Structural Model Parameters Research Articles

Item-Specific Factors in IRTree Models: When They Matter and When They Don’t

Lyu et al. (Psychometrika, 2023) demonstrated that item-specific factors can cause spurious effects on the structural parameters of IRTree models for multiple nested response processes per item. Here, we discuss some boundary conditions and argue that person selection effects on item parameters are not unique to item-specific factors and that the effects presented by Lyu et al. (Psychometrika, 2023) may not generalize to the family of IRTree models as a whole. We conclude with the recommendation that IRTree model specification should be guided by theoretical considerations, rather than driven by data, in order to avoid misinterpretations of parameter differences.

Automated multi-objective system identification using grammar-based genetic programming

In order to use existing identification tools effectively, a user must make critical choices a priori that ultimately determine the quality of estimated models. Furthermore, while estimated models are typically optimized for a single identification criterion, engineering applications typically impose multiple performance specifications that may contradict each other. In this contribution, we develop a system identification methodology that automatically selects parametric model structures from a wide range of dynamic system models based on measured data. The problem of inferring model structures and estimating model parameters within these structures is encapsulated in a bi-level optimization problem. The optimization problem is formulated for multiple user-specified identification objectives. Finally, the range of dynamical systems considered for the optimization problem is specified using Tree Adjoining Grammar. A solution approach based on genetic programming is developed, and its asymptotic properties and computational complexity is analysed. The empirical performance of the proposed identification techniques is studied using a simulation example.

Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator

AbstractUsing digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to real measurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducible way such that other researcher can use this setup to validate their methodologies.

On the Bayesian model updating based on model reduction using complex modal data for damage detection

An approach for the Bayesian model updating of a linear dynamic system using complex modal data identified from dynamic test data is proposed in this paper. Very few works have utilized complex modal data for Bayesian model updating, and these works consider only the Most Probable Value (MPV) of the modal parameters as the modal data. On the other hand, the present work considers the posterior uncertainties of the modal parameters along with the MPVs as the modal data. Additionally, dynamic reduction has been applied to downsize the full system model to a reduced model with master Degrees of Freedom (DOFs) of the reduced model confined to the observed DOFs. Dynamic reduction facilitates updating of structural parameters without the requirement of mode matching. Additional uncertain parameters in the form of system modal frequencies, damping ratios, and partial mode shapes are introduced to establish a link between the structural model parameters and the modal data through the eigenvalue equation. Detailed formulation leading to the development of the posterior Probability Density Function (PDF) is presented, and a new Metropolis-within-Gibbs (MWG) sampler is proposed to simulate samples from the posterior PDF. Furthermore, a formulation is presented for evaluating the probability of damage based on the posterior samples obtained using the proposed approach from the structure's undamaged and possibly-damaged state. The proposed approach is validated and demonstrated comprehensively through simulated examples and an experimental study. Lastly, the performance of the proposed approach is compared with an alternative approach which is developed by integrating out the partial system mode shape from the posterior PDF.

Simulation‐based methodology to identify damage indicators and safety thresholds for post‐earthquake evaluation of structures

AbstractAfter a strong earthquake, criteria are needed to determine whether buildings are safe to reoccupy based on observable damage. This paper presents a simulation‐based methodology to identify relevant damage indicators and safety thresholds for building structures. Prior knowledge of the most relevant damage indicators and their thresholds can increase the accuracy and confidence of post‐earthquake evaluations. Current practice to translate observable damage into a tagging decision relies on qualitative guidelines based on past earthquake experience and judgment, which may be susceptible to speculation and interpretation. In addition, past experience may not be relevant to newer structural systems and to large or complex (e.g., high‐rise) buildings. To augment past observations and data from structural component tests, nonlinear dynamic analyses can be used to estimate the collapse safety of structures with simulated damage. Technologies to execute these simulations have matured over the years, although to date they have not been systematically applied to evaluate the destabilizing effects of simulated damage on collapse safety. In this paper, a methodology is presented to use numerical simulations of damage to identify and evaluate relevant damage indicators that can be quantitatively related to safety thresholds. Damage indicators are selected based on their reliability in estimating the structural safety and their sensitivity to modeling uncertainty, that is, where the preferred indicators are insensitive to variability in the structural materials and model parameters. The safety threshold for each damage indicator is selected to maximize accuracy in post‐earthquake building assessments. The methodology is demonstrated through an application study of ductile reinforced concrete frame buildings. Results show that aggregated indices of structural component damage (e.g., aggregated over the floor of a building) outperform other damage indicators based on peak or residual drifts or simpler percentages of damaged components. Subject to agreement of a number underlying assumptions, this methodology can be applied to a wider variety of structures to improve post‐earthquake evaluation guidelines.

A Single Objective Crow Search for Modelling of Horizontal Flexible Plate Structure

The magnificent features of flexible plate structure, including lightweight and high-speed response, resulted in additional market demand, especially in the automotive and manufacturing industries. Nevertheless, the structure may incur structural damage and performance degradation when the system encounters excessive vibration. Therefore, a system identification approach utilising a metaheuristic algorithm via crow search to develop a horizontal flexible plate (HFP) model for vibration control is introduced in this paper. Crow search (CS) is a modern algorithm inspired by a crow’s intellectual operation to store additional food and memorise the food storage location. In this study, CS is employed to optimise the objective function, which is the mean squared error for accomplishing a precise predicted model in replicating the dynamic response of the actual structure. Hence, the preliminary action for modelling using this approach is designing and fabricating an HFP rig for experimentally gathering the real input-output vibration data. After that, the mathematical modelling utilising the CS algorithm was implemented using a parametric model structure. Finally, the best-fit model is chosen for the representation of the HFP based on the lowest mean squared error, correlation test within a 95% confidence level and stability in a pole-zero plot. The simulation result reveals that the CS algorithm with a second-order estimated model accomplished a minimum MSE of 1.1168 × 10-5, an unbiased correlation test and excellent stability for the HFP structure.

Self-construal, face concerns and conflict management strategies: a meta-analysis

PurposeIn this meta-analysis, the authors investigate the relationship between self-construal and conflict management strategies and shed light on the inconsistent findings in the literature. Moreover, they examine the mediating role of face concerns in this relationship. Importantly, the present meta-analysis is the first to test the assumptions of face-negotiation theory with the meta-analytic structural equation modeling (MASEM), which enabled the authors to test the hypothesized relationships in one single model.Design/methodology/approachThe authors used the method of MASEM to test the relationship between self-construal and conflict management and assess the mediating role of face concerns. In this regard, the authors employed one-stage meta-analytic structural equation modeling to perform MASEM and its moderators.FindingsTwo hundred fifty-four effect sizes based on thirty-three studies were pooled in this meta-analysis. The authors found that individuals with stronger independent self-construal and stronger self-face concerns were more likely to use forcing. Moreover, the relationship between independent self-construal and forcing was mediated by self-face concerns. Individuals with a stronger interdependent self-construal and individuals with stronger other-face concerns were more likely to use problem-solving and yielding. The relationship between interdependent self-construal and problem-solving and yielding was mediated by other-face concerns. Finally, interdependent self-construal also had an indirect effect, via other-face concern, on avoiding and compromising.Originality/valueThe present meta-analysis is the first effort that the authors are aware of to test the assumptions of face-negotiation theory (FNT) using MASEM method. The authors used one-stage meta-analytic structural equation modeling viewed as the state of the methods to perform MASEM and its moderators. They employed full information meta-analytic structural equation modeling to show the generalizability and heterogeneity of structural equation modeling parameters. They applied studentized deleted residuals to assess outlier analysis and also conducted different methods to perform MASEM to check the robustness of the findings resulted. Finally, the current study adds multiple methods of assessing for publication bias.

Elbow Joint Stiffness Functional Scales Based on Hill's Muscle Model and Genetic Optimization.

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified "Reach and retrieve" task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill's muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.

Optimization of Steel Portal Frames under a Parametric Structural Design Framework

This paper presents a framework for the structural design of single-story industrial steel portal frames. The structural mechanical utilization, stability, and serviceability design requirements employing a parametric structural tridimensional computational model are included. The model parameterization consists of adopting the column, rafter, purlins, and cladding span lengths as parameters, as well as the cross-section sizes of these members, the latter in accordance with the standard commercial cross section’s tables. The main outcome is the possibility to scan the range of parameters and store the structural performance ratios, generating a solution space for this structural system. This space of valid solutions attending structural code requirements defines a set of valid structural system topological configuration and dimensions, making it possible to obtain relationships between the parameters and material consumption rate. Concave-hull 2D plots are used to visualize the solution space and identify the best choice of parameters in the design process. It is shown that the solution space generation and the concave-hull 2D plots are powerful design aids that help to identify the best combination of structural system topological dimensions and cross-section types and dimensions.

Multi-Attribute Machine Learning Model for Electrical Motors Performance Prediction

Designing an electrical motor is a complex process that needs to deal with the non-linearity phenomena caused by the saturation of the iron at high magnetic field strength, the multi-physical nature of the investigated system and with requirements that may come into conflict. This paper proposes to use geometric parametric models to evaluate the multi-physical performances of electrical machines and build a machine learning model that is able to predict multi-physical characteristics of electrical machines from input geometrical parameters. The focus of this work is to accurately estimate the electromagnetic characteristics, motor losses and stator natural frequencies, using the developed machine learning model, at the early-design stage of the electrical motor, when the information about the housing is not available and to include the model in optimisation loops, to speed-up the computational time. Three individual machine learning models are built for each physics analysed, a model for the torque and back electromotive force harmonic orders, one model for motor losses and another one for natural frequencies of the mode-shapes. The necessary data is obtained by varying the geometrical parameters of 2D electromagnetic and 3D structural motor parametric models. The accuracy of different machine learning regression algorithms are compared to obtain the best model for each physics involved. Ultimately, the developed multi-attribute model is integrated in an optimisation routine, to compare the computational time with the classical finite element analysis (FEA) optimisation approach.

In Memory of Leif Johansen: The World’s First CGE Model and Its Application to Russia

Perhaps the best way to honor Leif Johansen’s legacy as a theorist on the fortieth anniversary of his death is to show that his ideas are still pertinent and able to inspire new research. This paper adapts the world’s first CGE model for MSG, which he created in the late 1950s, to current Russian statistics. The CGE model has been used to calculate the consequences to be anticipated from two groups of scenarios: 1) accession to Russia of four new regions in 2022; and 2) a complete trade blockade of Russia due to exacerbation of the crisis in Ukraine. During preparation of the input data, balancing of the input-output tables was achieved by developing an algorithm based on a certain discrete analog of the elastic filter algorithm. This approach can also be used to prepare input data for other single-region CGE models. The version of MSG that was constructed for Russia can be used for teaching university students, as a BOTE model, or for debugging more complex CGE models, or as an auxiliary tool for estimating import substitution frontiers, country production frontiers or various structural parameters of CGE models. Despite the more than six decades that have passed since its inception, the MSG model still does not look outdated in essence and contains some elements that are sometimes undeservedly overlooked in a number of modern CGE models.

Text Sentiment Analysis Based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure

Sentiment analysis, commonly called opinion mining or emotion artificial intelligence (AI), employs biometrics, computational linguistics, natural language processing, and text analysis to systematically identify, extract, measure, and investigate affective states and subjective data. Sentiment analysis algorithms include emotion lexicon, traditional machine learning, and deep learning. In the text sentiment analysis algorithm based on a neural network, multi-layer Bi-directional long short-term memory (LSTM) is widely used, but the parameter amount of this model is too huge. Hence, this paper proposes a Bi-directional LSTM with a trapezoidal structure model. The design of the trapezoidal structure is derived from classic neural networks, such as LeNet-5 and AlexNet. These classic models have trapezoidal-like structures, and these structures have achieved success in the field of deep learning. There are two benefits to using the Bi-directional LSTM with a trapezoidal structure. One is that compared with the single-layer configuration, using the of the multi-layer structure can better extract the high-dimensional features of the text. Another is that using the trapezoidal structure can reduce the model’s parameters. This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2 (STS-2) for experiments. It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances. However, the trapezoidal structure model parameters are 35.75% less than the normal structure model.

Estimation of Soil–Structure Model Parameters for the Millikan Library Building Using a Sequential Bayesian Finite Element Model Updating Technique

We present a finite element model updating technique for soil–structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil–structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring, and diagnosis of civil structures.

Design and Optimization of Multipoint Sampler for Seafloor Sediment Carried by a Deep-Sea Landing Vehicle

The present study proposes a low-energy consumption multipoint sampler carried by a deep-sea landing vehicle (DSLV) to meet the requirements of time series sampling in local areas and location series sampling in wide areas, and an optimization method of sampling structure based on least-squares support-vector machine (LSSVM) surrogate model and a multi-objective particle swarm optimization (MOPSO) algorithm. First, the overall structure and core components, such as the multipoint sampler’s sampling structure, were designed. The optimization variables were the cone angle, sampling tube inner diameter, and sampling tube inner hole length, which were determined by considering the force with which the sampling structure penetrates the seafloor sediment. Then, the sampling process was simulated by the finite element method-smoothed particle hydrodynamics (FEM-SPH) method, while the accurate LSSVM model of force required for sampling and sampling tube volume was established. Finally, the MOPSO algorithm was used for multi-objective optimization of model parameters of sampling structure. The optimal model of sampling structure that can provide theoretical support for the optimal design of multipoint sampler effectively reduces energy consumption and improves sampling efficiency by force required for sampling 25.89% lower and sampling tube volume 34.81% higher than the original model.

Structured Factor Analysis: A Data Matrix-Based Alternative Approach to Structural Equation Modeling

Jöreskog’s covariance-based approach (JCA) has been considered a standard method for structural equation modeling. However, JCA is prone to the occurrence of improper solutions and cannot make probabilistic inferences about the true factor scores. To address the enduring issues of JCA, we propose a data matrix-based alternative, termed structured factor analysis (SFA). Given a data matrix of indicators, SFA begins by estimating both measurement model parameters and factor scores by minimizing a single cost function via an alternating least squares algorithm, which mathematically guarantees convergence to proper solutions. It then employs the factor score estimates to estimate structural model parameters. Once all parameters are estimated, SFA further estimates the probability distribution of the factor scores that can generate the data matrix of indicators, which can be used for probabilistic inferences about the true factor scores. We investigate SFA’s performance and empirical utility through simulated and real data analyses.

A model of the U.S. food system: What are the determinants of the state vulnerabilities to production shocks and supply chain disruptions?

AbstractWe adapt a Ricardian general equilibrium model to the setting of U.S. domestic agri‐food trade to assess states’ vulnerability to adverse production shocks and supply chain disruptions. To this end, we analyze how domestic crop supply chains depend on fundamental state‐level comparative advantages—which reflect underlying differences in states’ cost‐adjusted productivity levels—and thereby illustrate the capacity of states to adapt to and mitigate the impacts of such disruptions to the U.S. agricultural sector. Based on the theoretical framework and our estimates of the model's structural parameters obtained using data on U.S. production, consumption, and domestic trade in crops, we undertake simulations to characterize the welfare implications of counterfactual scenarios depicting disruptions to (1) states’ agricultural productive capacity, and (2) interstate supply linkages. Our results emphasize that the distributional impacts of domestic supply chain disruptions hinge on individual states’ agricultural productive capacities, and that the ability of states to mitigate the impacts of adverse production shocks through trade relies on the degree to which states are able to substitute local production shortfalls by sourcing crops from other states.

Accelerating computations in two-stage Bayesian system identification with Fisher information matrix and eigenvalue sensitivity

Structural system identification aims at identifying the parameters of a theoretical (e.g., finite element) model using measured data of a constructed structure. As a means of extracting relevant information from noisy vibration data, one common strategy is to adopt a ‘two-stage’ approach, where natural frequencies and mode shapes are first estimated from Stage I (modal identification), and then used for further estimating the structural model parameters (e.g., stiffness, mass) in Stage II. In a Bayesian statistical context with sufficient data so that model parameters can be identified uniquely, the identification result is characterized by the ‘most probable value’ (MPV) that gives the best estimate, and a covariance matrix that quantifies the remaining uncertainty. Computationally, determining the MPV involves solving an optimization problem with a measure-of-fit function; the covariance matrix involves taking the Hessian of the function. Exploiting the mathematical structure of the measure-of-fit function in the two-stage problem, this work proposes a Fisher scoring method for determining the MPV, where the Hessian matrix in a Newton’s iteration is replaced by the Fisher information matrix to eliminate repeated computations of second order derivatives and to improve convergence robustness. Advanced techniques of eigenvalue sensitivity are applied so that the gradient and Hessian involved in MPV and covariance matrix computations can be obtained accurately and efficiently by solving full-rank matrix equations for the subject modes only. The proposed methodology is investigated with synthetic and laboratory experimental data. Performance in terms of convergence robustness and efficiency is compared with existing methods, including simplex search and Newton’s method.

An ensemble dynamic self-learning model for multiscale carbon price forecasting

Precise carbon price forecasting can provide decision support for policy-makers and investors. However, due to the high non-stationarity and nonlinearity of carbon price series, it is difficult to get accurate forecasting results under volatile situations. To accommodate different scenarios, this paper proposes a dynamic self-learning integrating forecasting model to forecast the carbon price by considering external impact factors. The multi-dimensional time series is initially decomposed into different intrinsic mode functions simultaneously by the noise-assisted multivariate empirical mode decomposition method. After reconstructing the decomposed series into high-frequency, low-frequency, and trend modules, the extreme learning machine optimized by the cosine-based whale optimization algorithm is proposed to predict the carbon price. The dynamic relationships between the carbon price and impact factors are simulated by the sliding window structure, which improves the adaptability of the proposed model. The high prediction accuracy under different situations including extreme scenarios demonstrates the stability of the proposed model. A self-learning algorithm, which can automatically learn the evolving model structure and update model parameters, is designed to alleviate the underfitting/overfitting problem. The comparison results with existing models indicate the superiority of the proposed model.

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.

Multilevel mediation analysis with structured unmeasured mediator-outcome confounding

Mediation analysis usually requires the assumption that there is no unmeasured mediator-outcome confounder. However, this may not hold in many social and scientific studies. Though various parametric and nonparametric mediation methods have been developed, this assumption remains instrumental, without which the causal effects are not identifiable unless alternative assumptions are imposed. To circumvent this, a multilevel parametric structural equation modeling framework is proposed to relax this no unmeasured mediator-outcome confounding assumption under a specific data setting inspired by a real experiment. Using the proposed framework, it is shown that the causal effects are identifiable and consistently estimated. Likelihood-based approaches are proposed with efficient optimization algorithms to estimate the parameters, including the unmeasured confounding effect, instead of performing sensitivity analysis. The asymptotic consistency is established. Using extensive simulations and a functional magnetic resonance imaging dataset, the improvement of the approaches over existing methods is demonstrated. The R package macc for implementation is available on CRAN.