Model Calibration Techniques Research Articles

Seismic performance of sheet pile walls in liquefiable soil using centrifuge tests and an assessment of nonlinear effective stress analysis using PM4Sand

Past earthquakes have revealed that damage to sheet-pile walls under saturated conditions is closely linked to excess pore water pressure buildup in the surrounding soil. Nonlinear effective stress analysis (ESA) is commonly employed to assess the seismic performance of sheet-pile walls in liquefiable soils, incorporating constitutive models for liquefaction simulation. However, ESA results are sensitive to uncertainties in input parameters, model calibration, and modeling techniques. Dynamic centrifuge tests conducted in the Liquefaction Experiments and Analysis Project (LEAP) offer valuable insights into important response mechanisms and validate ESA. Seven centrifuge tests on a cantilevered sheet-pile wall model showed that liquefaction did not occur in the backfill near the wall due to net seaward wall displacement but did occur farther away. In addition, the mechanism of wall displacement was mainly due to the shear deformation of the softened backfill, with the displacement magnitude depending on the relative density of soil, peak ground acceleration of base motion, and wall displacement during gravity loading. Nonlinear ESA was performed for three centrifuge tests using FLAC2D and the PM4Sand constitutive model for soil. Gravity analysis captured static wall displacement and initial stress distribution in the soil. Two calibrations of the PM4Sand model were pursued at the element level: C1 calibration for liquefaction strength and C2 calibration for liquefaction strength and the post-liquefaction shear strain accumulation rate. System-level simulations showed similar liquefaction behavior as observed in the tests for both calibrations. However, the C2 calibration provided closer predictions of wall displacements, while the C1 calibration (default for PM4Sand) resulted in larger and more conservative displacements. Overall, the PM4Sand model performed well with minimal calibration, making it suitable for nonlinear ESA of sheet-pile walls.

BAYESIAN CALIBRATION WITH ADAPTIVE MODEL DISCREPANCY

We investigate a computer model calibration technique inspired by the well-known Bayesian framework of Kennedy and O'Hagan (KOH). We tackle the full Bayesian formulation where model parameter and model discrepancy hyperparameters are estimated jointly and reduce the problem dimensionality by introducing a functional relationship that we call the full maximum a posteriori (FMP) method. This method also eliminates the need for a true value of model parameters that caused identifiability issues in the KOH formulation. When the joint posterior is approximated as a mixture of Gaussians, the FMP calibration is proven to avoid some pitfalls of the KOH calibration, namely missing some probability regions and underestimating the posterior variance. We then illustrate two numerical examples where both model error and measurement uncertainty are estimated together. Using the solution to the full Bayesian problem as a reference, we show that the FMP results are accurate and robust, and avoid the need for high-dimensional Markov chains for sampling.

History of the development of the hydrodynamic model of the open pit mine “Drmno”, Serbia

With the creation of a hydrodynamic model, the need for it usually ceases until some next moment when the model needs to be updated and used for the set task for the necessary project documentation or dedicated studies. It is relatively rare that almost every year the model is improved and used for forecast calculations. The paper presents the basic characteristics and development of the hydrodynamic model of the open pit mine “Drmno” for 25 years. This open pit mine is the second-largest mine in Serbia. At the time the model was made (1998), it was the largest and most complex model that was made in the former Yugoslavia. Also, advanced model calibration techniques were used for the first time in Serbia

Psychometric properties of the mock interview rating scale for autistic transition-age youth.

Employment is a major contributor to quality of life. However, autistic people are often unemployed and underemployed. One potential barrier to employment is the job interview. However, the availability of psychometrically-evaluated assessments of job interviewing skills is limited for autism services providers and researchers. We analyzed the psychometric properties of the Mock Interview Rating Scale that was adapted for research with autistic transition-age youth (A-MIRS; a comprehensive assessment of video-recorded job interview role-play scenarios using anchor-based ratings for 14 scripted job scenarios). Eighty-five transition-age youth with autism completed one of two randomized controlled trials to test the effectiveness of two interventions focused on job interview skills. All participants completed a single job interview role-play at pre-test that was scored by raters using the A-MIRS. We analyzed the structure of the A-MIRS using classical test theory, which involved conducting both exploratory and confirmatory factor analyzes, Rasch model analysis and calibration techniques. We then assessed internal consistency, inter-rater reliability, and test-retest reliability. Pearson correlations were used to assess the A-MIRS' construct, convergent, divergent, criterion, and predictive validities by comparing it to demographic, clinical, cognitive, work history measures, and employment outcomes. Results revealed an 11-item unidimensional construct with strong internal consistency, inter-rater reliability, and test-retest reliability. Construct [pragmatic social skills (r = 0.61, p < 0.001), self-reported interview skills (r = 0.34, p = 0.001)], divergent [e.g., age (r = -0.13, p = 0.26), race (r = 0.02, p = 0.87)], and predictive validities [competitive employment (r = 0.31, p = 0.03)] received initial support via study correlations, while convergent [e.g., intrinsic motivation (r = 0.32, p = 0.007), job interview anxiety (r = -0.19, p = 0.08)] and criterion [e.g., prior employment (r = 0.22, p = 0.046), current employment (r = 0.21, p = 0.054)] validities were limited. The psychometric properties of the 11-item A-MIRS ranged from strong-to-acceptable, indicating it may have utility as a reliable and valid method for assessing the job interview skills of autistic transition-age youth.

An extensive mathematical approach for wind speed evaluation using inverse Weibull distribution

Wind velocity and potential are frequently estimated using probability density functions (PDFs) such as the Inverse Weibull and Rayleigh. Determining whether they are suitable for modelling observed distributions is crucial. Many wind energy systems require a thorough understanding of wind speed probability distributions. This research presents an extensive mathematical approach for wind speed evaluation using the inverse Weibull distribution (IWD) and a modified maximum likelihood function. IWD is a valuable tool for modelling wind speed characteristics, providing insights into the PDF and cumulative distribution function (CDF) of wind speeds. The suggested modified maximum likelihood (MML) function includes tweaks and improvements to increase parameter estimate accuracy in the IWD. The IWD is computed using the MML function, which considers data features, outliers, and unique research demands. Model calibration techniques, including goodness-of-fit assessment, sensitivity analysis, and error analysis, are employed to refine and assess the model’s accuracy. The developed mathematical model based on the inverse Weibull distribution and modified maximum likelihood function offers a robust approach for wind speed evaluation and analysis, contributing to renewable energy resource assessment, wind farm design, and structural engineering applications.

Calibrating Distribution Models from PELVE

The Value at Risk (VaR) and the expected shortfall (ES) are the two most popular risk measures in banking and insurance regulation. To bridge between the two regulatory risk measures, the probability equivalent level of VaR-ES (PELVE) was recently proposed to convert a level of VaR to that of ES. It is straightforward to compute the value of PELVE for a given distribution model. In this article, we study the converse problem of PELVE calibration; that is, to find a distribution model that yields a given PELVE, which may be obtained either from data or from expert opinion. We discuss separately the cases when one-point, two-point, n-point, and curve constraints are given. In the most complicated case of a curve constraint, we convert the calibration problem to that of an advanced differential equation. We apply the model calibration techniques to estimation and simulation for datasets used in insurance. We further study some technical properties of PELVE by offering a few new results on monotonicity and convergence.

Can hydrological models assess the impact of natural flood management in groundwater‐dominated catchments?

AbstractNatural flood management (NFM) is widely promoted for managing flood risks but the effectiveness of different types of NFM schemes at medium (100–1000 km2) and large scales (&gt;1000 km2) remains widely unknown. This study demonstrates the importance of fully understanding the impact of model structure, calibration and uncertainty techniques on the results before the NFM assessment is undertaken. Land‐based NFM assessment is undertaken in two medium‐scale lowland catchments within the Thames River basin (UK) with a modelling approach that uses the Soil and Water Assessment Tool (SWAT) model within an uncertainty framework. The model performed poorly in groundwater‐dominated areas (P‐factor &lt;0.5 and R‐factor &gt;0.6). The model performed better in areas dominated by surface and interflow processes (P‐factor &gt;0.5 and R‐factor &lt;0.6) and here hypothetical experiments converting land to broadleaf woodland and cropland showed that the model offers good potential for the assessment of NFM effectiveness. However, the reduction of large flood flows greater than 4% in medium‐sized catchments would require afforestation of more than 75% of the area. Whilst hydrological models, and specifically SWAT, can be useful tools in assessing the effectiveness of NFM, these results demonstrate that they cannot be applied in all settings.

A Literature Review on Train Motion Model Calibration

The dynamics of a moving train are usually described by means of a motion model based on Newton’s second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on- board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on- the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.

A probabilistic inverse prediction method for predicting plutonium processing conditions

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

Calibration of Cohorts of Virtual Patient Heart Models Using Bayesian History Matching

Previous patient-specific model calibration techniques have treated each patient independently, making the methods expensive for large-scale clinical adoption. In this work, we show how we can reuse simulations to accelerate the patient-specific model calibration pipeline. To represent anatomy, we used a Statistical Shape Model and to represent function, we ran electrophysiological simulations. We study the use of 14 biomarkers to calibrate the model, training one Gaussian Process Emulator (GPE) per biomarker. To fit the models, we followed a Bayesian History Matching (BHM) strategy, wherein each iteration a region of the parameter space is ruled out if the emulation with that set of parameter values produces is “implausible”. We found that without running any extra simulations we can find 87.41% of the non-implausible parameter combinations. Moreover, we showed how reducing the uncertainty of the measurements from 10 to 5% can reduce the final parameter space by 6 orders of magnitude. This innovation allows for a model fitting technique, therefore reducing the computational load of future biomedical studies.

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. In this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

An improved model and field calibration technique for measuring liquid water content in unfrozen and frozen soils with dielectric probes

AbstractDielectric soil moisture probes can be used to obtain frequently logged field observations of volumetric water content, but because this is an indirect method, it is challenging to ensure that you have a suitable calibration relationship and parameters. Challenges are associated with (a) limitations in the probes’ ability to accurately measure the soil bulk dielectric constant and (b) limitations in obtaining reliable direct observations to calibrate the probe against, due to the spatial heterogeneity in the field. Furthermore, in soils that freeze, we do not have a robust approach to account for the effect of ice on the instrument. In this study, we propose a calibration relationship for all dielectric probes that is physically based, parsimonious, accounts for errors in the bulk dielectric constant, and quantifies the uncertainty in the liquid water content caused by the presence of ice. We show that our relationship has a better performance and more realistic parameter values than existing approaches. To calibrate a dielectric probe in the field, we show that it is necessary to account for spatial heterogeneity in samples taken for calibration. There is value in taking simultaneous measurements of the bulk dielectric constant (using the same dielectric probe as is being calibrated) and gravimetric samples for water content at a number of points in space. Taking too few samples is likely to be misinformative—we show that it is better to not calibrate the probe at all than to calibrate it with too few data points.

The Relationship between the Caster Angle and the Steering Stability and the Measurement Method

The size of the front wheel caster angle of the vehicle is closely related to the stability of high-speed steering. By establishing the vehicle two-degree-of-freedom model and the steering system force input dynamic equation, the relationship calculation formula between the front wheel caster angle and the steering stable limit speed is derived by using the Routh-Hurwitz stability criterion. Taking the specific data of a certain model as an example, the range of speed values that can maintain stability is analyzed. The accuracy of the model will be further verified by means of simulation. This paper also summarizes the relevant model analysis and calibration techniques for the measurement of the back inclination of the main sales of automobiles, and can obtain more accurate experimental data by improving the accuracy of the measurement. Thus providing a guiding basis for the design of the rear inclination angle of the main sales of the car.

Automatic model calibration of combined hydrologic, hydraulic and stormwater quality models using approximate Bayesian computation

A range of automatic model calibration techniques are used in water engineering practice. However, use of these techniques can be problematic due to the requirement of evaluating the likelihood function. This paper presents an innovative approach for overcoming this issue using a calibration framework developed based on Approximate Bayesian Computation (ABC) technique. Use of ABC in automatic model calibration was undertaken for a combined urban hydrologic, hydraulic and stormwater quality model. The simulated runoff hydrograph and total suspended solid (TSS) pollutograph were compared with observed data for multiple events from three different catchments, and found to be within 95% confidence intervals of the simulated results. The R programmed model was validated by comparing simulated flow with similar commercially available modeling software, MIKE URBAN output determined using mean value of parameters obtained from the calibration exercise, and performed well by satisfying statistical criteria's such as coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME). The developed framework is useful for automatic calibration and uncertainty estimation using ABC approach in complex hydrologic, hydraulic and stormwater quality models with multi-input-output systems.

Model Comparison and Uncertainty Quantification in Tumor Growth

Mathematical and computational modeling have been increasingly applied in many areas of cancer research, aiming to improve the understanding of tumorigenic mechanisms and to suggest more effective therapy protocols. The mathematical description of the tumor growth dynamics is often made using the exponential, logistic, and Gompertz models. However, recent literature has suggested that the Allee effect may play an important role in the early stages of tumor dynamics, including cancer relapse and metastasis. For a model to provide reliable predictions, it is necessary to have a rigorous evaluation of the uncertainty inherent in the modeling process. In this work, our main objective is to show how a model framework that integrates sensitivity analysis, model calibration, and model selection techniques can improve and systematically characterize model and data uncertainties. We investigate five distinct models with different complexities, which encompass the exponential, logistic, Gompertz, and weak and strong Allee effects dynamics. Using tumor growth data published in the literature, we perform a global sensitivity analysis, apply a Bayesian framework for parameter inference, evaluate the associated sensitivity matrices, and use different information criteria for model selection (First- and Second-Order Akaike Information Criteria and Bayesian Information Criterion). We show that such a wider methodology allows having a more detailed picture of each model assumption and uncertainty, calibration reliability, ultimately improving tumor mathematical description. The used in vivo data suggested the existence of both a competitive effect among tumor cells and a weak Allee effect in the growth dynamics. The proposed model framework highlights the need for more detailed experimental studies on the influence of the Allee effect on the analyzed cancer scenario.

Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods

In this study, a grey box (GB) model for simulating internal air temperatures in a naturally ventilated nearly zero energy building (nZEB) was developed and calibrated, using multiple data configurations for model parameter selection and an automatic calibration algorithm. The GB model was compared to a white box (WB) model for the same application using identical calibration and validation datasets. Calibrating the GB model using only one week of data produced very accurate results for the calibration periods but led to inconsistent and typically inaccurate results for the validation periods (root mean squared error (RMSE) in validation periods was 229% larger than the RMSE in calibration periods). Using three weeks of data from varying seasons for calibration reduced the model accuracy in the calibration period but substantially increased the model accuracy and generalisation abilities for the validation period, reducing the mean RMSE by over 160%. The use of one week of data increased the standard deviation in parameter selections by over 40% when compared with the three-week calibration datasets. Utilising data from multiple seasons for calibration purposes was found to substantially improve generalisation abilities. When compared to the WB model, the GB model produced slightly less accurate results (mean RMSE of the GB model was 1.5% higher). However, the authors found that employing GB modelling with an automatic model calibration technique reduced the human labour input for simulating internal air temperature of a naturally ventilated nZEB by approximately 90%, relative to WB modelling using a manually calibrated approach.

Machine learning applied as an in-situ monitoring technique for the water content in oil recovered by means of NIR spectroscopy

The present work proposes a study on the combination of artificial neural network based model and multivariate calibration techniques, as principais component analysis (PCA) and partial least squares (PLS) combined with near infrared spectrophotometer (NIR) to formulate online water content monitoring models for recovered fuel oils after effluent treatment of a Brazilian thermoelectric plant. The database for adjustments of these models was built using oil samples supplied by the SUAPE II thermoelectric plant, where they were characterized in laboratories and analyzed via NIR. 450 spectra were used to construct the PLS model for predictive model calibration using the PLS technique and 118 spectra for the model based on artificiais neurais networks. In the obtained PLS model, it was possible to obtain correlations around 0.97, cross-validation error (RMSECV) of 0.0906 and test prediction error (RMSEP) of 0.0651. The RNA model presented coefficient of determination R² of 0.99 for training and R² of 0.98 for test with prediction error of 0.062

Nonparametric Penalized Spline Model Calibrated Estimator in Complex Survey with Known Auxiliary Information at Both Cluster and Element Levels

The present study uses penalized splines (p- spline) to estimate the functional relationship between the survey variable and the auxiliary variable in a complex survey design; where a population divided into clusters is in turn subdivided into strata. This study has considered a case of auxiliary information present at two levels; at both cluster and element levels. The study further applied model calibration technique by penalty function to estimate the population total. The calibration problems at both levels have been treated as optimization problems and solved using penalty functions to derive the estimators for this study. The reasoning behind model calibration is that if the calibration constraints are satisfied by the auxiliary variable, the study expects that the variable of interest’s fitted values meets such constraints. This study runs a Monte Carlo simulation to assess the finite sample performance of the penalized spline model calibrated estimator under complex survey data. Simulation studies were conducted to compare the efficiency of p-spline model calibrated estimator with Horvitz Thompson estimator (HT) by mean squared error (MSE) criterion. This study shows that the p-spline model-based estimator is generally more efficient than the HT in terms of the mean squared error. The results have also shown that the estimator obtained is unbiased, consistent and very robust because it does not fail if the model is misspecified for the data.

Virtual Network Resource Optimization Model for Network Function Virtualization

Network function virtualization (NFV) is a concept aimed at achieving telecom grade cloud ecosystem for new‐generation networks focusing on capital and operational expenditure (CAPEX and OPEX) savings. This study introduces empirical throughput prediction model for the virtual network function (VNF) and network function virtualization infrastructure (NFVI) architectures based on Linux kernel. The model arises from the methodology for performance evaluation and modeling based on execution area (EA) distribution by CPU core pinning. EA is defined as a software execution unit that can run isolated on a compute resource (CPU core). EAs are derived from the elements and packet processing principles in NFVIs and VNFs based on Linux kernel. Performing measurements and observing linearity of the measured results open the possibility to apply model calibration technique to achieve general VNF and NFVI architecture model with performance prediction and environment setup optimization. The modeling parameters are derived from the cumulative packet processing cost obtained by measurements for collocated EAs on the CPU core hosting the bottleneck EA. The VNF and NFVI architecture model with performance prediction is successfully validated against the measurement results obtained in emulated environment and used to predict optimal system configurations and maximal throughput results for different CPUs.

Parameters Estimation at Ungauged Catchments Using Rainfall-Runoff Model, Upper Tekeze Basin, Ethiopia

This study was conducted for parametres estimation and stream flow prediction at ungauged catchments on the case of Upper Tekeze basin, Ethiopia by using Rainfall-runoff model. In the basin, most of the catchments were ungauged. The basin has 9199km2 and 3638km2 gauged and ungauged catchments respectively. Rainfall and stream flow data were analyzed in the period of 1992-2006 and 1992-20006, respectively. Parameters calibrated for gauged catchments were extrapolated to ungauged catchments on the base of similar physical catchment characteristics using regionalization techniques. Regionalization methods such as multiple linear regression, spatial proximity, sub basin mean and area ratio were applied to transfer model parameters values from gauged to ungauged catchments. For this study seven gauged catchments were satisfied objective functions in the calibrated and validation period, for example in Gheba catchment Nash-Sutcliffe model efficiency coefficient (NSE), relative volume error (RVE) and coefficient of determination (R2) were, 0.81, -4.25, 0.77 and 0.71, 5.5, 0.74 respectively. Stream flow simulation at ungauged catchments by using spatial proximity and sub basin mean method were contributing high runoff volume compare to other methods. The result for this study shows that the Key model parameters like runoff coefficient (Beta), recession coefficient of upper reservoir zone (Khq), Limit for evapotranspiration (Lp), field capacity (Fc), percolation (Perc) as defaulting value when applying HBV-96 model to the future regionalization studies. Model parameters were calibrated manually by try and error rules, however it was tidies therefore more creative automatic model calibration techniques could be useful for upcoming studies. Thus, Current and future water resources development endeavors may use apply such discharge data for planning and design purposes.