Material Uncertainties Research Articles

Efficient Approximation of Varying Fiber Orientation States in Injection Molded Parts Under Consideration of Multiple Manufacturing Uncertainties

AbstractThe production of high-quality fiber reinforced polymer parts is an important aspect in several industrial areas. However, due to unavoidable uncertainties in material and manufacturing processes, the part quality scatters. One important aspect here is the fiber orientation, being crucial for the thermo-mechanical properties of the part and being influenced by the uncertain material state and process conditions. Process simulations are an important tool for predicting the fiber orientation, but state-of-the-art simulations are normally deterministic and represent only one specific case. Performing enough deterministic simulations to model manufacturing uncertainties requires high numerical effort. Therefore, this work presents methods to quickly and efficiently approximate the fiber orientation under varying material and process parameters, requiring only a few simulations as input. Different schemes for approximation are evaluated and compared with each other and with 3D process simulations.

Advanced Sobol sensitivity analysis of a 1:4-scale prestressed concrete containment vessel using an ANN-based surrogate model

The safety of nuclear power plants can be enhanced by performing sensitivity analyses to identify and evaluate factors that significantly impact their structural integrity. In this study, an artificial neural network-based surrogate model was developed using an experimentally validated finite element model. A Sobol sensitivity analysis was performed using this model to quantitatively assess the impact of material uncertainty on the internal pressure–displacement behavior of containment buildings at various internal pressure levels. The results indicate that the compressive strength of concrete and the prestressing force in the hoop direction critically influence the behavior of containment buildings, with their importance varying according to the internal pressure level. Furthermore, the proposed surrogate model can efficiently substitute for expensive and complex finite element analyses, enabling effective exploration of the multidimensional uncertainty space and quantitative assessment of the major factors affecting structural behavior. The process presented herein facilitates the quantification of factors under various uncertainty scenarios.

A Comparative Study of Precast and Cast-in Place Concrete in Construction Home Project by Life Cycle Cost Analysis

Construction industry plays a significant role in economic growth, and the industry will continue to grow with the economic expansion. However, the industry is known for its labor-intensiveness, In order to shorten construction time and address labor issues, developers have explored new systems of construction such as a precast system. This paper is to present a comparative study of precast and cast-in place concrete system in construction home project by life cycle cost analysis by dividing project period into 3 phase (Preparing phase, Construction phase and O&M phase). In modelling life cycle costs, sensitivity analysis and Monte Carlo simulation will be employed to incorporate uncertainty of material and labor costs into the proposed model. The study also found that in 6 home projects, there have risk factors that impact to life cycle cost by using sensitivity analysis are construction cost, major repair and minor repair. The result from this study is life cycle cost from precast and cast -in place systems

Static analysis using flexibility disassembly perturbation for material nonlinear problem with uncertainty

Nonlinear finite element analysis of large-scale structures usually requires a lot of calculation cost, because it is necessary to repeatedly inverse the modified stiffness matrix caused by nonlinearity in the calculation process. When considering the uncertainty in materials, the calculation of the nonlinear analysis will be more unbearable. To improve the computational efficiency, this work develops a new method for nonlinear analysis with material uncertainty based on flexibility disassembly perturbation (FDP) approach. The FDP is an algorithm that can quickly calculate the inverse of a stiffness matrix. The basic idea of the proposed method is to introduce the FDP formula into Newton-Raphson iteration method to accelerate the nonlinear iterative calculation. Three numerical examples, one statically determinate structure and two statically indeterminate structures, are used to verify the accuracy and efficiency of the proposed method. The results show that the calculation time of the proposed method is far less than that of the existing complete analysis and combined approximation algorithms. In terms of computational accuracy, for statically determinate structures, the proposed algorithm can obtain exact solutions that are identical to the complete analysis results, while for statically indeterminate structures, the proposed algorithm can obtain approximate solutions that are very close to the complete analysis results.

Navigating Competing Discourses in Mixed-Citizenship Romantic Relationships

ABSTRACT The United States has an increasing number of people from around the world, which has led to a rise in mixed-citizenship romantic relationships. Yet, these relationships and family structures remain significantly understudied. Mixed-citizenship romantic relationships, referring to individuals in a romantic relationship who hold or previously held different citizenships, are faced with various unique experiences in comparison to same-citizenship romantic relationships (e.g. immigration rhetoric, filing of paperwork, and financial obligations). Grounded in relational dialectics theory, this study examined how discourses operate and inform the meaning of mixed-citizenship romantic relationships, as well as the ways discourses compete. Through 15 semi-structured interviews, results reveal two discourses: A Discourse of Love is the Driving Force and A Discourse of Material Uncertainty. Moreover, discourses competed for dominance and, at times, intersected to create new meanings. Findings illuminate implications of talk for members of mixed-citizenship relationships, and offer suggestions for practitioners in communicating about these consequences.

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.

Safety analysis of temporary anchorage system for immersed tube in Shenzhen–Zhongshan Link

In the construction of the Shenzhen–Zhongshan Link, a temporary anchorage system, distributed uniformly along the pipe wall, has been employed. To assess the safety and reliability of this system, a combined method utilizing numerical analysis and model experiments was applied to study the safety of the temporary anchorage system and the reliability of the tension rods. Firstly, an overall model of the caisson segment based on GINA rebound force was established to analyze the stress state of the entire system. Secondly, a comprehensive numerical analysis and model experiment verification were conducted for the single tensioning system, revealing its failure mode and safety margin. The results indicate that the tension rod systems are uniformly stressed at an average of 444 kN during underwater jointing, with a safety factor of 1.94. At this point, the maximum von Mises stresses appearing at the front plate corners and the lower edge of the U-groove, with stress values of 181.8 MPa and 172.4 MPa, and safety factors of 1.54 and 1.71, respectively. When the tension rod force reaches 940 kN, the tensioning system reaches its bearing limit, with initial yielding occurring at the front plate corners. Model experiments were conducted to verify the theoretical analysis results, under a test load of 444 kN, the stresses at the front plate corners and the lower edge of the U-groove were 159.6 and 195.9 MPa, respectively. As the test load increased to 940 kN, these stresses reached 390 and 389 MPa, exhibiting good agreement with the numerical analysis. Considering the uncertainty of loads and materials, a reliability analysis of the tension rods was conducted, yielding a reliability index of 4.34, meeting the secondary safety standard. Based on the comprehensive analysis, it can be concluded that the temporary anchorage system in the caisson segments of the Shenzhen–Zhongshan Link exhibits excellent safety margins.

Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures

Probabilistic seismic performance assessments of engineered structures can be highly sensitive to the seismic input excitation and its variability. In the present study, the scenario-based performance assessment recommended by Federal Emergency Management Agency (FEMA) P-58 guidelines is adopted to estimate seismic fragility of concrete dams for various seismic hazard scenarios. Due to the scarcity of recorded ground motions and thereby their poor representation of uncertainties, stochastic ground motion simulation methods are utilized to obtain the required input excitations. Moreover, to understand the uncertainty in ground motion simulation models, two broadband stochastic simulation models are used to generate input excitations representing six seismic hazard scenarios defined by earthquake magnitude, source-to-site distance, and soil conditions.Optimal intensity measure parameters for each scenario are identified using a systematic procedure that considers criteria such as efficiency, practicality, proficiency, sufficiency, and hazard compatibility. Fragility curves and surfaces are derived using the cloud analysis technique, taking into account various damage measures and limit state functions. The study finds that the derived fragility curves are particularly sensitive to the selection of earthquake scenarios, the choice of records, and the methods used to calculate fragility curves, with less sensitivity observed to different engineering demand parameters. Given this sensitivity, particularly to ground motion selection, the study highlights the necessity of incorporating both model-to-model variability (epistemic uncertainty) and record-to-record variability (aleatory uncertainty), alongside the established material and modeling uncertainties, in the probabilistic seismic assessment.

Surrogation and Bayesian updating of a finite element model of the Leaning Tower of Pisa

Numerical models play a crucial role in the study and understanding of cultural heritage structures, serving as valuable tools for predicting their behavior under diverse and prospective scenarios. They are however affected by various uncertainties, which impact can be mitigated through the calibration of model parameters. For heritage structures, where testing is usually restricted to the use of non-destructive techniques, and often unable to directly assess the inherent heterogeneity of the materials, a calibration approach can prove particularly useful to obtain a working model. This work applies a Bayesian model updating procedure to material-related uncertainties affecting a recently developed finite element model of the Leaning Tower of Pisa also comprising the underlying soil layers. A general Polynomial Chaos Expansion-based surrogation of the model was employed to evaluate the sensitivity of modal properties and ease the computational burden that comes with the probabilistic framing of the updating problem. Material property distributions were then updated, taking advantage of modal data from a newly installed sensor network which allowed the most up-to-date dynamic identification of the monument. The results represent the first probabilistic model-based assessment of material uncertainties in a three-dimensional finite element model of the Leaning Tower of Pisa. They shed some light into the value of specific modal information, while the use of analytical surrogation paves the way for the future design of a real-time updating procedure for monitoring and damage detection.

Numerical analysis of uncertain frequency of rotating composites using NURBS-based isogeometric HSDT approach: Rim-driven configuration

This study delves into the impact of material and fabrication uncertainties on the natural frequencies of rim-driven rotating structures. Utilizing a higher-order shear deformation theory (HSDT), an isogeometric analysis (IGA) model predicts these frequencies. Sensitivity analysis via a radial basis function network (RBFN) model, validated with Monte Carlo simulation, highlights parameter influence. Parametric investigations unveil sensitivity patterns and influential parameters. Employing a stochastic approach, the study compares the effects of random material properties on the free vibration response of rotating and non-rotating plates. Additionally, the RBFN-based surrogate model assesses reliability parameters concerning resonance failure in rim-driven rotating composite structures.

Investigating the Impact of Blunt Force Trauma: A Probabilistic Study of Behind Armor Blunt Trauma Risk.

Evaluating Behind Armor Blunt Trauma (BABT) is a critical step in preventing non-penetrating injuries in military personnel, which can result from the transfer of kinetic energy from projectiles impacting body armor. While the current NIJ Standard-0101.06 standard focuses on preventing excessive armor backface deformation, this standard does not account for the variability in impact location, thorax organ and tissue material properties, and injury thresholds in order to assess potential injury. To address this gap, Finite Element (FE) human body models (HBMs) have been employed to investigate variability in BABT impact conditions by recreating specific cases from survivor databases and generating injury risk curves. However, these deterministic analyses predominantly use models representing the 50th percentile male and do not investigate the uncertainty and variability inherent within the system, thus limiting the generalizability of investigating injury risk over a diverse military population. The DoD-funded I-PREDICT Future Naval Capability (FNC) introduces a probabilistic HBM, which considers uncertainty and variability in tissue material and failure properties, anthropometry, and external loading conditions. This study utilizes the I-PREDICT HBM for BABT simulations for three thoracic impact locations-liver, heart, and lower abdomen. A probabilistic analysis of tissue-level strains resulting from a BABT event is used to determine the probability of achieving a Military Combat Incapacitation Scale (MCIS) for organ-level injuries and the New Injury Severity Score (NISS) is employed for whole-body injury risk evaluations. Organ-level MCIS metrics show that impact at the heart can cause severe injuries to the heart and spleen, whereas impact to the liver can cause rib fractures and major lacerations in the liver. Impact at the lower abdomen can cause lacerations in the spleen. Simulation results indicate that, under current protection standards, the whole-body risk of injury varies between 6 and 98% based on impact location, with the impact at the heart being the most severe, followed by impact at the liver and the lower abdomen. These results suggest that the current body armor protection standards might result in severe injuries in specific locations, but no injuries in others.

Quantifying quanta: Determining emission rates from clinical data

It is important to quantify uncertainty in the viable genomic material encapsulated in the respiratory particles emitted by infected people so that it can be converted into an emission rate as a function of respiratory and metabolic activities and used to estimate the probability of infection for an indoor scenario. Clinical measurements of viral loads for SARS-CoV-2 made using infection surveys, Gesundheit-II samplers, and human challenge studies are evaluated and a mathematical model is derived to estimate the quantum emission rate as a function of the genomic and viable viral loads. Modelled emission rates for SARS-CoV-2 agree with clinical data above detection limits. The viral load is found to vary over at least 6 orders of magnitude because it is person and time dependent, and contingent on many other factors that are difficult to quantify. It is similarly large for other respiratory pathogens. Therefore, the genomic and viable-virion emission rates display similar heterogeneity. When emission rates are used to estimate absolute infection risk using the Wells-Riley model, the predictions are so uncertain that they cannot be used in any meaningful way to provide useful quantitative guidance for designing indoor spaces.

Multi‐objective reliability‐based robust design for a rock tunnel support system using Pareto optimality

AbstractIn the context of rock material and modeling uncertainties, the optimization of rock tunnel support systems is often conducted by selecting the most cost‐effective solution among several feasible options that typically rely on the engineer's experience, potentially leading to overlooking the most optimal design. To improve such a limitation, this paper presents a multi‐objective reliability‐based robust design, considering the cost, safety, and design robustness systematically while maintaining the computational efficiency. In this framework, the uncertainty‐based reliability constrains is performed using the first‐order reliability method (FORM) and an improved Hasofer–Lind–Rackwits–Fiessler recursive algorithm (iHLRF‐x). The design robustness, in terms of sensitivity index (SI), is evaluated using the normalized gradient of the system response to the noise factors, which can be efficiently obtained from the output of FORM analysis. Then, the Pareto front revealing the tradeoff between multiple objectives can be directly generated using the proposed optimization framework. To illustrate the effectiveness of this procedure, a set of the optimal design combinations of the shotcrete thickness and installation position for the exampled rock tunnel are obtained, and new perspectives pertaining the success of the reliability‐based robust designs are provided.

Surrogate models for seismic and pushover response prediction of steel special moment resisting frames

For structural engineers, existing surrogate models of buildings present challenges due to inadequate datasets, exclusion of significant input variables impacting nonlinear building response, and failure to consider uncertainties associated with input parameters. Moreover, there are no surrogate models for the prediction of both pushover and nonlinear time history analysis (NLTHA) outputs. To overcome these challenges, the present study proposes a novel framework for surrogate modelling of steel structures, considering crucial structural factors impacting engineering demand parameters (EDPs). The first phase involves the development of a process by which 30,000 random steel special moment resisting frames (SMRFs) for low to high-rise buildings are generated, considering the material and geometrical uncertainties embedded in the design of structures. In the second phase, a surrogate model is developed to predict the seismic EDPs of SMRFs when exposed to various earthquake levels. This is accomplished by leveraging the results obtained from phase one. Moreover, separate surrogate models are developed for the prediction of SMRFs’ essential pushover parameters. Various machine learning (ML) methods are examined, and the outcomes are presented as user-friendly GUI tools. The findings highlighted the substantial influence of pushover parameters as well as beams and columns’ plastic hinges properties on the prediction of NLTHA, factors that have been overlooked in prior studies. Moreover, CatBoost has been acknowledged as the superior ML technique for predicting both pushover and NLTHA parameters for all buildings. This framework offers engineers the ability to estimate building responses without the necessity of conducting NLTHA, pushover, or even modal analysis which is computationally intensive.

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.

Buckling behavior of rotor blade sandwich panels with spatially distributed material uncertainties

The study evaluates the impact of material uncertainties on the buckling behavior of sandwich panels in wind turbine rotor blades. The analysis is limited to linear buckling and is performed using stochastic finite element Monte Carlo simulation on a rectangular and flat submodel of the rotor blade’s trailing edge panel. The finite element model of the panels is simply supported on all edges. To generate the spatial material property distributions, the Karhunen-Loève expansion is used in combination with Latin hypercube sampling. The results compare the effects of various correlation lengths of the spatial distributions. The buckling loads vary in correlation to the average panel stiffness caused by the random distributions. The spatial distribution has a less dominant effect, reducing the mean value of the buckling load results. The amount of reduction in buckling load is highest when the correlation length of the distribution is close to the harmonic half-wave of the dominant buckling shape.

Probabilistic design and optimization of thermal protection system with variable thickness based on non-uniform aerodynamic heating

Thermal Protection System (TPS) is a critical component of space vehicles to protect them from damage by extreme aerothermal heating conditions. The uncertainty of the aerothermal heating conditions, including approaching velocities, atmospheric density, pressure, etc., significantly affects the aerothermal heating imposed on the TPS, vastly changing the initial design conditions. In this study, we developed a new uncertainty quantitative (UQ) analysis method to comprehensively consider all the uncertainty of TPS materials, structures, and aerothermal heating conditions. To address the challenges of a massive amount of input and determined optimization parameters, we first induced the response surface method and Monte Carlo algorithm to produce a large number of analysis samples for Probabilistic Design. Then, the key affecting factors, not all the input design parameters, were chosen for optimization with sequential optimization and reliability assessment (SORA) strategy by a sensitivity analysis. With such improvements, the optimization for UQ analysis becomes practical. We used this new UQ method to analyze the effects of uncertainty of the aerothermal heating conditions on the thermal protection layer. A variable thickness design is proposed to adapt the non-uniform aerothermal heating conditions for the TPS. The results show that significant light-weighting can be achieved without the reliability penalty. The optimized structure also reduces internal temperature gradients and facilitates a more uniform temperature field distribution for the TPS. This study provides a new UQ engineering design approach for the system with many designed parameters.

Robust topology and discrete fiber orientation optimization under principal material uncertainty

This paper introduces a formulation of the robust topology optimization problem that is tailored for designing fiber-reinforced composite structures with spatially varying principal mechanical properties. Specifically, a methodology is developed that incorporates the spatial variability in the engineering constants of the composite lamina into the concurrent topology (i.e., material distribution) and morphology (i.e., fiber orientation distribution) optimization problem for the minimization of the robust compliance function. The spatial variability in the mechanical properties of the lamina is modeled as a homogeneous random field within the design domain by means of the Karhunen-Loe´ve series expansion, and is thereafter intrusively propagated into the stochastic finite element analysis of the composite structure. To carry out the stochastic finite element analysis per iteration of the optimization cycle, the first-order perturbation method is utilized for approximating the current state variables of the physical system. The resulting robust topology and fiber orientation optimization problem is formulated step-by-step for the minimization of the robust compliance function. With the view of solving the optimization problem at hand by means of gradient-based solution algorithms, the first-order derivatives of the involved design functions w.r.t. the associated design variables are analytically derived. The present work concludes with a series of numerical examples, focusing on the benchmark academic case studies of the 2D cantilever and the half part of the Messerschmitt-Bölkow-Blohm beam, aiming to demonstrate the developed methodology as well as to explore the effect that different parameterization instances of the random field bear on the predicted topology and morphology of the beams.

Modification of the Reloading Plastic Modulus in Generalized Plasticity Models for Soil by Introducing a New Equation for the Memory Parameter in Cyclic Loadings

Nowadays, with the widespread supply of very powerful laboratory and computer equipment, it is expected that the analyses conducted for geotechnical problems are carried out with very high precision. Precise analyses lead to better knowledge of structures’ behavior, which, in turn, reduces the costs related to uncertainty of materials’ behavior. A precise analysis necessitates a precise knowledge and definition of the behavior of the constituent materials, which itself requires applying an appropriate constitutive model to show the behavior of materials. Constitutive models used in the generalized plasticity framework are very powerful constitutive models for the simulation of sand behavior. However, the simulation of a cyclic behavior in these models, especially the simulation of the undrained cyclic behavior, is not well-recognized. In this study, in order to eliminate the weakness of generalized constitutive models under cyclic loading, a new equation is presented to substitute the so-called coefficient of the discrete memory factor to consider the loading history in such a way that the plastic modulus is modified during reloading and, as a result, more appropriate predictions of sand behavior are obtained. The performance accuracy of the proposed coefficient was evaluated in accordance with the experimental data. Finally, the results show that after using the modification of the loading history coefficient, predictions of the constitutive model are significantly improved.

Robust topology optimization for multi-material structures considering material uncertainties

Multi-material structures have attracted more and more attention from scholars and engineers, because they usually have good composite performance. Uncertainty factors are ubiquitous in practice, especially for material uncertainties. Multi-material structures contain many materials with different properties, material uncertainties will affect the allocation of material usage, thereby affecting structural performance. Therefore, it is crucial to consider material uncertainties in the design of multi-material structures. This paper detailedly explores the impacts of elastic modulus uncertainty and Poisson's ratio uncertainty on the design of multi-material structures. The topology optimization is to minimize the compliance subjected to a mass constraint, where multiple materials can be freely allocated during the optimization. Floating projection topology optimization is adopted to search for the structural topologies, and robust sensitivity information is derived. For the quantification of material uncertainties, this paper introduces a non-intrusive polynomial chaos expansion (PCE) method to implicitly quantify them, the computational efficiency and accuracy in PCE is compared with the classical Monte Carlo method. Finally, topology optimization examples are provided to demonstrate the effects of elastic modulus uncertainty, Poisson's ratio uncertainty and hybrid uncertainty on the design of multi-material structures.