Uncertainties In Structural Parameters Research Articles

Efficient strategy for topology optimization of stochastic viscoelastic damping structures

In this study, the dynamic topology optimization (TO) of stochastic viscoelastic damping structures (VDSs) is performed for the first time. However, the expensive computation cost seriously hinders the TO process. To address this problem, an efficient strategy is proposed. On the one hand, in the stochastic structural response analysis, a fully adaptive method based on direct probability integral method is presented to determine the number and locations of samples. Meanwhile, to improve the computational efficiency of structural response for each sample, a piecewise model-order reduction method based on Krylov subspace is adopted to generate the orthonormal basis and project the original large-scale system onto a low-order system. On the other hand, to overcome the optimization challenge arising from large number of design variables in the density-based topology method, a material-field series-expansion method is employed to describe the topology layout and significantly reduce the number of design variables. Moreover, the sensitivity of the optimization model is derived by the adjoint method and the method of moving asymptotes (MMA) is used to efficiently update the design variables. Some numerical examples comprehensively demonstrate the effectiveness and efficiency of the proposed strategy. The results indicate that the uncertainty of structural parameters greatly affect both the topology layout of VDS and vibration damping capacity.

Response analysis of asymmetric monostable energy harvester with an uncertain parameter

The nonlinear piezoelectric vibration energy harvesting system offers a solution to the power supply challenge for low-power sensors. However, In practical engineering, structural parameter uncertainties arise from manufacturing errors, environmental temperature fluctuations, and changes in material properties. Besides, due to inherent flaws in the structure and materials, asymmetry may occur in the potential well, which leads to the complexity of the dynamic behavior exhibited in energy harvesting system. In this paper, in order to investigate the dynamics of the asymmetric monostable piezoelectric energy harvesting system under an uncertain parameter, the orthogonal polynomial approximation method is applied to transform the stochastic system into an equivalent deterministic system. Then, the responses of the equivalent deterministic system are used to reveal the stochastic behavior of the uncertainty system. The results indicate that, when the uncertainty intensity of the cubic nonlinear coefficient is 0, an increase in the asymmetry level of the potential well leads to an increase in the output voltage. Increasing the other parameters, the output voltage is reduced. In addition, as the uncertainty intensity increases, the parameter intervals of chaotic response become wider and the energy harvesting efficiency decreases. Moreover, as the potential well asymmetry increases, the sensitivity of energy harvesting system to uncertain parameter increases.

Mechanical analysis for deepwater drilling riser system with structural parameters uncertainty

Uncertain structural parameters increase the mechanical failure risk of the drilling riser system. The effect of uncertain structural parameters on the mechanical characteristics of the drilling riser system is unclear. Therefore, the mechanical characteristics of the drilling riser system with structural parameter uncertainty are analyzed. A mechanical model with uncertain structural parameters is established based on the basic analysis model and the local average theory. The basic analysis model takes into account the combined effects of platform motion, tensioner, riser string, and marine environment. The local average theory is suitable for situations where there is a lack of uncertainty information. A solution approach is proposed using the second-order central perturbation method and the Newmark-β method. The solution method reduces the load by simplifying degrees of freedom and random variables. The correctness of the solution method is verified using the Monte Carlo method. The analysis results indicate that uncertain structural parameters significantly affect the static response, while the dynamic response is less affected. The failure risk due to static axial stress and static shear stress is heightened by uncertain structural parameters. The failure risk arising from static shear stress at the bottom of the riser system experiences a significant increase.

Improved hierarchical Bayesian modeling framework with arbitrary polynomial chaos for probabilistic model updating

Bayesian finite element model updating techniques have found widespread application in the structural health monitoring. The conventional Bayesian modeling framework (CBMF) identifies the posterior distribution of structural parameters using data from a single experiment. However, structural parameters exhibit variability due to changes in environmental or experimental conditions, an aspect overlooked by CBMF. In recent years, the hierarchical Bayesian modeling framework (HBMF) has gradually attracted attention. This framework incorporates an additional layer of hyperparameters and utilizes multiple sets of experimental data to describe the variability in structural parameters caused by changes in experimental conditions. Unfortunately, the increase in hyperparameters and data size leads the HBMF to face the dilemma of exceedingly high computational costs. To address this issue, this study proposes an improved hierarchical Bayesian modeling framework (IHBMF). To enhance the computational efficiency of IHBMF, the importance sampling method is employed to simplify the computation of the likelihood function for hyperparameters. Subsequently, a surrogate model based on arbitrary polynomial chaos expansion is introduced to further reduce the computational cost of IHBMF. The framework's accuracy and efficiency were validated through analysis of a simply supported beam and a steel pedestrian bridge. The results demonstrate that IHBMF not only delivers a more accurate quantification of structural parameter uncertainty than CBMF but also exhibits higher computational efficiency compared to HBMF, showcasing its superior capability in structural health monitoring applications.

Multi-factors effects analysis of nonlinear vibration of FG-GNPRC membrane using machine learning

Functionally graded graphene nanoplatelet reinforced composite (FG-GNPRC) have exhibited significant potential for the development of high-performance and multifunctional structures. In this paper, we present a machine learning (ML) assisted uncertainty analysis of nonlinear vibration of FG-GNPRC membranes under the influence of multi-factor coupling. Effective medium theory (EMT), Mori-Tanaka (MT) model and rule of mixture are utilized to evaluate the effective material properties of the composite membrane. Governing equations are derived via an energy method with the frameworks of the hyperelastic membrane theory, Neo-Hookean constitutive model and the couple dielectric theory. Randomly generated inputs after data pre-processing are fed into governing equations, which are solved by numerical methods for outputs. Three ML models, including artificial neural network (ANN), support vector regression (SVR) and AutoGluon-Tabular (AGT), are adopted to capture the complex relationship between the systematic inputs (i.e., structural dimensions, attributes of GNPs and pores, external electric field, etc.), frequency ratio and dimensionless amplitude of FG-GNPRC membranes. The results demonstrate that all three ML models demonstrate exceptional computational efficiency, and AGT presents higher prediction accuracy compared to the other two models. Based on the Shapley additive explanations (SHAP) approach, the effects of uncertainties of system parameters and multi-factor coupling on the nonlinear vibration of the FG-GNPRC membrane are analyzed. It is found that the uncertainty of structural parameters has the greatest impact on the nonlinear vibration of FG-GNPRC membranes, particularly when the membrane is subjected to a voltage of 10 V and smaller stretching ratio.

Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.

Introduction: Several signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between endothelial cells under normoxia and hypoxia and their response to different stimuli can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. Methods: We calibrate and fit the model parameters based on well-established modeling techniques that include structural and practical parameter identifiability, uncertainty quantification, and global sensitivity. Results: Our results indicate that the main pathways involved in patterning tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia interferes with how different stimuli affect patterning. Additionally, our simulations indicate that Notch signaling might regulate vascular permeability and establish different Nitric Oxide release patterns for tip/stalk cells. Following simulations with various stimuli, our model suggests that factors such as time under hypoxia and oxygen availability must be considered for EC pattern control. Discussion: This project provides insights into the signaling and patterning of endothelial cells under various oxygen levels and stimulation by VEGFA and is our first integrative approach toward achieving EC control as a method for improving angiogenesis. Overall, our model provides a computational framework that can be built on to test angiogenesis-related therapies by modulation of different pathways, such as the Notch pathway.

Seismic resilience assessment of extended pile shaft supported coastal bridges considering scour and uniform corrosion effects

The resilience-informed assessment for coastal highway bridges exposed to multiple hazards plays a crucial role in the decision-making process for their operation, maintenance, and management during the bridge’s long-term service period. This study performs a seismic resilience assessment for a coastal reinforced concrete (RC) bridge under the combined effects of scour and uniform corrosion. Toward this goal, an extended pile shaft supported coastal RC bridge is taken as the benchmark and fifteen scenarios are generated to cover three regular scour conditions and five different corrosion levels. To inform the bridge management, the probabilistic distribution of the bridge service time corresponding to different corrosion levels is developed. Subsequently, the finite element model (FEM) for each scenario of the benchmark coastal highway RC bridge is developed, involving the soil-pile interaction and considering the uncertainty of structural and geotechnical parameters, as well as earthquake ground motions. The failure mode of the corroded extended pile shaft is identified as a flexural failure by comparing their flexural and shear capacities. Finally, a resilience loss ratio is proposed in this study for the first time to quantify the impacts of corrosion level and scour depth changes on seismic resilience across different ground motion intensities. Results indicate that both the seismic fragility and resilience decrease as the corrosion intensifies over time. Specifically, at a given intensity measure (IM) level, the resilience loss ratio increases as the corrosion level intensifies. For the studied benchmark bridge, the scour significantly increases the structural fundamental period, consequently reducing the curvature demand on the pile shaft. As a result, the scour generally decreases the failure probability and the earthquake-induced functionality loss for the extended pile shaft. As scour depth increases, the resilience loss of the bridge at a given corrosion level decreases.

Machine learning aided uncertainty analysis on nonlinear vibration of cracked FG-GNPRC dielectric beam

Functionally graded graphene nanoplatelet reinforced composite (FG-GNPRC) have shown significant potential for the development of high-performance and multifunctional materials and structures. Conducting an accurate analysis of the nonlinear vibration of FG-GNPRC dielectric beams can accelerate their application in various engineering fields. To investigate the nonlinear vibration of FG-GNPRC dielectric beams, the effective material properties of a cracked composite beams are evaluated using effective medium theory (EMT) and the rule of mixture. Three ML models adopted in this work, i.e., artificial neural network (ANN), random forest (RF), and AutoGluon (AG), are used to capture the complex relationship between the systematic inputs (i.e., parameters for materials and structure, the attributes of electrical field, etc.) and frequency ratio of the cracked FG-GNPRC beams. Moreover, shapley additive explanations (SHAP) is employed for the analysis of the nonlinear vibration of FG-GNPRC dielectric beams considering the influence of multi-factor coupling and uncertainties. The SHAP analysis suggests that the uncertainty of structural parameters has the greatest impact on the nonlinear vibration of FG-GNPRC dielectric beams, particularly when the structure is subjected to a voltage of 40 V.

Uncertainty of digital fringe projection measurement caused by structural parameters

Digital fringe projection (DFP) has been widely applied in three-dimensional (3D) precision measurement owing to its advantages of non-contact, high-precision, and large-field measurement. The DFP measurement system undergoes a calibration process to define the parameters that reconstruct the surface points from the camera images. As the DFP system is nonlinear, evaluating the measurement uncertainty caused by the calibration deviation of the structural parameters is a great challenge. In this study, we performed an uncertainty analysis based on the binocular vision model of a DFP system. A virtual DFP system based on the structural parameters of the actual system was built to remove the influence of other uncertainty factors. The adaptive Monte Carlo method (AMCM) was used to evaluate the uncertainty caused by the deviation of the parameters during calibration. The model and method for calculating the uncertainty of the DFP system proposed in this study are helpful in better understanding the propagation of the uncertainty of structural parameters, determining the critical factors affecting the measurement uncertainty, and providing a theoretical basis for subsequent error compensation and accuracy improvement for DFP measurement systems.

Angle resolved x-ray photoelectron spectroscopy assessment of the structure and composition of nanofilms—including uncertainties—through the multilayer model

The multilayer model (MLM) for assessing the structural and composition parameters of multilayered nanofilms from angle-resolved x-ray photoelectric spectroscopy is described in detail. It is compared with regularized back-transform (RBT) approaches such as the maximum entropy method (MEM) with Tikhonov-type regularizations. The advantages of MLM over MEM, such as the possibility of assessing confidence ranges, modeling structures beyond conformal multilayered nanofilms, and modeling abrupt interfaces, are discussed and exemplified. In contrast with MLM, the RBT methods have shortcomings such as the violation of the conservation of information and the inability to adequately address the dependence of the effective attenuation length on the material. Examples of the application of MLM to conformal films and systems with protrusions are shown. The covariance matrix method (CMM) is described and applied to assess uncertainties in structural parameters and composition under the MLM. The CMM constitutes the canonical method for assessing confidence ranges and adequately accounts for the covariance among structural (e.g., layer thicknesses) and composition parameters.

Uncertainty Inverse Analysis of Positioning Accuracy for Error Sources Identification of Industrial Robots

Uncertainties widely exist in modeling parameters of industrial robots due to wear during service. These uncertainties are too small to be directly measured and may severely affect the positioning accuracy of end-effector through multi-stage propagation. An uncertainty inverse analysis method of positioning accuracy is proposed to identify uncertainty of typical structural parameters with the selected optimal position information. The Denavit–Hartenberg method is employed to establish the kinematic model. For the probability uncertainty of modeling parameters, the inverse analysis model, which reflects propagation mapping between uncertain variables and end-effector position response is obtained by first-order second-moment method. The partial-derivative information is used to improve calculation efficiency through removing low-contributing parameters. To further reduce measurement noise interference, orthogonal matching pursuit strategy is presented to search optimal sensor placement with the objectives of maximum coefficient modulus and minimum correlation, then pseudoinverse matrix method is employed to obtain the exact solution of identification equation. Finally, numerical simulation results for 6 degrees-of-freedom robots indicate that the proposed method with improved solution efficiency can accurately identify uncertainties of joint angles within the error 5% under large measurement errors.

Sliding Mode Controller Based on Genetic Algorithm and Simulated Annealing for Assured Crew Reentry Vehicle

A sliding mode (SM) re-entry control system is proposed for an assured crew re-entry vehicle (ACRV). The vehicle has a low lift/drag ratio (0.3). The trajectory controller drives the ACRV in a predefined track. The reference vectors for the attitude controller are generated by the trajectory controller and navigation system. The feedback SM controller is designed to accommodate the structural parametric uncertainties and environmental disturbances. The SM controller is optimized by both the genetic algorithm (GA) and the simulated annealing (SA) methods. A comparative study is done between both optimization methods and the efficiency of the SA method is proved. The system is subjected to a sensitivity study in terms of various thrust torque profiles. A robustness check is done by changing the system parameters. A pulse width pulse frequency (PWPF) modulator modulates the output torque dictated by the attitude controller. The simulation results demonstrate the proposed method’s effectiveness in achieving the various required parameters.

A two-stage uncertainty quantification framework for reliability and sensitivity analysis of structures using the probability density evolution method integrated with the Fréchet-derivative-based method

In this work, a novel Fréchet-derivative-based global sensitivity analysis is incorporated into the probability density evolution method for the simultaneous reliability and sensitivity analysis of structures. The two methods are synthesized and realized under a two-stage uncertainty quantification framework. Fundamental and numerical algorithms of the method are described, which is validated by a benchmark problem with theoretical solutions. Then it is applied on studying stochastic seismic responses of a real-world reinforced concrete structure of 288 m high. Results demonstrate that the response characteristics of the structure will be greatly changed when both uncertainties from the ground motions and structural parameters are considered, e.g., the failure probability will be enlarged by 1∼2 orders of magnitude. Besides, contrary to the popular opinion that the uncertainty of structural parameters is trivial compared to that of parameters in the ground-motion model, the importance measures of the structural parameters can be greater than those of the parameters in the ground-motion model by a factor of 2. when the coupling effect of randomness and nonlinearity of structural materials becomes dramatic. Some issues to be further outlined are also discussed.

Coupling dynamic model of vehicle-wheel-ground for all-terrain distributed driving unmanned ground vehicle

The establishment of a dynamic model of distributed driving unmanned ground vehicle (DDUGV) is the premise of vehicle state identification and high-quality control. However, due to the strong nonlinearity of the DDUGV system itself, the various external influence and the difficulties of real-time calculating, the simulation results based on existing conditions are far from the actual vehicle test results, resulting in the huge gap between the simulation test and the actual engineering practice. Regarding the problem mentioned above, this paper takes the 3-axles DDUGV with variable structure as the research object, and proposes a universal dynamic modeling method for DDUGV, which considers the uncertainty of terrain and variable structural parameters. Firstly, based on Newtonian mechanics, mathematical models of the vehicle's variable structure, body posture, static/dynamic suspension, hydraulic transmission, wheel and in-wheel motors are established. Secondly, taking the wheel as the coupling node between vehicle dynamics and ground mechanics, 3 tire models in typical driving environments are established, which are the magic tire model used in paved road, and the Bekker-Wong-Janosi theoretical model used in sandy and clay soil, respectively. Thirdly, based on the established mathematical model, a universal DDUGV modeling method is proposed. Then, based on the co-calibration of geotechnics and discrete element method (DEM), the particle parameters of sandy soil are obtained and the theoretical mathematical model of wheel-soil interaction is verified and optimized. Finally, based on the established vehicle-wheel-ground coupling model of the 3-axles DDUGV with variable structures, simulation tests are carried out under typical working conditions, which are compared with the results of existing vehicle dynamics software to verify the accuracy, computation efficiency and real-time property. The vehicle-wheel-ground coupling dynamic model established in this paper can provide a reference for the construction of the vehicle dynamic models, the analysis of driving characteristics, and the research of control strategies in complex environment.

Extension of Computational Co-Design Methods for Modular, Prefabricated Composite Building Components Using Bio-Based Material Systems

Robotic coreless filament winding using alternative material systems based on natural fibers and bio-based resin systems offers possible solutions to the productivity and sustainability challenges of the building and construction sector. Their application in modular, prefabricated structures allows for material-efficient and fast production under tightly controlled conditions leading to high-quality building parts with minimal production waste. Plant fibers made of flax or hemp have high stiffness and strength values and their production consumes less non-renewable energy than glass or carbon fibers. However, the introduction of natural material systems increases uncertainties in structural performance and fabrication parameters. The development process of coreless wound composite parts must thus be approached from the bottom up, treating the material system as an integral part of design and evaluation. Existing design and fabrication methods, as well as equipment, are adjusted to emphasize material aspects throughout the development, increasing the importance of material characterization and scalability evaluation. The reciprocity of material characterization and the fabrication process is highlighted and contributes to a non-linear, cyclical workflow. The implementation of extensions and adaptations are showcased in the development of the livMatS pavilion, a first attempt at coreless filament winding using natural material systems in architecture.

Optimal intensity measure selection and probabilistic seismic demand model of pile group supported bridges in sandy soil considering variable scour effects

Scour of pile group foundations is a common hazard for cross-river bridges and can produce considerable damage to piles in earthquake-prone regions. The fragility-based performance assessment for pile group supported bridges subjected to earthquake and scour hazards has drawn increasing attention from engineering practice and academic communities. Such assessment requires numerical models that are computationally efficient and accurate in describing the mechanical behavior associated with the complex soil-pile interaction of these soil-bridge systems. Additionally, selecting an optimal ground motion intensity measure (IM) is generally required for such assessments. However, modeling the pile group effect for seismically excited pile groups and the optimal IM for the scoured bridges have not been well addressed in previous related studies. To this end, a practical pile group effect modeling approach is adopted in this study and validated by a set of shake table tests on scoured pile group bridge available in the literature. Based on the validated numerical modeling approach for the soil pile interaction, a series of coupled soil-bridge models are generated considering the structural and soil parameter uncertainty and analyzed in the OpenSees platform. The optimal IM for various engineering demand parameters (EDPs) is identified from thirty-nine scalar IM candidates through the criteria of efficiency, practicality, sufficiency, and hazard computability. Finally, a straightforward form of probabilistic seismic demand model (PSDM), incorporating the effect of scour depth changes, is developed for pile group supported bridges. Results show that the increase of scour depth significantly increases the curvature demand of a pile group and statistically reduces the embedded depth of the belowground plastic hinge of the pile shaft. The rank of optimal IM candidates directly depends on the selection criteria of an optimal IM and the selection of EDP. In addition, the scour depth slightly affects the rank of optimal IMs. Generally, VSI, PGV, and Sv,1.0 are recommended for the probabilistic seismic demand analysis of pile group supported bridges in sandy soils with scour potentials.

Fuzzy-logic framework for updating the seismic fragility of deteriorating bridges via visual inspections

Seismic evaluations of bridges are critical to ensure the post-earthquake integrity of transportation networks; however, systematic evaluations are costly. Hence, bridge management systems have developed screening policies to identify the most seismically vulnerable bridges. Nevertheless, such policies are typically qualitative and may not easily capture the impact of bridge aging or the uncertainty in material and structural parameters, leading to potentially underrated vulnerability estimates. Thus, a new quantitative-yet-practical framework is proposed to screen bridges given the fragilities of deteriorating critical components, namely bearings and columns. The framework uses fuzzy-logic to integrate deterioration data from routine visual inspections into fragility analysis. Then, parameterized fuzzy-fragility models are developed for columns and bearings given their deterioration level, bridge design parameters, and seismic intensity. These models are utilized to develop a seismic vulnerability ranking system to set bridge priorities for detailed seismic investigation. The analysis revealed that bridge vulnerability ranking could significantly increase with deterioration.

Probabilistic Seismic Sensitivity Analyses of High-Speed Railway Extradosed Cable-Stayed Bridges

It is known that the extradosed cable-stayed bridge, a hybrid bridge, possesses the virtues of both classic cable-stayed bridges and girder bridges in mechanical behaviors. In this paper, the sensitivity of seismic fragility demand parameters (SFDP) of a high-speed railway extradosed cable-stayed bridge is studied systematically along with the consideration of structural parameter uncertainty. Based on the probability distribution and correlation of random parameters, the Latin hypercube sampling method is adopted herein. The dynamic 3D finite element model of the employed bridge is established by using powerful and attractive OpenSEES nonlinear software. A nonlinear incremental dynamic analysis is performed to consider the randomness of structural parameters using sampling analysis. Some important conclusions are drawn indicating that the structural design parameter uncertainty predominantly has influence on the SFDP for fragility analysis of bridge structures. The design parameters of extradosed cable-stayed bridges are categorized and identified as primary, secondary and insensitive parameters. The high sensitivity parameters of extradosed cable-stayed bridges for fragility analysis include friction coefficient of bearing, concrete bulk density, damping ratio, peak compressive strength of confined concrete, component size and peak strain of confined concrete. Additionally, the strength and strain of unconfined concrete cannot be ignored. Furthermore, the uncertainty of structural design parameters fails to be responsible for the cable force responses due to larger girder stiffness. The structural design parameter uncertainty has a significant influence on the responses of extradosed cable-stayed bridges for seismic fragility analysis.

Uncertainty qualification in evaluating dynamic and static stress intensity factors using SBFEM based on model order reduction

This paper presents a novel framework for Monte Carlo simulation (MCs) of dynamic and static fracture problems. The stress intensity factors can be directly extracted by using polygon scaled boundary finite element method (SBFEM) to analyze dynamic and static fracture problems. Uncertainty qualification analysis focuses on the output uncertainty caused by the uncertainty of system input. MCs is considered as a general tool to solve complex multi-dimensional uncertainty problems because of its simplicity and directness. However, its applicability is hindered due to the huge calculating cost. The use of singular value decomposition (SVD) and radial basis function (RBF) can reduce the computational cost, improve the computational efficiency, and realize the rapid uncertainty qualification analysis based on MCs. Considering the influence of uncertainty of structural material property parameters and structural shape parameters on the calculation results, MCs is used to analyze the statistical characteristics of structural response under random variables. Finally, several examples are given to verify the correctness and effectiveness of the algorithm.

Semi-active fuzzy cooperative control of vehicle suspension with a magnetorheological damper

This study attempts to improve the vibration isolation performance of a vehicle suspension system with a magnetorheological damper (MRD) under complex driving conditions. Structure parameter uncertainty, disturbance of the driving process, and response time delay of MRD are all addressed. Firstly, experiments of MRD were carried out in a damping force testing machine to identify the parameters of the MRD adjustable Sigmoid model by the Levenberg-Marquardt optimization algorithm. Then, the parameter identification is verified by comparing experimental and simulation data. Secondly, the state space equations of the suspension system are derived by Newton’s second law. The transfer function from the bounded disturbance input to the control output is obtained based on H∞ control theory. To make the Infinite norm of the system transfer function less than a certain value, three control strategies are proposed: variable structure control (VSC), disturbance rejection control (DRC), and delay tolerance control (DTC). Thirdly, considering these issues together to weaken the effect of disturbances on vehicle driving conditions, a fuzzy cooperative control (FCC) strategy is proposed based on the linear matrix inequality (LMI) theory. Simulation results demonstrate that FCC semi-active vehicle suspension systems conduct effective vibration isolation performance while responding to multiple external disturbances.