Structural Model Parameters Research Articles

Random switching exponential smoothing: A new estimation approach

The random switching exponential smoothing as introduced by Sbrana and Silvestrini (International Journal of Production Economics, 156, 2014: 283–294) is a flexible forecasting model that can be used for time series with a (changing) trending behaviour. The estimation method originally proposed by these authors works only when some restrictive conditions on the parameters are met, therefore preventing its use in several practical applications. This paper presents a new, fast and efficient approach for estimating the random switching exponential smoothing model. The new method relies on the algebraic link between the model's structural parameters and the steady-state Kalman gain vector. This link simplifies the likelihood evaluation and thus reduces the computational burden. The finite-sample properties of the new estimation method are assessed in a Monte Carlo experiment and its out-of-sample forecasting performance is explored in an empirical evaluation exercise employing time series for wholesalers' inventories and sales in the United States. These series represent important business cycle indicators. Results show that both estimation methods perform broadly the same in simulations and in forecasting, over both short-term and longer-term horizons. However, despite the equal accuracy and forecasting performance, contrary to the originally proposed method, the new estimation approach can always be used, with no exceptions. In addition, its simplicity is a strong point in its favour for practitioners and forecasters working in business and industry.

Machine Learning Derived Quantitative Structure Property Relationship (QSPR) to Predict Drug Solubility in Binary Solvent Systems

Prediction of drug solubility is a crucial problem in pharmaceutical industries for both drug delivery and discovery purposes. Several theoretical approaches have been proposed to predict drug solubility in mixed solvent systems when the solubility values in pure solvents are known. Quantitative structure property relationship (QSPR) approaches are gaining attention to predict various physical properties due to their robustness and computational tractability. In this work, a machine learning based QSPR approach is proposed to predict drug solubility in binary solvent systems using structural features, such as molar refractivity, McGowan volume, topological surface area, and so forth. A genetic algorithm based feature selection procedure is used to check the dependency between the selected features and to obtain the final set of significant features. Initially, solubility is assumed to behave linearly with respect to the structural features and model parameters are estimated using ordinary least-squares an...

Seismic Vulnerability Analysis of Single-Story Reinforced Concrete Industrial Buildings with Seismic Fortification

As there is a lack of earthquake damage data for factory buildings with seismic fortifications in China, seismic vulnerability analysis was performed by numerical simulation in this paper. The earthquake-structure analysis model was developed with considering the influence of uncertainties of the ground motion and structural model parameters. The small-size sampling was conducted based on the Latin hypercube sampling and orthogonal design methods. Using nonlinear analysis, the seismic vulnerability curves and damage probability matrix with various seismic fortification intensities (SFI) were obtained. The seismic capacity of the factory building was then evaluated. The results showed that, with different designs at different SFIs, the factory building could consistently achieve the three seismic fortification objectives. For the studied factory buildings with the SFI of 6, they satisfied the seismic fortification requirements of “no damage in moderate earthquakes, mendable in strong earthquakes”; for those buildings with SFIs of 7 and 8, the requirement of “no collapsing in super strong earthquakes” was generally met; while for those with SFIs of 9, the requirement of “mendable in moderate earthquakes” was almost satisfied. The results showed factory buildings designed with low SFIs are better at achieving the seismic fortification objectives than those designed with high SFIs.

Data‐Driven Approximated Optimal Control for Chemical Processes with State and Input Constraints

Input and state constraints widely exist in chemical processes. The optimal control of chemical processes under the coexistence of inequality constraints on input and state is challenging, especially when the process model is only partially known. The objective of this paper is to design an applicable optimal control for chemical processes with known model structure and unknown model parameters. To eliminate the barriers caused by the hybrid constraints and unknown model parameters, the inequality state constraints are first transformed into equality state constraints by using the slack function method. Then, adaptive dynamic programming (ADP) with nonquadratic performance integrand is adopted to handle the augmented system with input constraints. The proposed approach requires only partial knowledge of the system, i.e., the model structure. The value information of the model parameters is not required. The feasibility and performance of the proposed approach are tested using two nonlinear cases including a continuous stirred‐tank reactor (CSTR) example.

Parametric Identification Using Weighted Null-Space Fitting

In identification of dynamical systems, the prediction error method with a quadratic cost function provides asymptotically efficient estimates under Gaussian noise, but in general it requires solving a nonconvex optimization problem, which may imply convergence to nonglobal minima. An alternative class of methods uses a nonparametric model as intermediate step to obtain the model of interest. Weighted null-space fitting (WNSF) belongs to this class, starting with the estimate of a nonparametric ARX model with least squares. Then, the reduction to a parametric model is a multistep procedure where each step consists of the solution of a quadratic optimization problem, which can be obtained with weighted least squares. The method is suitable for both open- and closed-loop data, and can be applied to many common parametric model structures, including output-error, ARMAX, and Box–Jenkins. The price to pay is the increase of dimensionality in the nonparametric model, which needs to tend to infinity as function of the sample size for certain asymptotic statistical properties to hold. In this paper, we conduct a rigorous analysis of these properties: namely, consistency, and asymptotic efficiency. Also, we perform a simulation study illustrating the performance of WNSF and identify scenarios where it can be particularly advantageous compared with state-of-the-art methods.

BIM-based structural survey design

The study, which is part of the digitalization project of the public real estate portfolio of the City of Turin (TOBIM), explores the use of parametric structural models in organizing the knowledge and the data involved in the investigations and analysis on an existing building. The recommended approach exploits Building Information Modelling to set up a graphic and alphanumeric database of interoperable information that can be implemented throughout the building life cycle. Specifically, this paper promotes solutions focused on maximizing BIM data for structural survey design activities. The case study analyzed is a pavilion school complex of the 70s located in Turin.

Specifications for Building a Parametric Model in Digital Architectural Designs

The construction of parametric model is an important stage in the digital design process in general and in the parametric design process in particular. The parametric model allows the designer to make changes and reshape the geometry without erasing and redrawing. It also helps to explore design alternatives as it provides a level of flexibility to be continuously evaluated, revised and updated when adding or altering different components within the same parametric model structure.
 The research problem has been identified, as there is no clear definition of the specifications of constructing a parametric model in the contemporary digital architectural designs. Therefore, the objective of the research is to put forward a theoretical framework that defines clearly the specifications of building a parametric model. The framework describes the specifications using the following issues: the timing of constructing the parametric model, the knowledge employed in the construction of parametric model, the methods of constructing and revising a parametric model, The place where a parametric model is applied, and finally the number of parametric models within a design. The framework has been applied to six international projects adopting a parametric design approach. The results showed that employing parametric modeling mostly starts at the development stage of design and continues in the detailing and manufacturing stages, the adoption of ill-defined knowledge, the definition of design variables in terms of quantitative and qualitative characteristics, and using one parametric model shared among multiple design disciplines.

Probabilistic hierarchical Bayesian framework for time-domain model updating and robust predictions

Abstract A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics. The proposed framework provides a reliable approach to account for the variability of the inference results observed when using different data sets. The proposed formulation is compared with a state-of-the-art Bayesian approach using numerical and experimental examples. The results indicate that the hierarchical Bayesian framework provides a more realistic account of the uncertainties, whereas the non-hierarchical Bayesian approach severely underestimates them. Moreover, the proposed hierarchical framework predicts the system output quantities of interest with reasonable accuracy producing reliable uncertainty bounds, as opposed to the non-hierarchical approach which yields unrealistically narrow uncertainty bounds, although the model error is considerable. It is found that in the hierarchical approach the response prediction uncertainties are dominated by the uncertainties in the model parameters. As a result, the propagation of uncertainty can be performed using only the uncertainty of structural model parameters. This feature allows it making robust predictions of system output quantities of interest, especially where no information about the statistics of prediction error parameters is available. The hierarchical framework is proposed herein in the time-domain when incomplete input-output data is available. However, it has great potential to be applied to different forms of inference problems met in various disciplines of science and engineering.

Bayesian Finite Element Model Updating and Assessment of Cable-Stayed Bridges Using Wireless Sensor Data.

We focus on a Bayesian inference framework for finite element (FE) model updating of a long-span cable-stayed bridge using long-term monitoring data collected from a wireless sensor network (WSN). A robust Bayesian inference method is proposed which marginalizes the prediction-error precisions and applies Transitional Markov Chain Monte Carlo (TMCMC) algorithm. The proposed marginalizing error precision is compared with other two treatments of prediction-error precisions, including the constant error precisions and updating error precisions through theoretical analysis and numerical investigation based on a bridge FE model. TMCMC is employed to draw samples from the posterior probability density function (PDF) of the structural model parameters and the uncertain prediction-error precision parameters if required. It is found that the proposed Bayesian inference method with prediction-error precisions marginalized as “nuisance” parameters produces an FE model with more accurate posterior uncertainty quantification and robust modal property prediction. When applying the identified modal parameters from acceleration data collected during a one-year period from the large-scale WSN on the bridge, we choose two candidate model classes using different parameter grouping based on the clustering results from a sensitivity analysis and apply Bayes’ Theorem at the model class level. By implementing the TMCMC sampler, both the posterior distributions of the structural model parameters and the plausibility of the two model classes are characterized given the real data. Computation of the posterior probabilities over the candidate model classes provides a procedure for Bayesian model class assessment, where the computation automatically implements Bayesian Ockham razor that trades off between data-fitting and model complexity, which penalizes model classes that “over-fit” the data. The results of FE model updating and assessment based on the real data using the proposed method show that the updated FE model can successfully predict modal properties of the structural system with high accuracy.

Improving multiple model parameters of a complex fluid-loaded structure from acoustic measurements

A method for correcting multiple mechanical and acoustical properties in a finite element model using the Neumann series is presented and demonstrated with numerical examples. In previous work, the authors developed a method for estimating a model parameter in a complex structure using a Neumann series as an approximation to system response. The method computed the sensitivity of the system response due to changes in the parameter and subsequently determined the change needed in the parameter to bring model response into agreement with vibration measurements. This previous work demonstrated the accuracy and computational efficiency of the method when correcting one model parameter. In the present work, the authors extend the analysis to compute sensitivities for multiple parameters of a system, allowing for the correction of more than one parameter. The limits and accuracies of the method are explored for a canonical acoustic system in which a complex structure interacts with an acoustic medium. Two uncertain model parameters of the system are corrected by bringing model response into agreement with acoustic measurements. Results of these examples will be reviewed and presented to illustrate the accuracy and robustness of the method. A method for correcting multiple mechanical and acoustical properties in a finite element model using the Neumann series is presented and demonstrated with numerical examples. In previous work, the authors developed a method for estimating a model parameter in a complex structure using a Neumann series as an approximation to system response. The method computed the sensitivity of the system response due to changes in the parameter and subsequently determined the change needed in the parameter to bring model response into agreement with vibration measurements. This previous work demonstrated the accuracy and computational efficiency of the method when correcting one model parameter. In the present work, the authors extend the analysis to compute sensitivities for multiple parameters of a system, allowing for the correction of more than one parameter. The limits and accuracies of the method are explored for a canonical acoustic system in which a complex structure interacts with an acoustic medium. Two uncer...

Multiple robust estimation of marginal structural mean models for unconstrained outcomes.

We consider estimation, from longitudinal observational data, of the parameters of marginal structural mean models for unconstrained outcomes. Current proposals include inverse probability of treatment weighted and double robust (DR) estimators. A difficulty with DR estimation is that it requires postulating a sequence of models, one for the each mean of the counterfactual outcome given covariate and treatment history up to each exposure time point. Most natural models for such means are often incompatible. Robins et al., (2000b) proposed a parameterization of the likelihood which implies compatible parametric models for such means. Their parameterization has not been exploited to construct DR estimators and one goal of this article is to fill this gap. More importantly, exploiting this parameterization we propose a multiple robust (MR) estimator that confers even more protection against model misspecification than DR estimators. Our methods are easy to implement as they are based on the iterative fit of a sequence of weighted regressions.

Statistical inference for nonignorable missing-data problems: a selective review

ABSTRACTNonignorable missing data are frequently encountered in various settings, such as economics, sociology and biomedicine. We review statistical inference for nonignorable missing-data problems, including estimation, influence analysis and model selection. For estimation of mean functionals, we review semiparametric method and empirical likelihood (EL) approach. For estimation of parameters in exponential family nonlinear structural equation models, we introduce expectation-maximisation algorithm, Bayesian approach, and Bayesian EL method. For influence analysis, we investigate the case-deletion method and local influence analysis method from the frequentist and Bayesian viewpoints. For model selection, we present the modified Akaike information criterion and penalised method.

Effects of Reducing the Length of the Questionnaire by Multiple Matrix Sampling on the Validity of Structural Equation Modeling for Factors Affecting Job Morale

The research was designed to examine the effects of question setting using different conditions into 10 sets on the validity of structural equation modeling for factors affecting job morale. The data was collected from 690 personnel working in regional Statistical Offices around Thailand by using cluster random sampling. The tool used in collecting data was the questionnaire with 95 items and 5 levels of rating scale. The discrimination value was between 0.244 and 0.860 and the reliability: α was from 0.699 to 0.900. Data analysis was conducted by the use of descriptive statistics and structural equation modeling by Mplus 7.4 program. The findings showed that the structural equation modeling with question setting in 3 sets of sub-questionnaires, cooperated with item sampling without replacement and non-fixing core items, conformed to the empirical data the most and that overall relationship between the structural model parameter and the model parameter from the complete questionnaire was high in a positive way.

Finite element response sensitivity analysis of three-dimensional soil-foundation-structure interaction (SFSI) systems

The nonlinear finite element (FE) analysis has been widely used in the design and analysis of structural or geotechnical systems. The response sensitivities (or gradients) to the model parameters are of significant importance in these realistic engineering problems. However the sensitivity calculation has lagged behind, leaving a gap between advanced FE response analysis and other research hotspots using the response gradient. The response sensitivity analysis is crucial for any gradient-based algorithms, such as reliability analysis, system identification and structural optimization. Among various sensitivity analysis methods, the direct differential method (DDM) has advantages of computing efficiency and accuracy, providing an ideal tool for the response gradient calculation. This paper extended the DDM framework to realistic complicated soil-foundation-structure interaction (SFSI) models by developing the response gradients for various constraints, element and materials involved. The enhanced framework is applied to three-dimensional SFSI system prototypes for a pile-supported bridge pier and a pile-supported reinforced concrete building frame structure, subjected to earthquake loading conditions. The DDM results are verified by forward finite difference method (FFD). The relative importance (RI) of the various material parameters on the responses of SFSI system are investigated based on the DDM response sensitivity results. The FFD converges asymptotically toward the DDM results, demonstrating the advantages of DDM (e.g., accurate, efficient, insensitive to numerical noise). Furthermore, the RI and effects of the model parameters of structure, foundation and soil materials on the responses of SFSI systems are investigated by taking advantage of the sensitivity analysis results. The extension of DDM to SFSI systems greatly broaden the application areas of the d gradient-based algorithms, e.g. FE model updating and nonlinear system identification of complicated SFSI systems.

Robust versus consistent variance estimators in marginal structural Cox models.

In survival analyses, inverse-probability-of-treatment (IPT) and inverse-probability-of-censoring (IPC) weighted estimators of parameters in marginal structural Cox models are often used to estimate treatment effects in the presence of time-dependent confounding and censoring. In most applications, a robust variance estimator of the IPT and IPC weighted estimator is calculated leading to conservative confidence intervals. This estimator assumes that the weights are known rather than estimated from the data. Although a consistent estimator of the asymptotic variance of the IPT and IPC weighted estimator is generally available, applications and thus information on the performance of the consistent estimator are lacking. Reasons might be a cumbersome implementation in statistical software, which is further complicated by missing details on the variance formula. In this paper, we therefore provide a detailed derivation of the variance of the asymptotic distribution of the IPT and IPC weighted estimator and explicitly state the necessary terms to calculate a consistent estimator of this variance. We compare the performance of the robust and consistent variance estimators in an application based on routine health care data and in a simulation study. The simulation reveals no substantial differences between the 2 estimators in medium and large data sets with no unmeasured confounding, but the consistent variance estimator performs poorly in small samples or under unmeasured confounding, if the number of confounders is large. We thus conclude that the robust estimator is more appropriate for all practical purposes.

PMS31 - Quantifying the Importance of Model Parameters and Structural Assumptions on the Value of Treatments for Rheumatoid Arthritis Using Metamodeling

Our aim was to quantify the relative importance of model parameters and model structures on the cost-effectiveness estimates of biologic disease modifying anti-rheumatic drugs (bDMARDs) vs conventional DMARDs (cDMARDs) for treatment of patients with moderate to severe rheumatoid arthritis with inadequate response to cDMARDs. We used the Innovation and Value Initiative’s open-source rheumatoid arthritis individual patient simulation to simulate outcomes. 1,000 sets of parameter values were sampled in a probabilistic sensitivity analysis (PSA) and 32 model structures were simulated. For each parameter set and model structure, 1,000 patients were simulated to calculate the mean incremental net monetary benefit (iNMB). The simulated mean iNMB was regressed on values of the model parameters and characteristics of the model structures using linear regression models. The absolute values of the coefficients of standardized variables were used to rank the model inputs by their relative importance. Structural assumptions related to treatment switching, long-term progression of the Health Assessment Questionnaire (HAQ) score, utility, and the effect of treatment on HAQ had large impacts on the iNMB. For example, models that use a latent class growth model to simulate HAQ progression for patients using cDMARDs were predicted to increase the iNMB by around $45,000 relative to models that assumed a linear rate of progression. Standardized input parameters with the largest coefficients in absolute value included the extent to which the HAQ score rebounded after treatment failure and the impact of changes in the HAQ score on mortality. Cost-effectiveness estimates for rheumatoid arthritis vary due to both parameter and structural uncertainty. New studies are needed to improve the quality of parameter estimates; consensus-driven approaches such as Delphi panels can help determine appropriate model structures. Research should prioritize the most sensitive model parameters and debates should focus on the structural assumptions most likely to influence results.

Sensitivity of Seismic Response and Fragility to Parameter Uncertainty of Single-Layer Reticulated Domes

Quantitatively modeling and propagating all sources of uncertainty stand at the core of seismic fragility assessment of structures. This paper investigates the effects of various sources of uncertainty on seismic responses and seismic fragility estimates of single-layer reticulated domes. Sensitivity analyses are performed to examine the sensitivity of typical seismic responses to uncertainties in structural modeling parameters, and the results suggest that the variability in structural damping, yielding strength, steel ultimate strain, dead load and snow load has significant effects on the seismic responses, and these five parameters should be taken as random variables in the seismic fragility assessment. Based on this, fragility estimates and fragility curves incorporating different levels of uncertainty are obtained on the basis of the results of incremental dynamic analyses on the corresponding set of 40 sample models generated by Latin Hypercube Sampling method. The comparisons of these fragility curves illustrate that, the inclusion of only ground motion uncertainty is inappropriate and inadequate, and the appropriate way is incorporating the variability in the five identified structural modeling parameters as well into the seismic fragility assessment of single-layer reticulated domes.

Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables

Pairwise likelihood is a limited information estimation method that has also been used for estimating the parameters of latent variable and structural equation models. Pairwise likelihood is a special case of composite likelihood methods that uses lower-order conditional or marginal log-likelihoods instead of the full log-likelihood. The composite likelihood to be maximized is a weighted sum of marginal or conditional log-likelihoods. Weighting has been proposed for increasing efficiency, but the choice of weights is not straightforward in most applications. Furthermore, the importance of leaving out higher-order scores to avoid duplicating lower-order marginal information has been pointed out. In this paper, we approach the problem of weighting from a sampling perspective. More specifically, we propose a sampling method for selecting pairs based on their contribution to the total variance from all pairs. The sampling approach does not aim to increase efficiency but to decrease the estimation time, especially in models with a large number of observed categorical variables. We demonstrate the performance of the proposed methodology using simulated examples and a real application.

Global sizing optimisation using dual‐level response surface method based on mixed‐resolution central composite design for permanent magnet synchronous generators

This study presents a global sizing design optimisation of a permanent magnet synchronous generator (PMSG) using the three-dimensional finite-element analysis (3D FEA). To build an optimal parametric model structure, the efficiency improvement of the PMSG is taken as the main objective, where iron and copper losses were minimised. A dual-level response surface methodology (D-RSM) with a window-zoom-in approach for a variable-speed-range analysis as a global optimisation technique is employed to find out the optimal design variables of the objective function. The D-RSM using mixed-resolution central composite design (MR-CCD), full factorial design, central composite design (CCD), and box-Behnken design are applied to optimise the geometry with very small error. Analysis of variance and multi-level RSM plots are used to check the adequacy of fit. However, the MR-CCD exceeds the range of the boundary in the design region. Hence, a modified MR-CCD is used that improves the efficiency and proposes the parameter settings to manufacture the high-class quality wind generator. The validation of the analytical and numerical fashions is successfully achieved through rigorous FEA, and the experimental verifications perfectly marked the theoretical and significance optimisation design.

Performance-based seismic assessment of a large concrete framed structure supporting multi-units of heavy compressors

In a liquefied nature gas (LNG) plant, a large concrete framed structure supporting multi-units of heavy compressors was designed in accordance with ACI 318-08 code requirements and ASCE 7-05seismic loads. When applied to new structures, provisions of ACI 318 are intended to provide Life Safety (LS) performance for the Design Basis Earthquake (DBE). Due to the important function of the compressors, this study will perform a seismic assessment of the Table Top Pedestal to assure adequate capacity of preventing collapse from Maximum Considered Earthquake (MCE) and only having limited structural damage under a moderate (MOD) 20%@50 years earthquake event. In this study, the Linear Dynamic Procedure (LDP) of ASCE 41-13 is used for the seismic performance evaluation of the table top structure. The structural modeling parameters and acceptance criteria of structural performances are based on Chapter 10 of ASCE 41-13. Soil-structural interaction and P-Delta effects are considered in the analysis process. The response spectra of the three levels of seismic hazards of DBE, MCE and MOD earthquake were developed for response spectra analysis. The procedures presented in this study can be used as a general guideline for Performance-Based Design of most reinforced concrete structures located in industrial plants.