunneling. The efficacy and limitations of this method are analyzed.

Quantification and validation of uncertainties in subsoil models

AbstractIn infrastructure planning and construction, modeling the subsoil and its associated uncertainty is a fundamental task of geotechnical engineers. However, probabilistic methods and tools for quantifying and displaying the uncertainty of the subsoil models are rarely used in practice where deterministic interpolation dominates. In digital planning using Building Information Modeling (BIM), the probabilistic approach supports creating a discipline model in which the uncertainties of the spatial layer structure are statistically quantified to evaluate the georisks in the design and execution of civil constructions. This article presents a case study using a combination of Sequential Gaussian Simulation (SGSIM) and Sequential Indicator Simulation (SISIM) to account for uncertainties in soil layer geometry. In a case study at the Munich Town Hall, a geostatistical approach is applied and validated based on 70 bore logs, whereby the probabilities for the occurrence of a particular layer are spatially quantified. The case study illustrates the methodology‘s great potential and benefits compared to the conventional deterministic approach based on interpolation procedures.

Exploring the interaction of design variability and stochastic operational uncertainties in software-intensive systems through the lens of modeling

AbstractIn software-intensive systems, navigating the complexities that emerge from the interaction of design variability and stochastic operational uncertainties presents a daunting challenge. This paper delves into the dynamics between these two dimensions of uncertainty, offering novel insights about how modeling can contribute to the analysis of their combined impact upon system properties. By elevating the abstraction level at which probabilistic models are conceptualized, our approach enables an integrated analysis framework that considers both structural and quantitative dimensions of design spaces. Through the introduction of novel language constructs, our methodology facilitates the direct referencing of structural relationships within probabilistic behavioral specifications. Furthermore, the adoption of novel quantifiers in probabilistic temporal logic enables evaluating complex properties across diverse design variants, thereby streamlining the assessment of guarantees within the solution space. We demonstrate the feasibility of this approach on four case studies, showcasing its potential to offer comprehensive insights into the trade-offs and decision-making processes inherent in managing different types of structural design variability and operational uncertainties in software-intensive systems.

Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy.

Making timely management decisions is often hindered by uncertainty. Monitoring reduces two key types of uncertainty. First, it serves to reduce structural uncertainty of how the system works and provides support for expectations of how a system works. Second, it serves to reduce parametric uncertainty of the drivers of system dynamics. By combining monitoring data and quantitative models, we can reduce structural and parametric uncertainty. To demonstrate this, we focus on the Shenandoah salamander (Plethodon shenandoah), a United States Federally Endangered Species. Early work suggested that P. shenandoah extinction risk results from competition with a conspecific (Plethodon cinereus). However, more recent work has found equivocal support for this claim, instead suggesting that abiotic factors, such as moisture and temperature, drive P. shenandoah persistence. Using long-term monitoring data, we find that while competition may play a part in P. shenandoah extinction risk, measures of surface moisture are better predictors of occupancy dynamics. Further, we find decreased detection rates of P. shenandoah when P. cinereus is present, suggesting a conflation of detection probability with actual competition, which cautions against making inference from unadjusted observations of occurrence. Using multiple lines of inquiry allows for more robust understanding of system drivers in the face of high uncertainty, increasing opportunities to manage extinction risk.

Exciton Dissociation and Long‐Lived Delayed Components in High‐Efficiency Quasi‐Two‐Dimensional Green Perovskite Light‐Emitting Diodes

AbstractQuasi‐two‐dimensional (quasi‐2D) perovskites, consisting of multi‐quantum wells (MQWs) separated by organic intercalating cations, exhibit high luminescence efficiency while the photophysical processes involved remain partially obscure due to the uncertainty of the MQWs structure. Herein, a synergetic dual‐additive strategy is adopted to prepare quasi‐2D perovskite films, where 18‐crown‐6 and tris(4‐fluorophenyl)phosphine oxide are utilized to suppress the formation of low‐dimensional perovskites, diminish defect density, and enhance photoluminescence quantum yield to 93.7%. Notably, a long‐lived delayed component, spanning hundreds of microseconds, is identified for the first time in perovskite emitters, which correlates with exciton dissociation and recombination, and the differences in temperature‐dependent radiative lifetime of the delayed components match with the luminescence properties. Finally, benefiting from lower trap density and more efficient energy‐funneling, green PeLEDs treated with dual additives have achieved a high external quantum efficiency of 28.9% and a commendable operating half‐lifetime of 17.8 h at an initial brightness of 500 cd m−2. The observation of the long‐lived delayed components, coupled with insights into exciton dissociation, provides a profound and comprehensive understanding of the fundamental carrier behavior in perovskite emitters and establishes a direct correlation between the properties of perovskite emitters and their photophysical processes.

Advantages of base isolation in reducing the reliability sensitivity to structural uncertainties of buildings

Base isolation is among the most effective techniques for safeguarding buildings against earthquakes. Similar to non-isolated buildings, the seismic performance of a base-isolated building can vary not only due to the inherent record-to-record variability in earthquake ground motions but also the non-negligible influence of uncertainties associated with structural properties. Introducing an isolation layer of isolation bearings and energy dissipators to the building further adds to the source of uncertainties, raising the question regarding the reliability of base isolated buildings. This study proposed a methodology that employs the probability density evolution method (PDEM) with local time grid refinement to evaluate the reliability of a base-isolated structure and its non-isolated counterpart under earthquakes of various magnitudes and fault distances. It allows for simultaneously considering the uncertainties of various sources in the reliability analyses with affordable computational cost, including those of the ground motions, of the structural nonlinearity, and of the ultimate states that define the reliability. The results show that the base-isolated building exhibits much higher reliability than its non-isolated counterpart in all cases even if the ultimate behavior such as the isolator fracture or the impact to the retaining wall is considered. The added source of uncertainties in the isolation layer does not exhibit a notable impact on the reliability. In contrast, the seismic reliability of base-isolated buildings is much less sensitive to structural uncertainties than that of their non-isolated counterparts.

MvWECM: Multi-view Weighted Evidential [formula omitted]-Means clustering

Traditional multi-view clustering algorithms, designed to produce hard or fuzzy partitions, often neglect the inherent ambiguity and uncertainty in the cluster assignment of objects. This oversight may lead to performance degradation. To address these issues, this paper introduces a novel multi-view clustering method, termed MvWECM, capable of generating credal partitions within the framework of belief functions. The objective function of MvWECM is introduced considering the uncertainty in the cluster structure included in the multi-view dataset. We take into account inter-view conflict to effectively leverage coherent information across different views. Moreover, the effectiveness is heightened through the incorporation of adaptive view weights, which are customized to modulate their smoothness in accordance with their entropy. The optimization method to get the optimal credal membership and class prototypes is derived. The view wights can be also provided as a by-product. Experimental results on several real-word datasets demonstrate the effectiveness and superiority of MvWECM by comparing with some state-of-the-art methods.

Off-policy reinforcement-learning-based fault-tolerant [formula omitted] control for topside separation systems with time-varying uncertainties

The topside separation system plays a pivotal role in the treatment of produced water within offshore oil and gas production operations. Due to high-humidity and salt-infested marine environments, topside separation systems are susceptible to dynamic model variations and valve faults. In this work, fault-tolerant control (FTC) of topside separation systems subject to structural uncertainties and slugging disturbances is studied. The system is configured as a cascade structure, comprising a water level control subsystem and a pressure-drop-ratio (PDR) control subsystem. A fault-tolerant H∞ control framework is developed to cope with actuator faults and slugging disturbances. To enhance control performance in the presence of actuator faults and model uncertainties while reducing sensitivity to slugging disturbances, the fault-tolerant H∞ control problem for the topside separation system is established as the two-player differential game problem. In addition, a Nash equilibrium solution for the fault-tolerant H∞ control problem is achieved through the solution of the game algebraic Riccati equation (GARE). A model-free approach is presented to implement the proposed fault-tolerant H∞ control method using off-policy reinforcement learning (RL). Simulation studies demonstrate the effectiveness of the solution.

Evaluation of Managerial Economics Applications Strategy using the SPSS Method

Managerial Economics is an economy branch is, this economic theory and size using methods solve real-world managerial problems. It helps businesses and organizations make informed decisions by using economic analysis. Here are some common applications of managerial economics: Demand Analysis and Forecasting: Businesses use managerial economics to understand consumer behavior and forecast future demand for their products or services. This involves studying factors that affect demand, such as price, income levels, and consumer preferences. Production and Cost Analysis: Managerial economics helps businesses optimize production processes and minimize costs. It involves analyzing production functions, cost structures, and economies of scale to determine the most efficient way to produce goods or services. Pricing Strategies: A product or the right price for the service It is up to a company to decide Crucial to profitability. Managerial economics helps businesses set prices by considering factors like production costs, competition, and consumer willingness to pay. Market Structure Analysis: Understanding the type of market, a company operates in (e.g., perfect competition, monopoly, oligopoly) is essential for making strategic decisions. Managerial economics helps analyses market structures and determine appropriate strategies for each. Risk and Uncertainty Analysis: Businesses often face uncertainty in various aspects, such as demand fluctuations, production costs, and market conditions. Managerial economics provides tools to assess and manage risks, including techniques like decision tree analysis and expected value calculations. Capital Budgeting and Investment Analysis: Managerial economics assists in evaluating investment opportunities and capital projects. It involves methods Payback period analysis of investments. Regulatory and Government Policy Analysis: Managerial economics helps businesses understand and navigate government regulations and policies that affect their operations. This can include areas like taxation, environmental regulations, and trade policies. Strategic Planning: Using economic analysis, businesses can develop long-term strategies for growth and sustainability. This may involve assessing market trends, competitive dynamics, and potential expansion opportunities. Game Theory and Competitive Strategy: Managerial economics incorporates game theory to analyze strategic interactions between competitors. It helps businesses formulate optimal strategies in competitive environments. Resource Allocation: Efficient allocation of resources, including labor, capital, and technology, is crucial for a company's success. Managerial economics provides tools to distribute funds in a manner that optimisesoverall productivity and profitability. Demand Analysis and Forecasting, Production and Cost Analysis, Pricing Strategies, Market Structure Analysis, Risk and Uncertainty Analysis, Capital Budgeting and Investment Analysis and Regulatory and Government Policy Analysis. The Cranach’s Alpha Reliability result. The overall Cranach’s Alpha value for the model is .744 which indicates 74% reliability. From the literature review, the above 50% Cranach’s Alpha value model can be considered for analysis.

Field ionization intensity used to measure local pressure in gas flows

A coaxial ion source produces an ion beam via field effect in a gas flow through a coaxial microchannel structure. Measuring the intensity of ion emission under an electric voltage condition reveals the pressure at the tip of the coaxial structure, where ionization occurs. The spatial resolution of the measurements is defined by the volume into which the position of the tip fits, here estimated as a cube with an edge of 10 μm. The pressure at the tip is also obtained analytically as a function of the throughput through the coaxial structure. The theoretical and experimental pressure values reported in the present work are in agreement between them within the geometric uncertainties of the coaxial structure itself.

The influence of major faults and fractures on the development of non-matrix porosity system in a pre-salt carbonate reservoir, Santos Basin – Brazil

Faults and fractures are central for characterizing the permeability distribution in carbonate reservoirs since they act as pathways for diagenetic fluids that often favor intense rock dissolution and permeability. Usually, high permeability zones and fractures are not easily recognized in seismic data due to limited resolution and they are often associated with higher concentrations of hydrocarbons or even significant fluid losses during drilling, thus creating a challenge for hydrocarbon exploration. In the Santos Basin, southeast Brazil, the pre-salt carbonate reservoirs from the Barra Velha Formation (BVE) are the main hydrocarbon producers in Brazilian Atlantic margin and well-known for being extremely heterogeneous, exhibiting complex dual-porosity systems. In this study, we built a conceptual model of these fracture zones and non-matrix porosity formation that helped narrowing the understanding of these complex systems. The characterization of faults and fractures was carried out using seismic, well-logs, and borehole image data to understand the influence of these structures in the porosity formation along the Barra Velha Formation. In the study area, three fault sets were defined (F1, F2, and F3) from seismic data. F1 represents to the larger faults, while the F3 fault set represents the smaller faults related to the reactivation of F1; both sets being oriented NE-SW. The F2 fault set is associated with the rift formation and is oriented to NNE-SSW. These three fault sets compartmentalized the studied area into different domains, each exhibiting distinct fracture sets. The natural open fractures were formed during the reactivation of rift faults and are oriented mainly NW, NNE-NNW, NE, and ENE and were identified across the entire study area, but with different intensity values. The fracture intensity closely relates to the distance from major faults where the wells with the highest fracture intensity occurs located 150–590 m from the larger F1 fault set. Scan-lines were conducted throughout the area to determine the fault width, and an average value of 1.2 km was established. Seven non-matrix porosity classes were characterized and classified into stratigraphically concordant and discordant non-matrix pore types at well scale through borehole image interpretation. The Barra Velha Formation exhibit higher occurrence of stratigraphically discordant non-matrix porosity related to fractured zones while stratigraphically concordant non-matrix porosity is mainly controlled by the paleotopography of the study area. Overall, non-matrix porosity formation tends to follow an orientation that suggests a preferential dissolution flow towards NE and ENE directions. Intervals with higher silica content shows a positive correlation with both fracture intensity and stratigraphically discordant non-matrix porosities. This work provides a conceptual model about the fractures and non-matrix porosity distribution in pre-salt carbonate rocks that can help address some of associated structural and stratigraphic uncertainties during field appraisal and development.

Structural Uncertainty in the Sensitivity of Urban Temperatures to Anthropogenic Heat Flux

AbstractOne key source of uncertainty for weather and climate models is structural uncertainty arising from the fact that these models must simplify or approximate complex physical, chemical, and biological processes that occur in the real world. However, structural uncertainty is rarely examined in the context of simulated effects of anthropogenic heat flux in cities. Using the Weather Research and Forecasting (WRF) model coupled with a single‐layer urban canopy model, it is found that the sensitivity of urban canopy air temperature to anthropogenic heat flux can differ by an order of magnitude depending on how anthropogenic heat flux is released to the urban environment. Moreover, varying model structures through changing the treatment of roof‐air interaction and the parameterization of convective heat transfer between the canopy air and the atmosphere can affect the sensitivity of urban canopy air temperature by a factor of 4. Urban surface temperature and 2‐m air temperature are less sensitive to the methods of anthropogenic heat flux release and the examined model structural variants than urban canopy air temperature, but their sensitivities to anthropogenic heat flux can still vary by as much as a factor of 4 for surface temperature and 2 for 2‐m air temperature. Our study recommends using temperature sensitivity instead of temperature response to understand how various physical processes (and their representations in numerical models) modulate the simulated effects of anthropogenic heat flux.

Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy.

Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST). A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario. The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16). Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Experimental validation of an efficient strategy for FE model updating and damage identification in tubular structures

ABSTRACT Early identification of damages in tubular structures is crucial for their long-term safety and functionality, as they are essential in various modern life applications. Experimental and numerical modal data may slightly differ due to unknown structural characteristics and uncertainties, which are typically addressed using finite element (FE) model updating procedures. Instead of using the Euler-Bernoulli beam element, this paper utilises the semi-rigidly connected frame element (S-RCFE). By incorporating extra design parameters, such as the end fixity factor of all connections, the S-RCFE offers a unique opportunity to establish a strong agreement between experimental and numerical models through an optimisation-based FE model updating procedure. A well-calibrated FE model represents the actual behaviour of the structure and leads to achieving accurate results in the damage detection step. This paper employs the improved grey wolf optimiser (IGWO) and weIghted meaN oF vectOrs (INFO) to minimise 11 objective functions with adjustable coefficients. The statistical investigations reveal that the IGWO effectively minimised five out of six objective functions, which were defined based on the modified total modal assurance criterion (MTMAC). The rest of the objective functions based on the modal assurance criterion (MAC), natural frequency vector assurance criterion (NFVAC), differences in natural frequencies, and a combination of the MAC and NFVAC could not obtain accurate outcomes for the model updating problem. The statistical comparison indicates that the INFO algorithm is unreliable for the FE model updating despite achieving at least one successful result in ten independent runs. The INFO algorithm and the IGWO algorithm demonstrate comparable performance in damage detection. The analysis also shows that the coefficients of MTMAC, alpha and beta, should be adjusted to 0.65 and 1, respectively, to achieve the most accurate damage detection result.

Antarctic sensitivity to oceanic melting parameterizations

Abstract. The Antarctic Ice Sheet (AIS) has experienced accelerated loss of ice over the last decades and could become the main contributor to sea level rise in the coming centuries. However, the associated uncertainty is very large. The main sources of this uncertainty lie in the future scenarios, the climatic forcing and, most notably, the structural uncertainty due to our lack of understanding of ice–ocean interaction processes, in particular, the representation of subshelf basal melt. In this study, we use a higher-order ice sheet model to investigate the impact of these three sources of uncertainty on the contribution of the AIS to sea level in the coming centuries in the context of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) but extending the projections to 2500. We test the sensitivity of the model to basal melting parameters using several forcings and scenarios simulated in the CMIP5 and CMIP6 ensembles. Results show a strong dependency on the values of the parameter that controls the heat exchange velocity between ice and ocean as well as the forcing and scenario. Higher values of the heat exchange parameter lead to higher sea level rise, with the contribution depending on the forcing–scenario configuration and reaching in some cases more than 3 m in sea level equivalent by the end of 2500. Idealized simulations considering the individual effects of the atmospheric and the oceanic forcing have been performed, demonstrating that the oceanic forcing plays a dominant role over the western sector of the AIS, while the atmospheric forcing is more important for the eastern sector and the interior.

A spectral element approach for response spectrum estimation of frame structures with uncertain parameters subjected to stationary stochastic excitations

Determination of the response spectra of structures under excitation is crucial for vibration control, reliability analysis, fatigue life prediction, and optimization design of structures. However, available methods can be time-consuming and even erroneous when dealing with structural uncertainty, making them difficult to apply to actual complex structures. This paper proposes a spectral element approach for response spectrum estimation of structures with uncertain parameters subjected to stationary stochastic excitations. First, the spectral element method and Karhunen-Loève expansion technology are utilized to represent the response spectra of structures with uncertain parameters to reduce computation cost. Then, a dimension-reduction technology of random variables is utilized to simplify the numerical computations while retaining accuracy. Based on the above, the method for the envelope response spectrum with practical applicability is proposed in this study. The method can accurately and efficiently estimate the response spectrum of structures with uncertain parameters. The proposed approach is validated by comparison with the Monte Carlo method using three numerical examples.

Fisheries shocks provide an opportunity to reveal multiple recruitment sources of sardine in the Sea of Japan

The abrupt decline in sardine catches in the Sea of Japan and the East China Sea (SJ-ECS) in 2014 and 2019 and the recovery in the following years call into question the current assumption that sardines in the SJ-ECS form a self-recruiting subpopulation. To test this hypothesis, we analysed otolith stable oxygen and carbon isotope profiles (δ18O, δ13C) of age-0 and age-1 sardines from 2010 and 2013–2015 year-classes captured in the SJ-ECS, as geographic markers for nursery areas. Age-0 sardines generally showed a significant ontogenetic decrease in otolith δ18O from larval to juvenile stages. However, the majority of age-1 captured in spring 2011, 2015 and 2016 showed non-decreasing otolith δ18O profiles, suggesting that the age-0 off the Japanese coast were not the main source of recruitment. Different migration groups were thus indicated: the “locals” growing up off the Japanese coast and the migrating “nonlocals”. The isotope profiles of the “nonlocals” overlapped with those of age-0 captured in the subarctic North Pacific, suggesting that they may be migrants from the Pacific, or perhaps an unobserved northward migration group in the SJ-ECS. Our results highlight the considerable uncertainty in the population structure assumed in current stock assessment models for Japanese sardine.

Verification, validation, and uncertainty quantification (VVUQ) in structural analysis of concrete dams

Over the past three decades, advancements in computational power and numerical methods have significantly enhanced the role of structural analyses in the design and safety assessment of dams. Simulating concrete dam behavior, particularly in interactions with reservoir water and rock foundations, poses formidable computational challenges. Additionally, the need to define uncertainties related to material parameters, loading conditions, and modeling strategy adds complexity to the modeling process, therefore, quantifying sources of uncertainty is crucial for maintaining credibility and confidence in analysis results. This paper provides a synthesis and an overview of existing research and presents a generic framework for evaluating the credibility of advanced structural analysis methods for concrete dams, with a focus on their limitations and associated uncertainties. The methodology includes a comprehensive process for structural analysis, verification, validation, and uncertainty quantification, aiming to facilitate condition assessments of concrete dams.

Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging

This study proposes a two-step probabilistic post-processing approach that combines different machine learning-based postprocessors through the Copula-Embedded Bayesian Model Averaging (COP-BMA) method to improve the performance of a hydrological model for streamflow predictions. The proposed approach serves a two-fold purpose: firstly, it aims to enhance the accuracy of streamflow predictions, and secondly, it provides probabilistic results that implicitly address the structural uncertainty inherent in different postprocessing methods. We validate our approach by applying it to the Conceptual Functional Equivalent, a lumped hydrologic model utilized for simulating extreme floods during Hurricane Harvey. The validation is conducted across twelve distinct watersheds in the Southeast Texas region at both daily and monthly scales. The findings indicate that the proposed framework significantly enhances the performance of the hydrologic model across the studied watershed. Specifically, on a daily time scale, there is a 23% and 53% improvement in the NSE and KGE respectively, while on a monthly time scale, the framework enhances NSE by 21% and KGE by 25%. Additionally, the MAE (cms) was notably reduced from 4.64 to 2.23 on the daily scale, and from 2.8 to 1.65 on the monthly scale.