Uncertainties In Structural Parameters Research Articles

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.

Robust Control of Structures with Active Tendon and Fuzzy-LQR Algorithm Considering Uncertainties of Structural Parameters

Robust Control of Structures with Active Tendon and Fuzzy-LQR Algorithm Considering Uncertainties of Structural Parameters

Reliability evaluation method of vibration isolation performance of nonlinear isolator

Nonlinear isolators are widely used to reduce harmful vibrations due to their broad isolation bandwidth. The vibration isolation performance of nonlinear isolators can be quantitatively evaluated by the transmitted force or displacement. However, uncertainties exist in the excitation and structural factors of nonlinear isolators, which cause the dispersion of displacement and force transmissibility, and affect the vibration isolation performance of nonlinear isolators. In this paper, a reliability evaluation method of vibration isolation performance of nonlinear isolator is proposed by considering the influence of the uncertainties. First, quasi-zero stiffness characteristics of the nonlinear isolator is analyzed and a dynamic model is established to study the isolation performance of the nonlinear isolator. Then, the types, sources, and influence degrees of the uncertainty of dimensional, structural, and excitation parameters on the nonlinear isolator are studied, and sensitive parameters are extracted. Furthermore, the reliability model of the nonlinear isolator under the action of sensitive parameters is proposed by considering the amplitude of the steady-state displacement response of the nonlinear isolator as the evaluation index of vibration isolation performance. Finally, the reliability of the nonlinear isolator is evaluated using an advanced surrogate model method based on stochastic configuration network. An experimental platform is built to verify the validity of the reliability evaluation method. The proposed method can provide potential guidance for the reliability evaluation of the isolation performance of complex nonlinear isolators.

Reliability analysis of complex structures based on Bayesian inference

This paper outlines a logical process based on Bayesian inference to exploit monitoring observations to improve the accuracy in the reliability estimates of civil structures that are retrofitted during their service lives. The proposed approach is particularly effective for complex structures, whose response prediction is affected by structural parameters with significant uncertainty; typical examples are prestressed concrete bridges, heavily influenced by the inherent uncertainties of the material’s creep and shrinkage. The methodology is applied to a real-life complex bridge in Italy, as opposed to academic problems and laboratory tests; the structural model developed for this study includes the original creep law of the Model B3 proposed by Bazant rather than linear approximation or response surfaces. The Bayesian inference is performed using a Markov Chain Monte Carlo (MCMC) method, while the reliability assessment utilises importance sampling (IS) for computational efficiency. The results show the extent to which structural health monitoring (SHM) and Bayesian inference reduce the uncertainty in structural parameters and the prediction of the structural demand. At the same time, they increase structural reliability. Particular attention is given to computational effort, structural modelling and algorithm optimisation.

Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties. First, considering the uncertainty of structural and aerodynamic parameters, the six-degree-of-freedom (6DoF) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established, respectively, in the inertial and body-fixed reference frames. Next, a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6DoF dynamics. Then, a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix. As long as the finitely excited condition is satisfied, the precise identification of unknown parameters can be achieved. Finally, the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

Laser-induced fluorescence spectroscopy for kinetic temperature measurement of xenon neutrals and ions in the discharge chamber of a radiofrequency ion source

Application of single-photon absorption laser-induced fluorescence (LIF) spectroscopy for non-intrusive measurement of neutral xenon and singly charged xenon ion kinetic temperatures in the discharge chamber of a gridded radiofrequency ion source is demonstrated. A LIF spectrum analysis approach including hyperfine structure reconstruction and inverse filtering (Fourier deconvolution) is outlined. Special focus is set on optimization of post-deconvolution filtering as well as retracing of deconvolution result imperfection due to hyperfine structure parameter uncertainty, incorrect natural linewidth, and saturation of the LIF signal. The corresponding contributions to the kinetic temperature estimation error are quantified via simulation of spectral lineshapes. Deconvolution of almost unsaturated LIF spectra recorded in the center of the ion source discharge chamber reveals that the neutral xenon and xenon ion kinetic temperatures range between approximately 500 and 700 K and, respectively, 700 and 1000 K depending on the radiofrequency power supplied to the discharge.

Uncertain Dynamic Characteristic Analysis for Structures with Spatially Dependent Random System Parameters.

This work presents a robust non-deterministic free vibration analysis for engineering structures with random field parameters in the frame of stochastic finite element method. For this, considering the randomness and spatial correlation of structural physical parameters, a parameter setting model based on random field theory is proposed to represent the random uncertainty of parameters, and the stochastic dynamic characteristics of different structural systems are then analyzed by incorporating the presented parameter setting model with finite element method. First, Gauss random field theory is used to describe the uncertainty of structural material parameters, the random parameters are then characterized as the standard deviation and correlation length of the random field, and the random field parameters are then discretized with the Karhunen-Loeve expansion method. Moreover, based on the discretized random parameters and finite element method, structural dynamic characteristics analysis is addressed, and the probability distribution density function of the random natural frequency is estimated based on multi-dimensional kernel density estimation method. Finally, the random field parameters of the structures are quantified by using the maximum likelihood estimation method to verify the effectiveness of the proposed method and the applicability of the constructed model. The results indicate that (1) for the perspective of maximum likelihood estimation, the parameter setting at the maximum value point is highly similar to the input parameters; (2) the random field considering more parameters reflects a more realistic structure.

Influence of uncertainty of structural parameters on the stochastic dynamic response of asphaltic lining dam-foundation systems to non-stationary random seismic excitation

The present work aims toward the effect of uncertain structural parameters on the stochastic dynamic response of an asphaltic lining dam-foundation system subjected to stationary as well as non-stationary random excitation. Uncertain structural parameters of interest are shear modulus and mass density, modeled using the lognormal distribution. The stochastic seismic response of the dam-foundation system to the random loads with uncertain structural parameters is carried out with the Monte Carlo simulation method. The spatial variability of ground motion is considered with incoherence and wave passage effects as stationary as well as non-stationary random excitation. A time-dependent frequency response function is used throughout the study for non-stationary responses. Obtained results indicate that the variability of shear modulus and mass density can be neglected in stochastic dynamic analysis of an asphaltic lining dam-foundation system. Also, it is seen from the results that stationarity is a reasonable assumption for asphaltic lining dams to typical durations of strong shaking.

A direct solution method for generalized stochastic eigenvalue problems of structures with random parameters

AbstractIn this paper, a direct solution method is proposed for solving generalized stochastic eigenvalue problems of structures with random uncertainties in structural parameters. Taking random parameters into consideration, predicting the statistical characteristics of the eigenvalues of structures within a probabilistic framework precisely is significant for structural analysis and design. However, the most typical method, the Monte‐Carlo Simulation is highly time consuming. And most of the existing methods with high computational efficiency compared to the Monte‐Carlo Simulation are approximate methods and may not be applicable for high‐precision requirements. Within the proposed method, the solution equations are deduced in terms of the properties of matrix operations step by step based on the Kronecker product and straightening operation, and thus the mean values and variances of the eigenvalues can be obtained directly with high precision. Three numerical examples are employed to illustrate the feasibility and applicability of the proposed method in specific engineering structures. The results indicate that the proposed method yields consistent mean values and variances of the eigenvalues with those obtained by the Monte‐Carlo Simulation respectively with significantly less computation time showing its superiority in accuracy and efficiency.

Simultaneous layout and size optimization of nonlinear viscous dampers for frame buildings under stochastic seismic excitation

Viscous dampers, as effective energy-dissipation devices, have been widely used for seismic mitigation of building structures. Due to the intrinsic uncertainties of structural parameters and earthquake ground motions, to obtain an optimal performance of structural control, the stochastic design optimization of viscous dampers for buildings is essential. This paper establishes a new dynamic reliability-based optimization framework to address the simultaneous layout and size design of nonlinear viscous dampers in frame buildings, considering the dual randomness of both nonstationary seismic excitations and uncertain structural model parameters. Firstly, a mixed integer optimization problem for nonlinear viscous dampers, including discrete and continuous design variables, is formulated, and the design target is to minimize the cost of the dampers subjected to performance constraints on dynamic reliability of building structures. Then, sequential approximate mixed integer programming with trust region is proposed to solve the optimization problem. Moreover, direct probability integral method is suggested to assess the dynamic reliability and its sensitivity with respect to design variables. Finally, accounting for both stochastic nonstationary seismic excitations and random structural parameters, the optimized results of two numerical examples indicate that the proposed method is a competitive choice for realizing the layout and size optimization of limiting types of nonlinear viscous dampers in frame buildings. To ensure the same target performance of buildings in terms of the probability of exceeding the target value of inter-story drift, the larger peak ground acceleration of stochastic ground motions is, the more cost of dampers is required for seismic protection of buildings.

Probability-based residual displacement estimation of unbonded laminated rubber bearing supported highway bridges retrofitted with Transverse Steel Damper

Predicting the post-earthquake residual displacement of isolated bridges is crucial in deciding their operational and recovery strategies after a seismic event. This study proposes a probability-based approach to estimate the residual displacement of a trilinear hysteretic system representing the unbonded laminated rubber bearing (ULRB) supported highway bridge retrofitted with transverse steel dampers (TSDs), considering the uncertainty of structural parameters and the variability of ground motions. To this end, this study systematically derives the quasi-static residual displacement of a trilinear hysteretic system, and presents the statistical distribution model for the related structural parameters, especially for the pier height and the TSD model parameters. After that, the dynamic residual displacement property of the ULRB supported highway bridge retrofitted with TSDs is investigated considering the uncertainty of various parameters, including model parameters of TSD and ULRB, the mass of superstructure, the height of piers, as well as the ground motions with and without pulse. The maximum possible residual displacement of the trilinear hysteretic system is then related to its quasi-static residual displacement and peak displacement demand. Finally, an innovative probability-based approach is developed in this study to estimate the conditional probability of the dynamic residual displacement of the trilinear system exceeding a threshold at the given ground motion intensity, and this innovative approach is further validated by comparing it with the incremental dynamic analysis (IDA) method. Results show that the dynamic residual displacement of the TSD retrofitted ULRB supported highway bridge system is random, and the ratio between the dynamic residual displacement and the maximum possible residual displacement approximately satisfies a uniform distribution bounded by zero and one. The probability-based approach can obtain a close result with the IDA method and significantly reduce computational efforts. This research represents a first step toward developing a rapid post-earthquake assessment approach for seismically isolated bridges and the seismic design of these bridges considering the residual displacement.

Quantifying the influence of modeling uncertainties on performance evaluation of mega column-core tube-outrigger structure under near-field and far-field ground motions

Different types of ground motions have the potential to aggravate the failure state of mega column-core tube-outrigger structure(MCCTOS) considering uncertainty. It is of great engineering significance to comprehensively analyze the seismic fragility of the structure under different types of ground motions. The purpose of this work is to analyze and predict the impact of modeling uncertainties on the performance evaluation of MCCTOS under different types of ground motions and determine the sensitivity of independent input parameters. On the premise of considering both seismic uncertainty and structural uncertainty, the Latin hypercube sampling and first-order second-moment, which are respectively combined with incremental dynamic analysis, are adopted to perform seismic fragility analyses of MCCTOS. By comparing the fragility curves of MCCTOS under non-pulse near-field ground motions, pulse-type near-field ground motions, and far-field ground motions, the influence of the randomness of earthquakes and structural parameters on the fragility of MCCTOS under different types of ground motions is analyzed. In addition, the seismic damage index is introduced into the fragility assessment of MCCTOS. By combining the seismic damage index with the beta distribution function, the matrix and probability density function of the seismic damage index of MCCTOS are established. Furthermore, the influence of structural parameter uncertainty on the seismic damage index of MCCTOS under different types of ground motions is investigated. The sensitivity analysis of uncertain parameters in the MCCTOS is carried out by using tornado analysis technology and Sobol' method to investigate the influence of the considered parameters on the seismic response of MCCTOS. The results show that pulse-type near-field ground motions are easier to cause structural damage than non-pulse near-field ground motions and far-field ground motions. However, for the MCCTOS under the non-pulse near-field ground motions, the modeling uncertainties have the greatest impact on the seismic capacity and lead to the maximum reduction of the average seismic damage index. The structural responses are found to be most sensitive to the density of concrete, compressive strength of concrete, and structural damping ratio.

Joint-Angle Adaptive Coordination Control of a Serial-Parallel Lower Limb Rehabilitation Exoskeleton

A serial–parallel lower limb rehabilitation exoskeleton can achieve a kinematic function similar to that of the human lower limb. Based on this bionic structure, joint-angle coordination is needed for model-based control. Because the kinematics of the parallel mechanism is difficult to solve, real-time attitude control of the task space cannot be realized. Moreover, owing to structural errors and parameter uncertainty, accurate modeling cannot be realized, which causes difficulties in system control. Therefore, based on existing modeling methods, adaptive control of a serial–parallel lower limb rehabilitation exoskeleton is proposed. The sensor of the parallel mechanism can realize accurate perception of the attitude, and the controller is designed to solve the control problem of the task space of the parallel component. A radial basis function neural network adaptive controller is used to compensate for the uncertainty and external disturbance of the model. Experiments were conducted to verify the effectiveness of the dynamic modeling and control system.

Uncertainty Analysis and Optimization of Quasi-Zero Stiffness Air Suspension Based on Polynomial Chaos Method

To improve the vibration isolation performance of suspensions, various new structural forms of suspensions have been proposed. However, there is uncertainty in these new structure suspensions, so the deterministic research cannot reflect the performance of the suspension under actual operating conditions. In this paper, a quasi-zero stiffness isolator is used in automotive suspensions to form a new suspension−quasi-zero stiffness air suspension (QZSAS). Due to the strong nonlinearity and structural complexity of quasi-zero stiffness suspensions, changes in structural parameters may cause dramatic changes in suspension performance, so it is of practical importance to study the effect of structural parameter uncertainty on the suspension performance. In order to solve this problem, three suspension structural parameters d0, L0 and Pc0 are selected as random variables, and the polynomial chaos expansion (PCE) theory is used to solve the suspension performance parameters. The sensitivity of the performance parameters to different structural parameters was discussed and analyzed in the frequency domain. Furthermore, a multi-objective optimization of the structural parameters d0, L0 and Pc0 of QZSAS was performed with the mean and variance of the root-mean-square (RMS) acceleration values as the optimization objectives. The optimization results show that there is an improvement of about 8%−10% in the mean value and about 40%−55% in the standard deviation of acceleration (RMS) values. This paper verifies the feasibility of the PCE method for solving the uncertainty problem of complex nonlinear systems, which provide a reference for the future structural design and optimization of such suspension systems.