Uncertainties In Material Parameters Research Articles

A hybrid approach combining UD and GA-CV-SVM to evaluate shear performance in high asphalt concrete core

The shear effect on high asphalt concrete core is significant. However, studies on the reliability of 100-meter-scale cores against shear damage remain limited. A key challenge in this research field is establishing the control criteria for the core and improving the computational efficiency of implicit limit state function (LSF). Additionally, the impact of material parameter uncertainty on the shear failure reliability of the core during the dam construction and impoundment stages remains unclear. To address this, a safety evaluation method based on time discretization was proposed, combining uniform design (UD), K-fold cross-validation (K-CV), and genetic algorithm (GA) to optimize the support vector machines (SVM). The core parameters of 52 asphalt concrete-core rockfill dams (ACCRDs) were analyzed, with the statistical values of the basic variables considered in determining the reliability index. The theoretical derivation of the critical shear failure safety index established a stability formula to assess the safety state of the dam core. The significance parameters were identified, and the sample points were generated at each stage using UD, and the Support Vector Regression (SVR) was applied to reconstruct the LSF, and reliability was calculated through the checking point method.

Assessment of Slope Stability Taking into Account the Uncertainty of Standard Seismic Impacts and Material Parameters of the Site

Assessment of Slope Stability Taking into Account the Uncertainty of Standard Seismic Impacts and Material Parameters of the Site

Uncertain seismic response and robustness analysis of isolated continuous girder bridges

When calculating the seismic response of structures, conventional methods often neglect uncertainty and suffer from excessive computation. Although robustness metrics have been effective in evaluating structural safety, they are not suitable for isolated continuous girder bridges (ICGB). To address this issue, this paper introduces an uncertain seismic response and robustness assessment method tailored to ICGB: Firstly, the method considers uncertainty in material parameters and ground motion by constructing a data set using Monte Carlo simulation. Secondly, a genetic algorithm for parallel optimisation of radial basis function neural network (GAPO-RBFNN) is trained to predict the seismic responses of the bridges. Thirdly, a robustness metric specifically designed for bridges is derived and applied to bridge. Finally, the proposed robustness metric is validated on lead rubber bearing (LRB) and shape memory alloy-lead rubber bearing (SMA-LRB) bridges. Results indicate that the GAPO-RBFNN model accurately approximates the mapping relationship between structural parameters and seismic response, with an average error of only 0.32% and a 70% reduction in computation time compared with traditional methods. Moreover, the new robustness metric overcomes the limitations of the dismantled member method and is applicable to bridge. The robustness of the SMA-LRB-ICGB, strengthened by SMA, is improved, providing evidence of the effectiveness of the proposed robustness metric.

Collapse fragility analysis of historical masonry buildings considering in-plane and out-of-plane response of masonry walls.

This paper explores the seismic fragility assessment of historical masonry buildings in the context of performance-based earthquake engineering, using multiple-record incremental dynamic analyses (MR-IDA). The methodology is applied to two case studies, the first being a stiff monumental structure, and the second a tall and slender masonry building. Both case studies are modelled in the OpenSees software using three-dimensional macroelements that account for the in-plane (IP) and out-of-plane (OOP) response of masonry walls. Simultaneously, the numerical models account for the influence of non-linear connections in floor-to-wall interfaces and wall-to-wall interlocking. The record-to-record variability induced by the random nature of the ground motion is addressed through earthquake selection consistent with the variability of European hazard. Ground motion records are normalised at a uniform level of seismic intensity to be sequentially increased for the computation of IDA curves. Once IDA curves and fragility functions are derived, the failure results are thoroughly discussed. The fragility functions accounting for the uncertainties arising from the record-to-record variability are contrasted against the results of the seismic assessments affected by multiple sources of uncertainty in material and modelling parameters from a previous study. Finally, the fragility curves derived from the methodology presented herein are directly compared with fragility functions from probabilistic seismic demand analysis (PSDA). Overall, IDA-based fragility functions are found to provide more conservative predictions than the ones obtained from the cloud-based approach using unscaled records.

Influence Analysis of Material Parameter Uncertainties on the Stability Safety Factor of Concrete Gravity Dams: A Probabilistic Method

Anti-sliding stability safety is a critical issue that must be given sufficient and widespread attention during the entire lifecycle of gravity dams. The calculated anti-sliding stability safety factor (ASS-SF) is usually compared with the allowable value required by the standards in the traditional method, which ignores the influence of material parameter uncertainties and leads to unreasonable safety evaluation results. Therefore, the nonlinear functional relationship between the stability safety factor (SF) and the random variable parameters is constructed based on the response surface equations, and the distribution types of SF sequences calculated by the Monte Carlo sampling are determined, then a probabilistic stability evaluation method for concrete gravity dams is proposed. Engineering application shows that the calculated SF obeys the normal distribution; the minimum guaranteed rate of different sliding paths in a gravity dam is 86.66%, and the guaranteed rate for the overload safety factor (OSF) is 36.00%. The results imply that a guaranteed rate for the allowable value of the ASS-SF should be provided when making the stability safety evaluation of the dams, especially the OSF. The outcome of this research will advance the understanding of stability evaluation of concrete dams, and reduce the potential risk of sliding instability of concrete dams.

Study on Seismic Behavior and Component Vulnerability of Corroded RC Shear Walls Subjected to Flexural Shear Failure

ABSTRACT The impact of corrosion degree, axial compression ratio, reinforcement ratio of longitudinal bar and horizontal distribution rebar, and stirrup of boundary column on seismic performance of RC shear walls were investigated. The corrosion forms (rust expansion cracks and corroded steel bars), failure process, hysteretic characteristics, bearing capacity, and deformability were compared. Subsequently, a numerical model of corroded RC shear walls was established adopting shell elements. Finally, according to the numerical simulation method, considering the uncertainty of corrosion degree and material parameters, the vulnerability models of RC shear wall components with different corrosion degrees were built. The results show that the mechanical properties of the shear wall were seriously deteriorated due to the corrosion of steel bars, and the ultimate displacement and ductility coefficient of severely corroded specimens were reduced by 28.5% and 12.27% respectively, compared to non-corroded specimens. A proposed numerical modeling method for corroded RC shear walls based on shell element is accurate, and the bearing capacity error and energy dissipation error of the simulation and test results were less than 20%. As the degree of corrosion grew, the probability of shear wall components experiencing more severe failure added significantly. When the displacement angle was 1/120 (This is the limit value of elastic-plastic displacement angle specified in the code), the probability of DS3 and DS4 occurring in the shear wall with a corrosion degree of 0% were 56% and 39.44%, and the probability of DS4 and DS5 occurring in the shear wall with a corrosion degree of 10% were 41.1% and 38.44%.

Probability Prediction of Curing Process-Induced Deformation for V-Shape Composite Structures Based on FEM Method and Data Mining.

Continuous fiber-reinforced composites are increasingly used in industry for their superior specific modulus and strength. The curing process-induced deformation (PID) has been a critical problem during manufacturing, which always exhibits dispersed values even if the curing process curve and structural parameters remain consistent. This work conducted probability prediction of PID for V-shape composite structures based on the FEM method and data mining. A sequential coupling thermal-chemical-mechanical coupling FE model is established in ABAQUS. The prediction accuracy of the included angle between two sides is verified by the experimental results. Material parameter uncertainties are considered for V-shape structures with different radii and thicknesses. Based on the dataset from the FE model, a decision tree is established and trained to analyze the sensitivity and to predict the probability distribution of PID. The results show that PID increases with the coefficients of thermal expansion in the in-plane perpendicular fiber direction and out-of-plane normal direction. The data-mining method is accurate enough for the PID prediction, and its efficiency provides an additional calculation option in engineering applications.

Drift damage limit estimation based on cracks of concrete gravity dam considering dam-foundation interaction

ABSTRACT This study focuses on estimating the drift limits associated with different damage states of concrete gravity dams, emphasizing the role of cracks along the dam base and the interaction between the dam and its foundation. Employing a comprehensive framework and nonlinear pushover analysis, the research accounts for uncertainties in concrete and foundation material parameters, evaluating three distinct models with varying height-to-base width ratios (1, 1.25 and 1.5) through 20 runs based on central composite design. The findings indicate that as the height-to-base width ratio increases, the concrete gravity dam demonstrates greater resilience against lateral movements, highlighting its ability to withstand larger drifts without incurring damage. Additionally, the study’s proposed mathematical model accurately predicts the drift limits across different levels of damage, facilitating a comprehensive understanding of the structural behavior and its implications for seismic risk assessments. The research underscores the significance of considering the varying degrees of damage and their impact on the concrete gravity dam’s operational capability and safety, offering crucial insights for ensuring the integrity and stability of these essential infrastructures.

Effect of variability of mechanical properties on the predictive capabilities of vulnerable coronary plaques

Background and Objective:Coronary plaque rupture is a precipitating event responsible for two thirds of myocardial infarctions. Currently, the risk of plaque rupture is computed based on demographic, clinical, and image-based adverse features. However, using these features the absolute event rate per single higher-risk lesion remains low. This work studies the power of a novel framework based on biomechanical markers accounting for material uncertainty to stratify vulnerable and non-vulnerable coronary plaques. Methods:Virtual histology intravascular ultrasounds from 55 patients, 29 affected by acute coronary syndrome and 26 affected by stable angina pectoris, were included in this study. Two-dimensional vessel cross-sections for finite element modeling (10 sections per plaque) incorporating plaque structure (medial tissue, loose matrix, lipid core and calcification) were reconstructed. A Montecarlo finite element analysis was performed on each section to account for material variability on three biomechanical markers: peak plaque structural stress at diastolic and systolic pressure, and peak plaque stress difference between systolic and diastolic pressures, together with the luminal pressure. Machine learning decision tree classifiers were trained on 75% of the dataset and tested on the remaining 25% with a combination of feature selection techniques. Performance against classification trees based on geometric markers (i.e., luminal, external elastic membrane and plaque areas) was also performed. Results:Our results indicate that the plaque structural stress outperforms the classification capacity of the combined geometric markers only (0.82 vs 0.51 area under curve) when accounting for uncertainty in material parameters. Furthermore, the results suggest that the combination of the peak plaque structural stress at diastolic and systolic pressures with the maximum plaque structural stress difference between systolic and diastolic pressures together with the systolic pressure and the diastolic to systolic pressure gradient is a robust classifier for coronary plaques when the intrinsic variability in material parameters is considered (area under curve equal to [0.91–0.93]). Conclusion:In summary, our results emphasize that peak plaque structural stress in combination with the patient’s luminal pressure is a potential classifier of plaque vulnerability as it independently considers stress in all directions and incorporates total geometric and compositional features of atherosclerotic plaques.

CCZM‐based fatigue analysis and reliability assessment for wind turbine blade adhesive joints considering parameter uncertainties

AbstractWind turbine blades are complex structures composed of multiple bonded components. The fatigue performance of these adhesive joints is crucial for ensuring operational safety over the blade's lifespan. Traditional structural fatigue analysis methods are inadequate for evaluating the fatigue properties of these joints due to the unique characteristics of adhesive materials. Variations in material and dimensional parameters, as well as fluctuating operational loads, further complicate the fatigue analysis of adhesive joints in wind turbine blades. To tackle this issue, this study introduces a fatigue analysis and reliability assessment method for the adhesive joints of wind turbine blades, employing the Cyclic Cohesive Zone Model (CCZM) and accounting for parameter uncertainties. Specifically, a novel methodology for fatigue analysis based on the CCZM is presented. The methodology is programmatically implemented to obtain a fatigue life dataset through multiple simulations, considering uncertainties in material parameters, adhesive dimensions, and loads. Subsequently, a fatigue reliability model is formulated to evaluate the fatigue reliability of adhesive joints in wind turbine blades under different parameter conditions, and the sensitivity of fatigue reliability to each parameter is investigated. The findings offer valuable insights for improving the safety and reliability of adhesive structures in wind turbine blades.

Neural network assisted for parameters uncertainty of isogrid tube under different load types

Isogrid structures offer an excellent balance between mass and rigidity, making them a crucial component in various industrial sectors. However, like all types of structures, isogrids are susceptible to performance degradation due to uncertainties in geometrical and material parameters arising from manufacturing errors and exposure to critical service environments. This paper addresses the effects of parameter uncertainty on the structural behavior of isogrid tube structures. To this end, a Sobol index is employed to assess the sensitivity of the tube’s natural frequency, axial buckling, torsional buckling, and overall mass to three geometrical and one material parameter, each subject to a 10% random variation. The data for this analysis were generated using a parametrized numerical model specifically developed for this purpose. To optimize computational costs further, an artificial neural network predictive model is proposed, which accurately replicates the behavior of the numerical model. The results indicate that the angle between helicoidal ribs is the most influential parameter.

Thermomechanical problem of functionally graded spherical shells based on homogenization schemes: Data-driven volume fraction optimization with material uncertainties

Mechanical analysis and optimal design for functionally graded materials (FGMs) structures are significant. The present paper analyzes the parameter uncertainties problem for the thermomechanical response of the FGMs spherical shells with volume fraction power distribution. Here, the interval random uncertainty model is recommended to describe the uncertainties of each component of material parameters in FGMs, and a data-driven artificial neural network (ANN) fast calculation for FGMs spherical shells thermomechanical problems with uncertainties analysis is established and trained. The thermomechanical coupling response analysis and volume fraction optimization analysis of the FGMs spherical shell with material uncertainties are realized for zirconia-titanium FGMs, with the maximum normalized Von Mises stress as the structural safety index and the ANN as the fast calculation method. In addition, the Von Mises stress of the FGMs spherical shell and a ZrO2-Ti alloy double-layer spherical shell under different loads is compared. The results show that material parameter uncertainties, especially those in Young's modulus, thermal expansion coefficient, and uniaxial yield limit significantly affect structural safety. The structural safety index λ calculated based on the volume fraction optimization design of FGMs spherical shell is significantly lower than that of the ZrO2-Ti alloy double-layer. The ANN method greatly improves the computational efficiency for uncertain problems and maintains high accuracy, compared with the statistical analysis after many calculations by randomly selecting material parameters. This study provides a path for the safety design of FGMs structures with uncertain parameters.

Analysis of seepage failure probability for high core rockfill dams during rapid drawdown of reservoir water level

Rapid drawdown (RDD) is an abnormal reservoir flood control operation that may cause seepage failure of high core rockfill dams. In this paper, a novel framework combining Bayesian inference and transient unsaturated seepage simulation is proposed to evaluate the seepage failure probability of the core during RDD. The dynamic hydraulic boundaries utilized in the simulation are first determined by the performance of water release structures. The extreme learning machine (ELM) is then employed as the surrogate model to establish the mapping relationship between material parameters with simulated seepage pressure and hydraulic gradient. The Bayesian inference method is applied to characterize the uncertainty of material parameters based on seepage monitoring data. Finally, the posterior distribution samples of random parameters are utilized to calculate the seepage failure probability of the core. The seepage safety of the core under different drawdown rates is studied with a real 186 m high core rockfill dam. The results reveal that a faster RDD rate can lead to an increased probability of seepage failure. The maximum failure probability is 35.56 % for the four release structures working together (about 0.316 m/h), higher than 23.16 % for the three release structures (about 0.165 m/h). It is also found that the peak of seepage failure probability often occurs in the middle stage of RDD. Therefore, to ensure the safety of the project, it is important to control the RDD rate and carefully monitor the seepage pressure. From the point of view of seepage failure, the proposed method for evaluating the safety of core rockfill dams under RDD is of high guiding significance to the risk assessment of similar projects under extreme conditions.

Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Failure modes-based seismic fragility analysis of concrete gravity dams considering concrete heterogeneity

The heterogeneity of mass concrete often leads to the initiation of micro-cracks in gravity dams, while the accumulation of damage of concrete on the meso-scale leads to the cracking behaviour on the macro-scale. To improve the seismic safety of the dam, a comprehensive study on the effect of heterogeneous properties is crucial in seismic fragility analysis. A novel methodology considering concrete heterogeneity for seismic fragility analysis was developed by combining damage element model for seismic failure analysis, a five-level standard for failure modes-based seismic damage state and a method to generate seismic fragility curves. Then, seismic failure analysis of the Koyna gravity dam was taken as an example to verify the correctness of the damage element model, the damage process of dam from micro-cracks to macro-cracks were simulated. Finally, the seismic fragility of a practical dam in China was studied to verify the effectiveness of the proposed methodology. The results showed that simulated failure modes-based fragility curves were suitable for the target dam and more capable of considering the uncertainty of concrete material parameters. The obtained fragility curve proved valuable in optimizing seismic design, reinforcement and maintenance measures and improve seismic capability of the dam.

Acoustic Performance of a Metamaterial Coating with Uncertainty in Design Parameters

The effect of uncertainty in design parameters on the acoustic performance of a viscoelastic metamaterial coating is investigated. The coating is composed of a layer of hard inclusions embedded in a soft host medium. The layer of inclusions in the soft medium is modelled as a homogenised layer using effective medium approximation theory. Stochastic models based on the polynomial chaos expansion (PCE) method and Monte Carlo (MC) simulations are developed. The effect of uncertainty in geometric and material parameters are quantified by examining the mean and envelopes of the absorption coefficient. Uncertainty in geometric and material parameters produce similar levels of variation in the absorption coefficient of the coating, particularly around the peaks of the absorption coefficient spectrum.

Analysis of material parameter uncertainty propagation in preoperative flap suture simulation

Skin flap transplantation is the most commonly used method to repair tissue defect and cover the wound. In clinic, finite element method is often used to design the pre-operation scheme of flap suture. However, the material parameters of skin flap are uncertain due to experimental errors and differences in body parts. How to consider the influence of material parameter uncertainty on the mechanical response of flap suture in the finite element modeling is an urgent problem to be solved at present. Therefore, the influence of material parameter uncertainty propagation in skin flap suture simulation was studied, Firstly, the geometric model of clinical patient’s hand wound was constructed by using reverse modeling technology, the patient’s three-dimensional wound was unfolded into a flat surface by using curved surface expansion method, yielding a preliminary design contour for the patient’s transplant flap. Based on the acquired patient wound geometry model, the finite element model of flap suture with different fiber orientations and different sizes was constructed in Abaqus, and the uncertainty propagation analysis method based on Monte Carlo simulation combined with surrogate model technology was further used to analyze the stress response of flap suture considering the uncertainty of material parameters. Results showed that the overall stress value was relatively lower when the average fiber orientation was 45°. which could be used as the optimal direction for the flap excision. when the preliminary design contour of the flap was scaled down within 90%, the stress value after flap suturing remained within a safe range.

Fragility curves accounting for uncertainties in material parameters and ground motion characteristics using a data driven surrogate model

This study uses a statistical surrogate model to develop fragility curves for an infilled reinforced concrete frame building, considering uncertainties in both material properties and ground motion parameters. The focal point of this study is a school building in Nepal damaged during the 2015 Gorkha earthquake. The school was instrumented, and its seismic response was simulated using a nonlinear numerical model. The model, developed following a recently proposed framework and extensively validated with the field data, is used in a parametric study conducted to identify the most influential material parameters (MPs). The model is then used in incremental dynamic analyses conducted to provide data for the calibration of a surrogate model. The three-staged least square statistical modeling approach is adopted to relate the influential MPs and ground motion intensity measures with important response quantities related to the peak and residual first-story drift ratios. The surrogate model is employed to generate fragility curves accounting for the two sources of uncertainty. The results indicate that accounting for uncertainties associated with the MPs can alter the fragility curves, causing a shift in the prediction of the median and dispersion of intensity measures.

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.

Computations and measurements of the magnetic polarizability tensor characterisation of highly conducting and magnetic objects

PurposeThe ability to characterise highly conducting objects, that may also be highly magnetic, by the complex symmetric rank–2 magnetic polarizability tensor (MPT) is important for metal detection applications including discriminating between threat and non-threat objects in security screening, identifying unexploded anti-personnel landmines and ordnance and identifying metals of high commercial value in scrap sorting. Many everyday non-threat items have both a large electrical conductivity and a magnetic behaviour, which, for sufficiently weak fields and the frequencies of interest, can be modelled by a high relative magnetic permeability. This paper aims to discuss the aforementioned idea.Design/methodology/approachThe numerical simulation of the MPT for everyday non-threat highly conducting magnetic objects over a broad range of frequencies is challenging due to the resulting thin skin depths. The authors address this by employing higher order edge finite element discretisations based on unstructured meshes of tetrahedral elements with the addition of thin layers of prismatic elements. Furthermore, computer aided design (CAD) geometrical models of the non-threat and threat object are often not available and, instead, the authors extract the geometrical features of an object from an imaging procedure.FindingsThe authors obtain accurate numerical MPT characterisations that are in close agreement with experimental measurements for realistic physical objects. The assessment of uncertainty shows the impact of geometrical and material parameter uncertainties on the computational results.Originality/valueThe authors present novel computations and measurements of MPT characterisations of realistic objects made of magnetic materials. A novel assessment of uncertainty in the numerical predictions of MPT characterisations for uncertain geometry and material parameters is included.