Variance-based Sensitivity Analysis Research Articles

Estimating global identifiability using conditional mutual information in a Bayesian framework

A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model parameter is used to quantify the identifiability with practical considerations. No assumptions are made about the structure of the statistical model or the prior distribution while constructing the estimator. The estimator has the following notable advantages: first, no controlled experiment or data is required to conduct the practical identifiability analysis; second, unlike popular variance-based global sensitivity analysis methods, different forms of uncertainties, such as model-form, parameter, or measurement can be taken into account; third, the identifiability analysis is global, and therefore independent of a realization of the parameters. If an individual parameter has low identifiability, it can belong to an identifiable subset such that parameters within the subset have a functional relationship and thus have a combined effect on the statistical model. The practical identifiability framework is extended to highlight the dependencies between parameter pairs that emerge a posteriori to find identifiable parameter subsets. The applicability of the proposed approach is demonstrated using a linear Gaussian model and a non-linear methane-air reduced kinetics model. It is shown that by examining the information gained for each model parameter along with its dependencies with other parameters, a subset of parameters that can be estimated with high posterior certainty can be found.

Iterative hierarchical clustering algorithm for automated operational modal analysis

Recent developments in sensors and data processing made the structural health monitoring (SHM) sector expanding to big-data field, particularly when continuous long-term strategies are implemented. Nevertheless, main shortcomings are due to the identification and extraction of modal features. In fact, although machine learning methods have been implemented to automate modal identification processes, intense user interaction and time-consuming procedures are still required, limiting the extensive use of these techniques. In order to provide a fully automated procedure capable of identifying and extracting modal properties from covariance driven SSI analyses, an innovative and flexible algorithm for Iterative Hierarchical Clustering Analysis (IHCA) is proposed. To evaluate the stability and robustness of the IHCA method, a Variance-Based Global sensitivity Analysis (VBGA) was performed considering a numerical and experimental case study. The outcomes demonstrated that the IHCA is stable in clustering the physical structural modes and selecting the most representative modal features.

Developing an airport sustainability evaluation index through composite indicator approach

This paper proposes a holistic definition of airport sustainability comprising social, economic, operational, and environmental dimensions. Methodologically, a composite indicator approach is applied to build the Airport Sustainability Evaluation Index (ASEI), which aims to benchmark airports' sustainability performance across the four dimensions. To justify the issue of subjectivity in composite indicator building, two different methods are used in each of the normalization, weighting, and aggregation processes. Consequently, this forms eight composite indicator building schemes. Then, a variance-based sensitivity analysis, average shift in ranking (ASR), and Cronbach's alpha test are performed to examine the sensitivity and reliability among the eight schemes. Schiphol airport is selected as a demonstration to validate the ASEI with its data from 2012 to 2021 as inputs. The results reveal a significant consensus among the eight schemes in identifying the outstanding and bottom performers across the analyzed period. Additionally, weighting is found to be the most influential composite indicator building process. Further, the scheme with the most significant contribution to the result reliability found in this paper is re-scaling as the normalization method, Benefit-of-the-doubt (BoD) as the weighting method, and Non-compensatory multi-criteria approach (NCMC) as the aggregation method.

Effect of uncertainty of material parameters on stress triaxiality and Lode angle in finite elasto-plasticity—A variance-based global sensitivity analysis

This work establishes a computational framework for the quantification of the effect of uncertainty of material model parameters on extremal stress triaxiality and Lode angle values in plastically deformed devices, whereby stress triaxiality and Lode angle are accepted as key indicators for damage initiation in metal forming processes. Attention is paid to components, the material response of which can be represented as elasto-plastic with proportional hardening as a prototype model, whereby the finite element method is used as a simulation approach generally suitable for complex geometries and loading conditions. Uncertainty about material parameters is characterized resorting to probability theory. The effects of material parameter uncertainty on stress triaxiality and Lode angle are quantified by means of a variance-based global sensitivity analysis. Such sensitivity analysis is most useful for apportioning the variance of the stress triaxiality and Lode angle to the uncertainty on material properties. The practical implementation of this sensitivity analysis is carried out resorting to a Gaussian process regression, Bayesian probabilistic integration and active learning in order to decrease the associated numerical costs. An example illustrates the proposed framework, revealing that parameters governing plasticity affect stress triaxiality and Lode angle the most.

On quantifying uncertainty in lightning strike damage of composite laminates: A hybrid stochastic framework of coupled transient thermal-electrical simulations

Lightning strike damage can severely affect the thermo-mechanical performance of composite laminates. It is essential to quantify the effect of lightning strikes considering the inevitable influence of material and geometric uncertainties for ensuring the operational safety of aircraft. This paper presents an efficient support vector machine (SVM)-based surrogate approach coupled with computationally intensive transient thermal-electrical finite element simulations to quantify the uncertainty in lightning strike damage. The uncertainty in epoxy matrix thermal damage and electrical responses of unprotected carbon/epoxy composite laminates is probabilistically quantified considering the stochasticity in temperature-dependent multi-physical material properties and ply orientations. Further, the SVM models are exploited for variance-based global sensitivity analysis to investigate the input parameters' relative influence on the lightning strike-induced damage behavior. Due to the adoption of a coupled SVM-based simulation approach here, it has become possible to carry out a comprehensive uncertainty quantification leading to complete probabilistic descriptions of the electrical and lightning damage parameters despite the requirement of performing a large number of computationally intensive function evaluations. The results reveal that source-uncertainty of the unprotected laminates significantly influences the epoxy matrix decomposition, electrical current density and electric potential, wherein longitudinal electrical conductivity is most sensitive to stochastic variations followed by other electrical, thermal and geometric parameters.

The application of semantic modelling to map pollination service provisioning at large landscape scales

Mapping ecosystem services (ES), including crop pollination by wild insect pollinators, is challenging due to the number of variables involved and the spatial-temporal dimensions of their interactions. To enhance the synergistic relationship between pollination service and crop yield in agricultural landscapes, a better appreciation of the spatial dynamics of pollination service provisioning is needed. Spatially explicit modelling approaches have been used to investigate how different land cover types influence the distribution and abundance of wild bee pollinators in agricultural landscapes. However, an integrated dynamic and spatial modelling framework is needed to address the complexities of pollination supply mapping at the landscape scale. The Artificial Intelligence for Environment and Sustainability (ARIES) framework is a collaborative, spatially explicit and integrated tool for ES assessment. We applied a set of high-resolution process-based pollination models within ARIES to represent landscape capacity to supply pollination by wild bees at the local scale in the Canadian prairies. We also developed a systematic approach to perform a global sensitivity analysis by using a surrogate model (Gaussian Process Regression) and variance-based sensitivity analysis for the selected uncertain key parameters of the model. We modelled pollination dynamics through the mechanistic behavior of native bee guilds, including foraging distance, nesting ability, flight activity, the relative importance of bee guilds, and seasonal variation of floral resources. We focused on three guilds, bumblebees, sweat bees and mining bees, which differed by their nesting habits, floral preferences, and flight distances. We found that over 45% of pollination-dependent croplands in our study area lack wild pollination. The global sensitivity analysis revealed the significance of all key parameters, with seasonal activity across guilds identified as the key driving factors. Our results highlight the significance of the ecological role of wild bees in agricultural landscapes and the sensitivity analysis underscores the importance of temporal dynamics in ecological modeling and pollination.

Bayesian parameter identification in electrochemical model for lithium-ion batteries

Electrochemical models can characterize the internal behavior of cells and are powerful and effective tools for the design and management of batteries. This study proposes a comprehensive framework of Bayesian parameter identification to determine the parameter distributions in the electrochemical model and to estimate the global sensitivity of the parameters for lithium-ion batteries. Bayesian inference in parameter identification can simultaneously determine accurate parameter estimates and parameter identifiability, given specific voltage measurements. Among the several parameters in the pseudo-two-dimensional model, 15 parameters are selected to estimate the posterior distributions. The reconstructed voltage curves through the estimated parameter distributions are consistent with the reference voltages, with relative errors of less than 0.7% at various discharge rates. Changes in the parameter distributions and identifiability were investigated for different discharge rates through the estimated joint and marginal distributions of the parameters. Moreover, based on variance-based global sensitivity analysis, the identifiability of the electrochemical parameters according to the discharge rates is quantitatively analyzed. We demonstrate that the Bayesian parameter identification simultaneously obtains the parameter distributions and identifiability, considering the correlation between various parameters. The proposed framework can help to analyze the behaviors of batteries according to specific operating conditions and materials.

Fusing time-varying mosquito data and continuous mosquito population dynamics models

Climate change is arguably one of the most pressing issues affecting the world today and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under climate change scenarios. Many current models for disease transmission are continuous and autonomous, while mosquito data is discrete and varies both within and between seasons. This study uses an optimization framework to fit a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region, and a variance-based sensitivity analysis highlights the influence each parameter has on the peak magnitude and timing of the mosquito season. This method can easily extend to other geographic regions and be integrated into a larger disease transmission model. This method addresses the ongoing challenges of data and model fusion by serving as a link between discrete time-series data and continuous differential equations for mosquito-borne epidemiology models.

Variance-based sensitivity analysis of oil spill predictions in the Red Sea region

To support accidental spill rapid response efforts, oil spill simulations may generally need to account for uncertainties concerning the nature and properties of the spill, which compound those inherent in model parameterizations. A full detailed account of these sources of uncertainty would however require prohibitive resources needed to sample a large dimensional space. In this work, a variance-based sensitivity analysis is conducted to explore the possibility of restricting a priori the set of uncertain parameters, at least in the context of realistic simulations of oil spills in the Red Sea region spanning a two-week period following the oil release. The evolution of the spill is described using the simulation capabilities of Modelo Hidrodinâmico, driven by high-resolution metocean fields of the Red Sea (RS) was adopted to simulate accidental oil spills in the RS. Eight spill scenarios are considered in the analysis, which are carefully selected to account for the diversity of metocean conditions in the region. Polynomial chaos expansions are employed to propagate parametric uncertainties and efficiently estimate variance-based sensitivities. Attention is focused on integral quantities characterizing the transport, deformation, evaporation and dispersion of the spill. The analysis indicates that variability in these quantities may be suitably captured by restricting the set of uncertain inputs parameters, namely the wind coefficient, interfacial tension, API gravity, and viscosity. Thus, forecast variability and confidence intervals may be reasonably estimated in the corresponding four-dimensional input space.

Potential and Benefit of Green Roof Energy Renovation of Existing Residential Buildings with a Flat Roof in Belgrade

Green roofs are considered to be one of the optimal tools for saving energy and protecting the environment in developed countries. In this paper, an analysis of the possible application of green roofs on existing residential buildings with flat roofs is presented. In the economic analysis, models of existing buildings in Belgrade, with two different types of green roofs, are studied. A key indicator of investment profitability in this investigation is the net present value (NPV) of the green roof project. Besides the private economic impact, other aspects of green roof applications, significant for sustainable development, have been highlighted. The values of the reductions in the annual energy needed for heating and cooling are compared for different scenarios. A maximum energy saving of 22% in the heating season is determined in the building energy simulation program for the model with an intensive green roof. Life cycle profit analysis was based on the probabilistic approach. The corresponding variance-based sensitivity analysis determined the impact of various parameters on the final result. In all models, the first order sensitivity index, which measures the impact of the number of residential units on the NPV, ranges from 12.2% to 63.6%. Sensitivity analysis showed that the benefit of property value increase has the highest influence on the calculated NPV in scenarios that account for this benefit. The obtained results in those scenarios indicate that the most probable NPV at the end of the life cycle is EUR 43/m2 and EUR 82/m2 for extensive and intensive green roofs, respectively.

Bayesian Model Updating for Structural Dynamic Applications Combing Differential Evolution Adaptive Metropolis and Kriging Model

The Bayesian model updating approach has attracted much attention by providing the most probable values (MPVs) of physical parameters and their uncertainties. However, the Bayesian approach has challenges in high-dimensional problems and requires high computational costs in large-scale engineering structures dealing with structural dynamics. This study proposes a new Bayesian updating framework using the Differential Evolution Adaptive Metropolis (DREAM) algorithm to enhance the Bayesian approach’s performance and computational efficiency. In addition, two time-saving strategies are proposed. Firstly, variance-based global sensitivity analysis is used to eliminate insignificant parameters to model responses and reduce model dimensionality. Secondly, a fast-running kriging model is employed as a surrogate of the time-consuming finite-element (FE) model to alleviate the computational burden. DREAM essentially is a multichain sampling method that runs different paths to seek all possible solutions and accurately approximate the posterior probability distribution function in the Bayesian approach. The proposed updating framework was demonstrated using one numerical example and a real-world cable-stayed pedestrian bridge. The results showed that the proposed method enables rationally identifying structural parameters and recovering dynamic responses. Compared with the traditional Bayesian approach without a surrogate model, the computational cost is orders of magnitude lower.

Sparse-grids uncertainty quantification of part-scale additive manufacturing processes

The present paper aims at applying uncertainty quantification methodologies to process simulations of powder bed fusion of metal. In particular, for a part-scale thermomechanical model of an Inconel 625 super-alloy beam, we study the uncertainties of three process parameters, namely the activation temperature, the powder convection coefficient and the gas convection coefficient. First, we perform a variance-based global sensitivity analysis to study how each uncertain parameter contributes to the variability of the beam displacements. The results allow us to conclude that the gas convection coefficient has little impact and can therefore be fixed to a constant value for subsequent studies. Then, we conduct an inverse uncertainty quantification analysis, based on a Bayesian approach on synthetic displacement data, to quantify the uncertainties of the two remaining parameters, namely the activation temperature and the powder convection coefficient. Finally, we use the results of the inverse uncertainty quantification analysis to perform a data-informed forward uncertainty quantification analysis of the residual strains. Crucially, we make use of surrogate models based on sparse grids to keep to a minimum the computational burden of every step of the uncertainty quantification analysis. The proposed uncertainty quantification workflow allows us to substantially ease the typical trial-and-error approach used to calibrate power bed fusion part-scale models, and to greatly reduce uncertainties on the numerical prediction of the residual strains. In particular, we demonstrate the possibility of using displacement measurements to obtain a data-informed probability density function of the residual strains, a quantity much more complex to measure than displacements.

Comparison of national and local building inventories for damage and loss modeling of seismic and tsunami hazards: From parcel-to city-scale

In this paper, we compare the National Structure Inventory, a recently released building inventory for the United States, with a local tax assessor building inventory for use in damage and loss modeling of seismic-tsunami hazards. The city of Seaside, Oregon located in the North American Pacific Northwest and subject to seismic-tsunami hazards from the Cascadia Subduction Zone is used as a testbed. Input attributes – such as spatial footprint, year built, structure type, and number of stories – are compared at the parcel, block, block group, and tract levels. The input attributes show large differences at the parcel level and compare favorably when aggregated at increased spatial scales. The National Structure Inventory consistently underestimates structure value and number of stories compared to the tax assessor data. Expected damages across 7 mean recurrence intervals, from 100-yr to 10,000-yr, are computed using IN-CORE, an open-source community resilience model. Expected damage to the National Structure Inventory tends to be slightly larger than that of the tax assessor data and errors are reduced when aggregated at increased spatial scales. The National Structure Inventory underpredicts total economic losses and risks compared to the tax assessor data, particularly for recurrence intervals associated with high economic risks. A variance-based sensitivity analysis is performed to identify how uncertainties in the input attributes propagate to uncertainties in expected damage. Structure type, design-level, and building location all influence expected damages for seismic-tsunami hazards, highlighting the importance of accurate building inventories in multi-hazard damage and loss modeling.

Uncertainty quantification in free vibration and aeroelastic response of variable angle tow laminated composite plate

The influence of uncertainty in material properties on free vibration and aeroelastic response of variable angle tow (VAT) laminated plates is presented in this work. Free vibration analysis is conducted by a finite element model based on third-order shear deformation theory. The FE model is further coupled with MSC. Nastran to carry out aeroelastic analysis of VAT laminates in the subsonic regime. MSC. Nastran helps to extract the aerodynamic influence coefficient matrix at discrete values of reduced frequencies using Doublet Lattice Method. The accuracy and adaptability of the highly efficient radial basis neural network (RBFN) based surrogate model developed for uncertainty analysis are checked by comparing the result with that of the Monte Carlo simulation. Stochastic analysis of natural frequencies and flutter characteristics of VAT laminates are conducted through various parametric studies. Variance-based sensitivity analysis is used to calculate the contribution of individual material properties to the free vibration and aeroelastic response of VAT laminates. Further, the influence of the highly sensitive properties on the structural failure probability is estimated. This research can be leveraged to predict the probability of failure of the structure at different levels of material uncertainty present in it.

Deterministic and probabilistic-based model updating of aging steel bridges

Numerical modeling is a very useful tool in different fields of bridge engineering, such as load-carrying capacity assessment or structural health monitoring. Developing a reliable computational model that accurately represents the actual bridge mechanical behavior entails advanced FEM-based modeling complemented by a comprehensive experimental campaign that provides the necessary supporting information and allows validating simulation outcomes. This paper proposes a unified approach aimed at the experimental characterization and FE model updating of aging steel bridges. It first involves the realization of an extensive experimental campaign aimed at the bridge's geometrical, material, and dynamic behavior characterization. Then, a model calibration framework is developed, where deterministic (optimization) and probabilistic (Bayesian inference) approaches are employed, and techniques such as global variance-based sensitivity analysis and Kriging-based surrogate modeling are further implemented in order to enhance the identification process and reduce the overall computational burden. The methodology has been validated in a historical riveted steel bridge in O Barqueiro, north of Galicia, Spain. The results show a good agreement in the identified model parameter values and a noticeable correlation between numerical and experimental modal properties, with an average relative error in frequencies of 0.34% and 0.44% for the deterministic and probabilistic approaches and an average MAC (Modal Assurance Criterion) ratio of 0.96.

SOBOL sensitivity analysis and acoustic solid coupling approach to underwater explosion

In this paper, we investigate the influence of explosive parameters on the results of Sobol variance-based sensitivity analysis in the context of underwater explosion simulations. While there is an extensive body of literature related to this field, none of the existing studies has specifically examined the effect of explosive parameters on the results and conducted Sobol variance-based sensitivity analyses. Our findings demonstrate that the “K" and “k" explosive parameters are the most impactful, and both explosive parameters and mesh structures considerably affect the simulation outcomes. These insights can be employed to enhance the accuracy of numerical simulations of explosive-target interactions.We begin by introducing the acoustic-solid coupling (ASC) numerical method and the Sobol variance-based sensitivity analysis technique. We then delve into the explosive parameters and mesh structures utilized in our study. Finally, we present the outcomes of our sensitivity analysis and discuss the implications of our findings in light of the existing literature.The insights derived from our study can contribute to improving the accuracy of numerical simulations of explosive-target interactions. By comprehending the effects of explosive parameters and mesh structures, we can refine our simulations and reduce the uncertainty associated with the results, ultimately advancing the field of underwater explosion analysis and filling the identified gap in the literature.

OC-0624 variance-based sensitivity analysis (SA) for the evaluation of margin concepts in radiotherapy

OC-0624 variance-based sensitivity analysis (SA) for the evaluation of margin concepts in radiotherapy

Reliability, economic, and environmental analysis of fuel-cell-based hybrid renewable energy networks for residential communities

A fuel cell system has received considerable interest as an effective combined heat and power (CHP) generation system. In particular, a polymer electrolyte membrane fuel cell (PEMFC) has been regarded as suitable for residential buildings as their low-temperature heat generation can be used to meet high heat demand. This study aims to optimize renewable energy networks on the basis of PEMFC, considering both different system mixes in an energy network and different system sizes. This study scrutinizes the overall performance of different hybrid energy networks based on PEMFC in terms of the reliability, economic, and environmental perspectives when both the heat and power generation from PEMFC are used to meet the energy need of residential communities. A hybrid renewable energy network for a residential community with 12 households is modeled and simulated using TRNSYS to evaluate the performance of different hybrid energy network scenarios. Four scenarios are created on the basis of the base case with a gas-based fuel cell (FC) system and heat tank by incrementally adding renewable energy systems—a ground source heat pump (GSHP) system, photovoltaic (PV) system, battery, electrolyzer, and hydrogen storage tank. First, the impacts of individual design variables are quantified using variance-based sensitivity analysis for identification of key design variables. Second, the cost optimization of hybrid energy network is performed under the four scenarios of system mixes. Last, the effects of the key design variables for each scenario are further scrutinized through heat maps. The analysis results of the case study confirms the feasibility of a FC system as part of renewable energy network for residential buildings. Furthermore, Scenario 3 comprising a FC system, GSHP, PV system, and battery is found to be the optimal design in terms of all the three aspects. This result suggests that the whole performance of hybrid energy network can be improved when other renewable systems are included in the network to complement the FC system. In contrast, Scenario 4 using green hydrogen has poorer performance than Scenario 3 using gas as fuel due to high unit prices of hydrogen systems and low efficiency of green hydrogen production. Furthermore, external factors such as utility prices and system unit prices are considered in further analysis and found to have great influence on the optimal design of energy networks.

The prognostic value of gastroesophageal reflux disorder in interstitial lung disease related hospitalizations

BackgroundGastroesophageal reflux disease (GERD) is a common comorbidity in patients with interstitial lung disease (ILD). We built and validated a model using the national inpatient sample (NIS) database to assess the contributory role of GERD in ILD-related hospitalizations mortality.MethodsIn this retrospective analysis, we extracted ILD-related hospitalizations data between 2007 and 2019 from the NIS database. Univariable logistic regression was used for predictor selection. Data were split into the training and validation cohorts (0.6 and 0.4, respectively). We used decision tree analysis (classification and regression tree, CART) to create a predictive model to explore the role of GERD in ILD-related hospitalizations mortality. Different metrics were used to evaluate our model. A bootstrap-based technique was implemented to balance our training data outcome to improve our model metrics in the validation cohort. We conducted a variance-based sensitivity analysis to evaluate GERD's importance in our model.FindingsThe model had a sensitivity of 73.43%, specificity of 66.15%, precision of 0.27, negative predictive value (NPV) of 93.62%, accuracy of 67.2%, Matthews Correlation Coefficient (MCC) of 0.3, F1 score of 0.4, and area under the curve (AUC) for the receiver operating characteristic (ROC) curve of 0.76. GERD did not predict survival in our cohort. GERD contribution to the model was ranked the eleventh among twenty-nine variables included in this analysis (importance of 0.003, normalized importance of 5%). GERD was the best predictor in ILD-related hospitalizations who didn’t receive mechanical ventilation.InterpretationsGERD is associated with mild ILD-related hospitalization. Our model-performance measures suggest overall an acceptable discrimination. Our model showed that GERD does not have a prognostic value in ILD-related hospitalization, indicating that GERD per se might not have any impact on mortality in hospitalized ILD patients.

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.