Influence Of Uncertain Parameters Research Articles

Uncertainty quantification and sensitivity analysis of neuron models with ion concentration dynamics.

This paper provides a comprehensive and computationally efficient case study for uncertainty quantification (UQ) and global sensitivity analysis (GSA) in a neuron model incorporating ion concentration dynamics. We address how challenges with UQ and GSA in this context can be approached and solved, including challenges related to computational cost, parameters affecting the system's resting state, and the presence of both fast and slow dynamics. Specifically, we analyze the electrodiffusive neuron-extracellular-glia (edNEG) model, which captures electrical potentials, ion concentrations (Na+, K+, Ca2+, and Cl-), and volume changes across six compartments. Our methodology includes a UQ procedure assessing the model's reliability and susceptibility to input uncertainty and a variance-based GSA identifying the most influential input parameters. To mitigate computational costs, we employ surrogate modeling techniques, optimized using efficient numerical integration methods. We propose a strategy for isolating parameters affecting the resting state and analyze the edNEG model dynamics under both physiological and pathological conditions. The influence of uncertain parameters on model outputs, particularly during spiking dynamics, is systematically explored. Rapid dynamics of membrane potentials necessitate a focus on informative spiking features, while slower variations in ion concentrations allow a meaningful study at each time point. Our study offers valuable guidelines for future UQ and GSA investigations on neuron models with ion concentration dynamics, contributing to the broader application of such models in computational neuroscience.

Dynamic Analysis of the Bolted Joint Structure with Interval Uncertainties

This paper investigates the dynamic influence of joint interfaces in bolted joint structures subjected to uncertain-but-bounded parameters. Firstly, applying the thin layer elements (TLE) represents the contact interfaces with nonuniform stiffness distribution. The stochastic model updating method is employed to identify the uncertain parameters of TLE. Secondly, the Legendre interval method (LIM) is applied to calculate the natural frequencies of bolted lap joints with uncertain parameters. By introducing the elastic modulus of TLE as interval vectors, relations between the natural frequencies and uncertain parameters could be analyzed using the LIM. Furthermore, the dependence of the dynamic response on TLE parameters can be established based on the modal superposition principle. Considering the influence of uncertain parameters, an uncertain acceleration frequency response surrogate function (UAFRSF) is proposed to predict the dynamic behavior and qualitatively analyze the parameter sensitivity of the bolted lap joints. The elastic modulus of TLE in different contact regions is selected as the interval variable. The acceleration frequency responses of the bolted lap joints under uncertain parameters are investigated. Compared with MCs simulations with 200 samples and Chebyshev interval method results, the effectiveness of the proposed method for solving the uncertain acceleration frequency response of bolted lap joints is verified. This research will provide theoretical support for improving the design of aero-engine joint structures.

Performance analysis of electrical signal output of multi-state flexoelectric structures with parameter uncertainties through quasi-Monte Carlo method

Flexoelectric effect is a more universal electromechanical coupling effect than piezoelectric effect. Flexoelectric beams as the main structural component of flexoelectric power signal output have broad application prospects in the next generation of micro–nano electromechanical systems. However, the electrical signal output of flexoelectric structures in macro-scale is far less than the output of the piezoelectric signal. Therefore, it is urgent to explore the influence of the parameter uncertainties on the electrical signal output of the flexoelectric structures, in order to improve the electrical signal output of flexoelectric materials with excellent design performance. Based on the quasi-static theory, the output voltage model and the output charge model of flexoelectric structures as well as the effective piezoelectric coefficient model are constructed. Then the influences of the flexoelectric parameters on the output voltage and the output charge are researched as well as the influence of the effective piezoelectric coefficient. Finally, the influences of uncertain parameters under different electrical states (e.g. the electrical open circuit and short circuit states) on the output performance of flexoelectric signal are studied by the quasi-Monte Carlo method, in order to further provide a reference for the reliability analysis and optimization design of the flexoelectric structures.

Uncertainty-oriented optimal impedance control for EPS-human system with reliability evaluation

This study proposes an interval optimal impedance control method for the electric power steering- human (EPS-H) system with reliability constraints. Considering the relationship between the lateral control using electric power steering of the vehicle and the human, a position-based impedance control model for the integrated EPS-H system is first developed. Polynomial chaos expansion in the surrogate model is employed to establish the interval state-space equation to obtain the bounded responses of the impedance control system using the interval dimensional analysis method. Moreover, the influence of uncertain parameters on the overall control system is obtained by deriving the interval optimal control law consisting of the interval linear quadratic tracking (LQT) inner loop and the interval impedance control outer loop through the interval uncertainty analysis. Furthermore, a non-probabilistic time-varying reliability method is proposed to evaluate the safety of the whole impedance system using the partial derivatives with respect to the uncertain parameters derived from the interval state-space equation. Regarding the nominal cost function and steady-state error of nominal responses as two optimization objectives, the multi-objective reliability-based design optimization is built and solved. Finally, a numerical example of the impedance control model for the EPS-H system is applied to verify the effectiveness of the proposed method.

An Interval-based privacy – Aware optimization framework for electricity price setting in isolated microgrid clusters

With the advance of communication infrastructures and the necessity of increasing the efficiency of energy systems, electricity networks are evolved towards more decentralized architectures and operational schemes. In this context, microgrid clusters are emerging as a valuable framework for optimal integrating renewable sources, demand response initiatives, and storage systems. When the cluster is competitive, the different agents partaking in the cluster compete for trading energy with others, for which an upstream agent (coordinator) sets nodal prices in a similar way to conventional energy markets. This paper is focused on this aspect by developing a novel price setting mechanism for islanded microgrid clusters based on an original equilibrium problem with an equilibrium constraints structure. The new proposal concerns about privacy issues, for which an original diagonalization algorithm is proposed by which only boundary information is transferred from microgrids to the coordinator. Moreover, uncertainties from renewable generation and demand are accommodated using interval notation and equivalent scenarios. The overall problem is raised as a bi-level model, which is further linearized and transformed into a single-level framework tractable by off-the-shelf solvers. A 4-microgrid cluster integrated into a 9-bus network serves as an illustrative case study. The impact of uncertainties is profusely studied, showing that total energy exchanged among microgrids can decrease by 61 % when the influence of uncertain parameters is notable. Other relevant aspects are discussed, and the practicability of the new proposal is demonstrated by analysing its computational performance. Moreover, the developed tool is further validated on a medium-scale network involving a large number of microgrids.

A Proof-of-Concept of a Reliability-Based Life-Cycle Cost Optimization Framework for Activated Sludge Processes

Wastewater treatment plants need their reliability to be properly assessed to preserve society’s health and availability of water resources. To date, no wastewater treatment plant framework comprehends the reliability of the system, considering the influence of uncertain parameters, and the total costs during the lifespan of the project. Here, an innovative framework combining a reliability analysis, a reliability-sensitivity analysis, and a life-cycle cost assessment is presented as a proof-of-concept tool for reliability-based life-cycle cost optimization of an activated sludge process due to its common application in sanitation worldwide. The results indicate (1) great variability of the reliability scenario due to the influent loads, (2) the sludge volumetric index exerts the most influence on the system’s reliability and risk expenditures, and (3) an optimum scenario associated with the lowest life-cycle cost and an annual failure rate of 19 day of failure year−1 can be obtained via an exhaustive simulation protocol. The framework demonstrates to be a promising tool for techno-economic analysis of activated sludge processes, as well as to optimize project expenditures based on its reliability to withstand different influent loads. This proof-of-concept optimization framework is expected to find multiple applications not only in sanitation and water treatment but also in other industries while supporting sustainable development goals.

Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation

Under the influence of uncertain parameters and external disturbance, the accuracy is low and the response is slow of attitude and trajectory tracking in the flight control of the quadrotor unmanned air vehicles (UAV). In order to solve the problems, Because of the excellent adaptability and noise suppression ability of the extended Kalman filter for nonlinear system problems , it is used to suppress the high-frequency signal disturbance to reduce the estimation burden of the extended state observer, and jointly with the expanded state observer to estimate the total disturbance formed by the system uncertainty parameters and external disturbances to reduce system reliance on accurate models, and the differential values of perturbation estimated values is used by feedforward compensation to improve the tracking accuracy under abrupt disturbances and to overcome the phase lag caused by abrupt disturbances. Extended Kalman filtering, extended state observer with the feedforward compensation and PD controller with error compensation are comprehensively integrated into an improved active disturbance rejection controller which can suppress high frequency noise and abrupt disturbances to the better extent. Simulation and experiment results show that the joint observer can effectively reduce the observation error amplitude, correct the observation phase lag in advance, and obtain more accurate state information, and that the improved active disturbance rejection controller can better meet requirements of quadrotor UAV for fast response, efficient and stable control. Therefore, it can accurately and efficiently complete complex trajectory tracking operations.

Reliability Analysis of an Uncertain Single Degree of Freedom System Under Random Excitation

In practical engineering problems, uncertainty exists not only in external excitations but also in structural parameters. This study investigates the influence of structural geometry, elastic modulus, mass density, and section dimension uncertainty on the stochastic earthquake response of portal frames subjected to random ground motions. The North-South component of the El Centro earthquake in 1940 in California is selected as the ground excitation. Using the power spectral density function, the two-dimensional finite element model of the portal frame’s base motion is modified to account for random ground motions. A probabilistic study of the portal frame structure using stochastic finite elements utilizing Monte Carlo simulation is presented using the finite element program ABAQUS. The dynamic reliability and probability of failure of stochastic and deterministic structures based on the first-passage failure were examined and evaluated. The results revealed that the probability of failure increases due to the randomness of stiffness and mass of the structure. The influence of uncertain parameters on reliability analysis depends on the extent of variance in structural parameters.

A new interval-valued fuzzy multi-objective approach for project time–cost–quality trade-off problem with activity crashing and overlapping under uncertainty

PurposeMost projects are facing delays, and accelerating the pace of project progress is a necessity. Project managers are responsible for completing the project on time with minimum cost and with maximum quality. This study provides a trade-off between time, cost, and quality objectives to optimize project scheduling.Design/methodology/approachThe current paper presents a new resource-constrained multi-mode time–cost–quality trade-off project scheduling model with lags under finish-to-start relations. To be more realistic, crashing and overlapping techniques are utilized. To handle uncertainty, which is a source of project complexity, interval-valued fuzzy sets are adopted on several parameters. In addition, a new hybrid solution approach is developed to cope with interval-valued fuzzy mathematical model that is based on different alpha-levels and compensatory methods. To find the compatible solution among conflicting objectives, an arithmetical average method is provided as a compensatory approach.FindingsThe interval-valued fuzzy sets approach proposed in this paper is denoted to be scalable, efficient, generalizable and practical in project environments. The results demonstrated that the crashing and overlapping techniques improve time–cost–quality trade-off project scheduling model. Also, interval-valued fuzzy sets can properly manage expressions of the uncertainty of projects which are realistic and practical. The proposed mathematical model is validated by solving a medium-sized dataset an adopted case study. In addition, with a sensitivity analysis approach, the solutions are compared and the model performance is confirmed.Originality/valueThis paper introduces a new continuous-based, resource-constrained, and multi-mode model with crashing and overlapping techniques simultaneously. In addition, a new hybrid compensatory solution approach is extended based on different alpha-levels to handle interval-valued fuzzy multi-objective mathematical model of project scheduling with influential uncertain parameters.

Uncertainty analysis of NOx and CO emissions in industrial ethylene cracking furnace using high-precision sparse polynomial chaos expansion

ABSTRACT Traditional designing of ethylene-cracking furnaces by computational fluid dynamics (CFD) does not consider the uncertainties in actual engineering. This paper introduces one uncertainty analysis framework based on non-intrusive polynomial chaos expansion (NIPCE) and multi-order Sobol indices to perform uncertainty quantification (UQ) and sensitivity analysis for NOx and CO emissions in the combustion process. In this framework, several operating parameters of the bottom burners that easily cause uncontrollable fluctuations in the flame and emission characteristics are selected as uncertainty variables and characterized as a probability distribution. Computationally expensive CFD simulation of cracking furnace is used to generate a small number of samples, which are carried out to develop high-precision sparse PCE models by the degree-adaptive scheme and least angle regression (LAR) algorithm. And multi-order Sobol sensitivity analysis based on PCE models is performed efficiently to research the influence of uncertain parameters on pollution generation. Under 3% of burner’s uncertainty operating parameters, the results find that the excess air coefficient of 1.20 can obviously reduce the generation of NOx and CO emissions and control the fluctuation caused by uncertainties. Moreover, sensitivity analysis determines the critical variables that affect pollutant emissions to help the actual process avoid the unknown effects of uncertainties.

Interval uncertainty analysis of vibration response of hydroelectric generating unit based on Chebyshev polynomial

The uncertainty of the hydroelectric generating unit (HGU) is unavoidable in practical engineering. However, up to now, the research about influences of the uncertain parameters on the system responses is still very scarce, especially in the case that the probability distribution of the uncertain parameter is not clear. So, this paper will aim to study the influence of interval uncertain parameters on the vibration response characteristics of HGU. Firstly, a nonlinear mathematical model of rotor-shaft-bearing system of the HGU is established under the action of interval uncertain parameters. Then, based on Chebyshev polynomial and interval analysis theory, three feasible analysis methods of interval uncertainty for the HGU are proposed, which are also compared with the brute-force Monte Carlo Simulation (MCS) method. And it is proved the proposed Chebyshev-MCS method has higher accuracy and lower cost obtaining the vibration response boundary of the HGU. Next, using Chebyshev-MCS method, the uncertain response characteristics of the system under single parameter, multiple parameters and shock excitation are further analyzed, respectively. Meanwhile, the upper and lower boundaries of the vibration response under different degrees of uncertainty and rotational speeds are obtained. Besides, the coupling effects of several uncertain parameters on the vibration response under the rated rotational speed condition are also presented. And the influence of uncertain parameters on the transient characteristics of the system under shock excitation is also gotten. More importantly, the proposed method and obtained conclusions are helpful to the manufacture, installation, operation of the HGU.

Energy hub-based optimal planning framework for user-level integrated energy systems: Considering synergistic effects under multiple uncertainties

The design of user-level integrated energy systems is challenged by a variety of combinations of energy converters, complex cascade utilization of multiple energy flows, as well as sequential matching between stochastic energy resources and periodic energy demands. These issues can be addressed by an optimal energy-hub based planning framework considering the synergistic effects under multiple uncertainties. The energy hub model is extended to analyze energy-level matching and source–load balance with time-varying coupling factors representing part-load characteristics. The optimization problem is formulated as a bi-level planning model with uncertainties evaluated by a two-stage global sensitivity analysis. The bi-level planning model determines the system structure and component sizing at the upper level and identifies the optimal operation strategy at the lower level by employing piecewise linearization of part-load characteristics of components involved. The global sensitivity analysis reduces model size with the elementary effect method, identifies the most influential uncertain parameters with a variance-based method. A case study in Beijing is demonstrated for the proposed methodology. The results show that the proposed method can effectively plan the integrated energy system considering sequential source–load matching with the rational scheduling strategy of components. The demand-side response influences the system configuration and renewable energy penetration. Integrating the components’ part-load characteristics help avoid the mismatch between the component capacity and energy demand, reducing 6.8% cost compared to the scheme with constant energy efficiency. The three most influential factors identified among 551 uncertain parameters are natural gas price, valley electricity price and nominal efficiency of the gas turbine.

Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis

This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure. In this paper, we focus on the ground impact footprints that contains 95%, 99% and 99.9% of the drone impacts. These regions are defined here with density minimum volume sets and may be estimated by Monte Carlo methods. As Monte Carlo approaches lead to an underestimation of extreme ground impact footprints, we consider in this article multiple importance sampling to evaluate them. Then, we perform a reliability oriented sensitivity analysis, to estimate the most influential uncertain parameters on the ground impact position. We show the results of these estimations on a realistic drone flight scenario.

Magnetic levitation planar motor and its adaptive contraction backstepping control for logistics system

Magnetic levitation planar motor has the characteristics of no friction loss, fast dynamic response, and real-time change of transportation track according to the demand. Therefore, a kind of magnetic levitation planar motor based on logistics transportation system is designed, which can meet the transportation needs of the logistics system through any combination of unit modules. Firstly, based on the mechanical model of magnetic levitation float in Halbach permanent magnet array magnetic field, establishing the thrust and torque model of maglev float. Secondly, according to the force/current relationship, considering the influence of uncertain parameters and load disturbance on the system, the decoupling control model of six degree of freedom motion system of magnetic levitation planar motor is established. Thirdly the adaptive contraction backstepping (ACB) controller is derived that can eliminate the uncertain disturbance of the nonlinear model and realize the real-time control of the system. The simulation and experimental results demonstrate that the method has expected response speed, strong robustness, and good dynamic tracking performance. Applying it to the logistics transportation system described in this article and has a good control effect.

Research on dynamic transmission error of harmonic drive with uncertain parameters by an interval method

Transmission error is one of the most important performance indicators for evaluating harmonic drives, and can have crippling effects on positioning accuracy and stability of industrial robots. However, most of the existing error analysis methods focus on a single factor, and do not consider the uncertainty of dynamic parameters, leading to evident limitations. In the present study, static transmission error (caused by manufacturing and assembly error) and dynamic transmission error (generated by static transmission error and dynamic parameters) of a harmonic drive are modeled. An interval method is developed and used to numerically express uncertain dynamic parameters of the system. Chebyshev polynomials are used to approximate the dynamic differential equations of the harmonic drive, and then the distribution of dynamic transmission error and its relationship with uncertain parameters are discussed in detail. In addition, a global sensitivity analysis is carried out to intuitively demonstrate how much impact each parameter has on dynamic transmission error. Our results suggest that the moment of inertia Jin and the torsional stiffness coefficient k1 have a large influence on dynamic transmission error. Finally, the proposed method is validated by experiment. The method can be adopted to determine the upper and lower bounds of dynamic transmission error of dynamic systems under the influence of uncertain parameters and provides a theoretical basis for transmission error optimization and compensation.

Parameter Identification of a Power Loss Model for Vehicle Transmissions Based on Sensitivity Analysis

<div class="section abstract"><div class="htmlview paragraph">As the transmission design directly impacts drive unite operation and power flow to the driveline, the transmission power loss is a critical target in the drivetrain development. The demand of more precise and more efficient power loss prediction has therefore increased significantly, which highlights the need of new methodologies in order to optimize the power loss model for vehicle transmissions. The possible power losses that exist in the power flow path, are gear mesh losses, gear churning losses, gear windage losses, bearing losses, synchronizer losses and sealing losses. Thanks to the decades of research, analytical models are available for the prediction of these component losses, which could deliver power loss distributions and overall efficiency maps of complex transmissions.</div><div class="htmlview paragraph">The aim of this paper is to introduce a methodology to improve the accuracy of a chosen power loss model on a system level. A detailed power loss prediction for a two-speed transmission in an electric vehicle has been performed. The simulated overall power losses and the available experimental results match well. However, since many assumptions are made in the analytical modelling process, there are still deviations between the predicted and the measured results. In order to reduce the deviations, all uncertain parameters are firstly analyzed based on the parameter sensitivity analysis method FAST that allows determining the influential uncertain parameters. The sensitivities of those influential parameters are locally defined at all operating regions, by which the sensitive operating areas of all influential uncertain parameters to the overall power losses could be defined. The identification of these parameters at their sensitive regions prevents the unnecessary interference with other uncertain parameters at the identification process. With the help of identified parameters, a better proximity between the simulated and measured results is achieved.</div></div>

Comprehensive sensitivity analysis of rotational stability of a super-deep underground spherical structure considering uncertainty

Jiangmen Underground Neutrino Observatory central detector is located 700 m below the ground and also submerged into an ultrapure water pool. The main structure of the Jiangmen Underground Neutrino Observatory central detector is a hybrid spherical shell that is vulnerable to rotation under the buoyancy effect. The influences of the model parameters on the rotational stability of this complex and unique structure are investigated. Since the model parameters are inevitably subjected to many sources of uncertainties (e.g. manufacturing tolerances and geometrical imperfections), the parameter uncertainty is taken into account. In addition, linear and nonlinear rotational stabilities of this super-deep underground spherical structure are also under consideration. Specifically, the critical loading multiplier is used as the evaluation indicator of linear rotational stability and the load proportionality factor- θ curve is considered as the evaluation indicator of nonlinear rotational stability. The sensitivity of linear and nonlinear rotational stabilities to uncertain parameters is systematically studied in terms of univariate and multivariate global sensitivity analyses. The univariate global sensitivity analysis is able to evaluate the effects of uncertain parameters on each evaluation indicator, whereas multivariate global sensitivity analysis enables to assess the global influence of uncertain parameters on all evaluation indicators. A polynomial chaos expansion surrogate model is utilized to replace the time-consuming simulation model for analytical implementation of the univariate and multivariate global sensitivity analyses. The present polynomial chaos expansion-based univariate and multivariate global sensitivity analyses effectively and efficiently reveal the sensitivity of the rotational stability of this super-deep underground spherical structure to uncertain parameters, and provide a practical method for comprehensive sensitivity analysis of similar structures.

Accounting for techno-economic parameters uncertainties for robust design of remote microgrid

This paper presents two different approaches to deal with uncertainties in the design optimization of renewable hybrid power systems in order to enhance the decision-making. The first one, Sensitivity Analysis Approach (denoted as SAA), takes the uncertainties into account after the optimization of the system. It permits first to evaluate the sensitivity on the static performances of the optimized system through uncertainty propagation. Secondly, it permits to identify the most influential uncertain parameters through Global Sensitivity Analysis (denoted as GSA). The second approach, called Robust Optimization (RO), integrates a Monte Carlo (MC) simulation into the process of optimization conducted with a Genetic Algorithm (GA). The two approaches have been set up and applied to a remote power system or microgrid, under uncertainties on techno-economic parameters. This illustrative case study is the electrical supply of a stand-alone application located in Nigeria, using photovoltaic production associated to a hybrid energy storage with a bank of batteries and a complete hydrogen chain (with an electrolyzer, a gas tank and a Fuel Cell (FC). Classically, the main source of uncertainties of such a system is associated to the temporal variation of renewable energy sources and load demands. Instead, this paper focuses on uncertainties of techno-economic parameters to improve the reliability of the optimization process for a stand-alone power system. Moreover, from a precise analysis of the state-of-the-art of such uncertainties, the authors propose to investigate complementarities of Sensitivity Analysis Approach and Robust Optimization. This study also aims to propose a methodological framework for any designer projecting to take into account uncertainties on techno-economic parameters. The results show the high interest to take into account such uncertainties for the decision-making and the ability of RO to limit their impact on system performance indicators.

Fatigue analysis of structures with interval axial stiffness subjected to stationary stochastic excitations

The fatigue analysis of linear discretized structures with uncertain axial stiffnesses modeled as interval variables subjected to stationary multi-correlated Gaussian stochastic excitation is addressed. Due to uncertainty, all response quantities, including the expected fatigue life, are described by intervals. The key idea is to extend an empirical spectral approach proposed by Benasciutti and Tovo (Probab Eng Mech 21(4):287–299, 2006. https://doi.org/10.1016/j.probengmech.2005.10.003 ), called $$ \alpha_{0.75}^{{}} $$ -method, to the interval framework. According to this approach, the expected fatigue life depends on four interval spectral moments of the critical stress process. Within the interval framework, the range of the interval expected fatigue life may be significantly overestimated by the classical interval analysis due to the dependency phenomenon which is particularly insidious for stress-related quantities. To limit the overestimation, a novel sensitivity-based procedure relying on a combination of the Interval Rational Series Expansion (Muscolino and Sofi in Mech Syst Signal Process 37(1–2):163–181, 2013. https://doi.org/10.1016/j.ymssp.2012.06.016 ) and the Improved Interval Analysis via Extra Unitary Interval (Muscolino and Sofi in Probab Eng Mech 28:152–163, 2012. https://doi.org/10.1016/j.probengmech.2011.08.011 ) is proposed. This procedure allows one to detect the combinations of the bounds of the interval axial stiffnesses which yield the lower bound and upper bound of the interval expected fatigue life. Sensitivity analysis is also exploited to identify the most influential uncertain parameters on the expected fatigue life. For validation purposes, a truss structure with uncertain axial stiffness under wind excitation is analyzed.

Seismic Fragility Curves and Damage Probabilities of Concrete Gravity Dam Under Near–Far Faults Ground Motions

Dams are very costly structures that are often built in areas prone to seismicity due to geological and geomorphological considerations. To protect such structures against seismic actions, in-depth studies on their vulnerability should be undertaken. This study deals with the seismic vulnerability of a concrete gravity dam. The Oued El Fodda dam on the Oued Chelif River, west Algeria, is considered as a case study. The dam is studied under near- and far-fault ground motions. To derive fragility curves, non-linear time history analyses are performed by using a Latin hypercube sampling numerical simulation procedure. Damage probabilities are determined for each defined limit state. The results show the vulnerability of the dam under high-level seismic motion. A sensitivity analysis was performed to evaluate the influence of uncertain parameters on structural responses. The most important parameters are then identified.