Calibration Approach Research Articles

Baseline-dependent improvement in CF studies, plausibility of bias

Background:It has been commonly reported that therapeutic treatments in cystic fibrosis (CF) have ceiling effects, such that their efficacy is diminished for persons with high pre-treatment health (Montgomery et al., 2012 and Newsome et al., 2019). Floor effects have also been reported where decline is of lower magnitude in those with below-average pre-treatment health (Harun et al., 2016; Konstan et al., 2012 and Szczesniak et al., 2017). When measurement error is present, the statistical literature has warned of exaggerated or spurious associations between pre-treatment measures and subsequent change (Chambless and Davis, 2003 and Yanez et al., 1998). Measurement error, equivalently described as day-to-day variation, has been described to occur in CF outcome measurements such as forced expiratory volume in 1 s taken by spirometry (FEV1pp) (Magaret et al., 2024; Stanojevic et al., 2020 and Thornton et al., 2023). Methods:We conducted a simulation study to assess the potential for spurious floor or ceiling effects in studies of CF therapeutics. We considered uncontrolled or single-arm studies, and evaluated estimated association between pre-treatment FEV1pp and treatment-induced change: post- versus pre-treatment. Results:When day-to-day variation was present in FEV1pp, at levels equivalent to those reported in large studies measuring spirometry both at home and in clinic, naive analytic approaches found spurious associations of change with baseline (Paynter et al., 2022 and Saiman et al., 2003). Type I error ranged from 31.9% to 98.3% for day-to-day variation as high as 3% to 15% relative to biological variation. Incorporating known day-to-day variation, the regression calibration approach corrected bias and controlled type I error (Chambless and Davis, 2003). Conclusion:Exaggerated ceiling effects are possible. Further studies could provide meaningful confirmation of ceiling effects in CF, perhaps reducing day-to-day variation by incorporating multiple pre- and post-treatment measurements.

A Simplified Calibration Procedure for DEM Simulations of Granular Material Flow

The discrete element method (DEM) has emerged as an essential computational tool in geotechnical engineering for the simulation of granular materials, offering significant advantages over traditional continuum-based methods such as the finite element method (FEM) and the finite difference method (FDM). The DEM’s ability to model particle-level interactions, including contact forces, rotations, and particle breakage, allows for a more precise understanding of granular media behavior under various loading conditions. However, accurate DEM simulations require meticulous calibration of input parameters, such as particle density, stiffness, and friction, to effectively replicate real-world behavior. This study proposes a simplified calibration procedure, intended to be conducted prior to any granular material flow DEM modeling, based on three fundamental physical tests: bulk density, surface friction, and angle of repose. The ability of these tests, conducted on dry quartz sand, to accurately determine DEM micromechanical parameters, was validated through numerical simulation of cylinder tests with varying height-to-radius ratios. The results demonstrated that this calibration approach effectively reduced computational complexity while maintaining high accuracy, with validation errors of 0% to 12%. This research underscores the efficacy of simplified DEM calibration methods in enhancing the predictive reliability of simulations, particularly for sand modeling in geotechnical applications.

Hall thruster model improvement by multidisciplinary uncertainty quantification

We study the analysis and refinement of a predictive engineering model for enabling rapid prediction of Hall thruster system performance across a range of operating and environmental conditions and epistemic and aleatoric uncertainties. In particular, we describe an approach by which experimentally-observed facility effects are assimilated into the model, with a specific focus on facility background pressure. We propose a multifidelity, multidisciplinary approach for Bayesian calibration of an integrated system comprised of a set of component models. Furthermore, we perform uncertainty quantification over the calibrated model to assess the effects of epistemic and aleatoric uncertainty. This approach is realized on a coupled system of cathode, thruster, and plume models that predicts global quantities of interest (QoIs) such as thrust, efficiency, and discharge current as a function of operating conditions such as discharge voltage, mass flow rate, and background chamber pressure. As part of the calibration and prediction, we propose a number of metrics for assessing predictive model quality. Based on these metrics, we found that our proposed framework produces a calibrated model that is more accurate, sometimes by an order of magnitude, than engineering models using nominal parameters found in the literature. We also found for many QoIs that the remaining uncertainty was not sufficient to account for discrepancy with experimental data, and that existing models for facility effects do not sufficiently capture experimental trends. Finally, we confirmed through a global sensitivity analysis the prior intuition that anomalous transport dominates model uncertainty, and we conclude by suggesting several paths for future model improvement. We envision that the proposed metrics and procedures can guide the refinement of future model development activities.

European croplands under climate change: Carbon input changes required to increase projected soil organic carbon stocks

Increasing soil organic carbon (SOC) stocks in agricultural systems is a pivotal strategy for promoting soil health and mitigating climate change. Global initiatives have set ambitious targets, aspiring to achieve an annual SOC stock increase of 4 ‰. In the European Union, the recently approved Nature Restoration Law aims to increase SOC stock trends in the top 30 cm of cropland mineral soils. However, current monitoring and reporting practices in some countries rely on simplistic SOC models with default parameters, which may not provide reliable predictions.In this paper, we study the feasibility of a 4 ‰ target in European croplands (i.e., an aspirational target proposed by The international “4 per 1000” Initiative), through estimations of required C input changes. To ensure robust predictions, we propose a novel calibration approach that links model parameters to pedo-climatic variables via statistical relationships from 16 long-term experiments. The effectiveness of the method is evaluated for three SOC models across 4281 sites from the European LUCAS soil survey.Our findings demonstrate that the statistical calibration of the multi-model ensemble improves the accuracy of 2015 and 2018 SOC stock predictions, compared to default parameterization. This improvement was however mainly due to the substantial enhancement of one of the models. According to the weighted multi-model mean, median C input changes to reach a 4 ‰ target for Northern, Central, and Southern Europe stand at 1.85, 1.20, and 0.13 Mg C ha−1 yr−1 under RCP 2.6, and 2.21, 1.26, and −0.10 Mg C ha−1 yr−1 under RCP 6.0, respectively.To achieve the aspirational 4 ‰ target, estimated C input change requirements exceed the predicted changes in net primary productivity under RCP 2.6 and RCP 6.0. This emphasizes the importance of strategic land-use and land-management interventions to enhance SOC stocks.

A General Method for Calibration of Active Scanning Thermal Probes

Scanning thermal microscopy (SThM) is a scanning probe technique aimed at quantitative characterization of local thermal properties at the length scale down to tens of nanometers. With many probe designs and approaches to interpretation of probe responses, there is a need for a universal framework, which would allow probe calibration and comparison of probe performance. Herein, a calibration framework based on an abstracted, formal, probe model for active SThM probes is developed. The calibration can be accomplished through measurements with two or three calibration samples. Requirements for calibration samples are described with examples of structures of suitable samples identified in published literature. A link to a published experimental work indirectly verifying the proposed procedure is provided. The calibration does not require knowledge of internal probe properties and yields a small and universal set of parameters that can be used to quantify thermal resistance presented to the probe by samples as well as to characterize active‐mode SThM probes of any type and at any measurement frequency. How the probe calibration parameters can be used to guide probe design is illustrated. When the calibration approach can be used directly to measure the thermal conductivity of unknown samples is also analyzed.

EMG model calibration and trajectory tracking of rehabilitation exoskeleton by asynchronous tele-training

ABSTRACT This study presents a novel kinematic tracking model, designed for a networked exoskeleton system that is asynchronously taught by a remote therapist. The therapist’s rehabilitation exercises are quantitatively assessed using a monocular vision system. The resultant metrics are then transmitted asynchronously over the network to patients equipped with exoskeletons. The exoskeleton utilises these metrics as reference paths for exercises, complemented by electromyography (EMG) feedback. This work introduces a calibration approach aimed at estimating angular positions by utilising EMG observations. The calibration model establishes real-time correlations between polynomial reference positions. We further explore redundant kinematics, incorporating an EMG observer for linear, time-variant rehabilitation tracking control. Our methodology is validated using vision-based metric data and experimental EMG measurements including shoulder flexion, elbow flexion, and rowing-like movements. Computer simulations demonstrate the system’s ability to reliably, robustly, and effectively follow desired trajectories. This research offers a promising approach for remote personalized rehabilitation.

Shortened and simplified traceability chain for dimensional metrology based on self-traceable standards

Abstract Nanoscale measurement is an essential task of nanomanufacturing, and measurement traceability is a fundamental aspect of nanoscale measurement. High-precision nanoscale measurement instruments (e.g. atomic force microscopes (AFM) and scanning electron microscopes (SEM)) need to be calibrated by traceable standards to ensure their accuracy and reliability. However, due to the suboptimal accuracy, uniformity, and consistency of existing standards, they need to be calibrated by metrological instruments traceable to primary length standards (e.g. physical wavelength standards) before use. This results in a long traceability chain that leads to error accumulation and significantly reduces calibration efficiency. This paper proposes a novel shortened and simplified traceability chain, where the physical wavelength standard corresponding to the 7S3 → 7P4° transition frequency of chromium atoms is materialized into self-traceable gratings using the atom lithography technology. The self-traceable gratings can then be directly applied for calibrating measurement instruments. To verify this approach, the self-traceable gratings are calibrated using a metrological AFM of the Physikalisch-Technische Bundesanstalt. Measurement results confirmed the feasibility of the approach. Particularly, our results show that the self-traceable gratings have excellent uniformity over different measurement areas and consistency over different samples, both at 0.001 nm level. Finally, the application of the self-traceable gratings for the calibrations of a commercial AFM and SEM is demonstrated. The new traceability chain significantly simplifies the calibration process, providing a more reliable and higher efficient calibration approach for advanced nanomanufacturing than that of the state-of-the-art.

Traceable characterisation of fibre-coupled single-photon detectors

Abstract The detection of single photons plays an essential role in advancing single-photon science and technologies. Yet, within the visible/near-infrared spectral region, accurate fibre-based optical power measurements at the few-photon level are not yet well-established. In this study, we report on a fibre-based setup, enabling traceable optical power measurements at the few-photon level in this spectral region. The setup was used to calibrate the detection efficiency (DE) of four single-photon avalanche diode (SPAD) detectors. The relative standard uncertainties on the mean DE values obtained from repeat fibre-to-detector couplings ranged from 0.67% to 0.81% (k = 2). However, the relative standard deviation of DE values, which ranged from 1.38% to 3.20% (k = 2), poses a challenge for the metrology of these devices and applications that require high accuracy and repeatability. We investigated the source of these variations by spatially mapping the response of a detector’s fibre connector port, using a focused free-space beam, allowing us to estimate the detector’s spatial non-uniformity. In addition, we realise a novel calibration approach for fibre-coupled SPADs in a free-space configuration, enabling a direct comparison between the fibre-based setup and the National Physical Laboratory’s established free-space facility using a single SPAD. Finally, we investigated alternative coupling methods, testing the repeatability of different fibre-to-fibre connectors in addition to direct fibre-to-detector couplings: SPADs from three manufacturers were tested, with both single-mode and multi-mode fibre.

Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencoder, an unsupervised machine learning model consisting of one encoder and one decoder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements. We also introduce the denoising autoencoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California. It turns out that our approach can achieve comparable performance to the-state-of-the-art calibration methods given normal data and outperform them given corrupted data with missing values. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This study was supported by the National Science Foundation [Grant CMMI-1949710] and the C2SMART Research Center, a Tier 1 University Transportation Center.

A Bayesian optimization-genetic algorithm-based approach for automatic parameter calibration of soil models: Application to clay and sand model

This paper develops a novel approach for automatic parameter calibration of soil constitutive models by combining Bayesian optimization (BO) and genetic algorithm (GA). The BO method enables locating the possible point over a large multi-parameter search domain quickly, by which the search domain is no longer required to be accurately pre-defined. Thereafter, the GA is employed to find the best solution within the BO-optimized search space. This calibration procedure can quickly give the best-matched model parameters for both sands and clays even with the same large search space. Taking a critical state-based soil model (Clay And Sand Model (CASM)) as an example, the superiority and capability of the proposed calibration method are examined by comparing experimental data with predicted results for both clays and sands. It is shown that the BO-GA-based approach can reduce errors by 46.4% for clays and 41.2% for sands compared with the conventional GA method and outperforms other traditional optimization algorithms. Eventually, the evolution process of BO and GA is visualized to better understand the principles of automatic parameter calibration, which is followed by model uncertainty analyses.

The Wheel–Rail Contact Force for a Heavy-Load Train Can Be Measured Using a Collaborative Calibration Algorithm

The wheel–rail contact force is a crucial indicator for ensuring the secure operation of a heavy-load train. However, obtaining the real-time wheel–rail contact force of a heavy-load train is a challenging task as, due to safety considerations, it is not possible to install instrumented wheelsets on heavy-load trains. This work presents a novel approach to quantify the wheel–rail contact force of a heavy-load train, utilizing a cooperative calibration methodology. First, a ground measurement platform for the wheel–rail contact force of a heavy-load train is constructed on a selected rail section. The railway inspection car’s wheel–rail contact force measurement system is fine-tuned using a multilayer perceptron calibration approach, and the ground platform then uses the results to fine-tune the railway inspection car’s examined wheelset. Second, based on actual measured data on the wheel–rail contact force of a heavy-load train, and using the golden jackal optimization algorithm, the multilayer perceptron correction approach is employed to create a data relationship mapping model. This model correlates the corrected data on the wheel–rail contact force obtained from the railway inspection car with the wheel–rail contact force of a heavy-haul train with an axle load of 25 tons, and the precision of the mapping is guaranteed. Finally, by combining the wheel–rail contact force correction method for the railway inspection car and the contact force mapping model between the railway inspection car and the heavy-load train, collaborative calibration of the wheel–rail contact force of the heavy-load train is realized. The experimental results under two working conditions show that this method can realize high-precision, real-time measurement of the wheel–rail contact force of a heavy-load train. For the working condition of a straight-line section, the calibration error was within 1.593 kN, and the MAPE was 0.105%; for the working condition of a curved-line section, the calibration error was 2.344 kN, and the RMSE was 184.72 N.

Cyclic mechanical response of LPBF Hastelloy X over a wide temperature and strain range: Experiments and modelling

Additive manufacturing (AM) of high-temperature alloys through processes such as laser powder bed fusion (LPBF) has gained significant interest and is rapidly expanding due to its exceptional design freedom, which enables the fabrication of complex parts that contribute to the increased efficiency of aerospace and energy systems. The materials produced through this process exhibit unique microstructures and mechanical properties, which necessitate dedicated study and characterization. In this context, our research focuses on the experimental characterization of the isothermal cyclic viscoplastic mechanical response of Hastelloy X (HX) over the temperature range of 22 to 1000 °C and at various strain rates, addressing a current gap in the literature. Recognizing the need for material models that can accurately represent the cyclic mechanical response of LPBF HX across a broad temperature range, we developed a robust extension of the viscoplastic isotropic-kinematic hardening Chaboche model, intended for applications in the thermomechanical simulation of the LPBF process for the analysis of residual stress and distortion, as well as for assessing the mechanical integrity of LPBF components. The extension involves expressing the entire set of model parameters explicitly with analytical functions to account for their temperature dependence. Consequently, the model includes a relatively large number of parameters to represent the isotropic-kinematic hardening viscoplastic response of the alloy over a wide temperature range, and hence to overcome the endeavor of its systematic calibration, a dedicated calibration approach was introduced. The model ultimately demonstrated its capability to precisely represent the isothermal response of the alloy over the examined temperatures and strain rates. To evaluate the model’s predictiveness for non-isothermal conditions, out-of-phase thermomechanical cyclic experiments were also conducted as independent benchmark tests, where the model’s predictions were fairly consistent with the experimental results. As a part of this study, the derived material model has been integrated into the UMAT subroutine, complete with an analytical derivation of the consistent Jacobian matrix.

Determination of electron‐hole pair creation energy in Cd0.9Zn0.1Te0.98Se0.02 quaternary semiconductor for room‐temperature gamma‐ray detection

AbstractWe report the first‐time measurement of the electron‐hole pair (ehp) creation energy (Wehp) in novel Cd0.9Zn0.1Te0.98Se0.02 (CZTS) quaternary semiconductor. CZTS in single crystalline form is poised to be the future of large‐volume room‐temperature gamma‐ray detectors due to its excellent compositional homogeneity with highly reduced defects, high‐Z (atomic number) constituents, wide bandgap (1.6 eV), and superior charge transport properties. Despite a great deal of study of the material and device properties since its inception, the Wehp in CZTS has not been measured experimentally. Accurate determination of Wehp is essential for calibration of the spectrometer and other theoretical calculations. In this study we have used an absolute calibration approach, which is based on an iterative approach that yields the Wehp as the best‐fit parameter. Using a 241Am alpha emitting radioisotope and a planar CZTS detector, the Wehp in CZTS was calculated to be 4.47 eV. The obtained value has been validated by accurately predicting the peak energy for gamma rays emitted by a 137Cs source and read by a CZTS detector with different dimensions. The dependences of the calculated Wehp value on the detector dimensions, type of interaction, and effect of charge trapping are also discussed.

Self-calibration method for geometric parameters based on feature point projection trajectories in Cone-beam Computed Laminography

Cone-beam computed laminography (CBCL) is an X-ray three-dimensional imaging method that is suitable for large plate-like objects. Geometric parameter calibration is crucial during CL imaging to prevent geometric artifacts in the reconstructed image. This study introduced a self-calibration method employing feature-point projection trajectories for CL geometric parameters. The method constructed point projection trajectory models for the system’s geometric parameters and optimized the best fit of these models to the characteristic point projection trajectories to solve for the geometric parameters of the system. Furthermore, this study examined the impact of the feature point location in the object coordinate system on the parameter calibration accuracy and reduced the required feature point locations from 11°to 4°using the alternating optimization algorithm. The imaging results demonstrated the ability of the method to achieve high-accuracy calibration of CL system geometric parameters, reducing the calibration error of the rotational axis tilt angle α from 1.1% to 0.08% while exhibiting resilience to noise. The proposed method enabled the calibration of all CL system geometric parameters without the model calibration, laying the groundwork for rectifying image geometric artifacts and enhancing CL imaging quality. Moreover, this method presents a novel approach for CT system geometric parameter calibration.

New Numerical Technique for the Inbuilt Modified Cam Clay Constitutive Model in FLAC3D

Fast Lagrangian Analysis of Continua (FLAC) is a powerful numerical platform for simulating the deformation and stability of geotechnical structures. Presently, it is documented that the embedded Modified Cam Clay constitutive model in the FLAC platform is deficient in solving geotechnical problems. This paper outlines the new calibration approach by determining the isotropic preconsolidation pressure multiplier based on the relationship between the total stress in the geotechnical problem and the reference pressure. The multiplier was used to establish the nominated maximum isotopic preconsolidation pressure and maximum referential consolidation ratio at the bottom of the soil layer. Subsequently, the maximum isotopic preconsolidation pressure was used to determine the bulk maximum. In addition, the developed approach was used to investigate the lateral displacement of the large grid wall foundation by deep mixing under the adjacent surcharge loading. The feasibility was validated using the centrifuge results. The coefficient of determination between lateral displacement from numerical and centrifuge modelling R2 was 0.95. The proposed numerical technique and calibration approach demonstrated that the embedded Modified Cam Clay model can be used to solve practical geotechnical problems.

Empirical and physical modelling of soil erosion in agricultural hillslopes

Abstract Soil erosion is a complex and highly heterogeneous process with a wide range of environmental and economic impacts. Its estimation is particularly challenging and modelling is typically used for erosion estimation over large areas. The aim of this study was to compare the two leading empirical and physical erosion estimation models, i.e. the Revised Universal Soil Loss Equation (RUSLE) and the Water Erosion Prediction Project (WEPP). The models were calibrated and validated using data collected from field experiments conducted in agricultural lands of Mexico. The simulated rainfall experiments involved measuring erosion from field plots subjected to four tillage systems (No crop, Conventional tillage, Conventional tillage + residues, and Handspike) under two antecedent soil moisture conditions (dry and wet). Different calibration approaches based on the factors K and C for RUSLE, and interrill erodibility and hydraulic conductivity in WEPP were tested. The best-performing methods in RUSLE involved measuring the K factor and adopting the recommended C factor by the National Forestry Commission of Mexico. In WEPP, the best results were obtained when interrill erodibility was estimated from experimental measurements. Overall, RUSLE outperformed WEPP in most of the treatments except for CT under WAMC.

Pipe roughness calibration approach for water distribution network models using a nonlinear state observer

This paper introduces an online approach based on a nonlinear state observer (NSO) to calibrate the roughness of each pipe within a water distribution network (WDN) or a sector thereof. The NSO is designed to obtain the estimations of pipes' friction factors, which are then used to estimate the roughness. The core of the NSO is a dynamic WDN model formulated through a structured set of ordinary differential equations derived from fundamental physical principles and taking advantage of both graph theory and rigid water column theory. By applying a coordinate transformation, the WDN model is represented as a fully connected network of damped nonlinear oscillators, with each oscillator formulated as a Liénard system. This representation allows for estimating the friction factors for each pipe using only flow rate information. The proposed approach facilitates a continuous calibration when hydrodynamic data are readily accessible, which is a capability that empowers engineers to enhance, concurrently or proactively, the day-to-day operations of water distribution networks, such as control or diagnose tasks, whenever data are available. The results of numerical simulations are presented to illustrate the practical utility of the proposed method.

A flexible and efficient calibration method for discrete element simulations of additive manufacturing in construction

Extrusion-based 3D concrete printing (E3DCP), as one of the most advanced Additive Manufacturing processes, is a hotspot in the construction industry. Using the Discrete Element Method (DEM), the E3DCP can be investigated with reduced prototyping- and trial costs. However, DEM parameter calibration is challenging in the accurate simulation of construction materials because they are hardly typical granular matters. In addition, the introduction of a novel extrusion-based process Near-Nozzle-Mixing calls for a more flexible, efficient, and reliable calibration procedure due to the useable wide variety of material models.In this study, DEM simulation parameters of mortar and its components (e.g. Poraver® and paste) were thoroughly calibrated based on the central composite design method. Notably, the specific shape indices of related substrates were identified in the study to achieve the precise calibration of Poraver® and paste. Moreover, the European standard-based Haegermann flow table test was applied in the calibration by quantifying mortar consistency. This work provides a holistic and efficient calibration approach for three typical and physically different construction materials, which can be universally explored in the DEM simulation of most materials in E3DCP.

Calibration approach-based estimator of population total under successive sampling design

The present article deals with the estimation of the finite population total on two occasions using a calibration approach. The estimators from different approaches on two successive occasions using calibration technique are obtained. Some of the properties of proposed estimators are also provided, including optimum composite weights and their sensitivity interval. An empirical study has been carried out using real data for numerical illustration to show the efficiency of the proposed calibrated estimator. In addition, a simulation study is also performed where proposed estimators are found to be more efficient than the other existing traditional estimators for different choices of tuning weights and composite weights under various sample proportions.

Bayesian calibration and uncertainty quantification of a rate-dependent cohesive zone model for polymer interfaces

This study presents a rate-dependent cohesive zone model for the fracture of polymeric interfaces and performs a Bayesian calibration, an uncertainty quantification, and a sensitivity analysis for the model. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. Inverse calibration of parameters for such complex models through trial and error is challenging due to the large number of parameters of the model. Moreover, the calibrated parameter values are often non-unique and uncertain when the available experimental data is limited. To tackle this challenge, we employ a Bayesian calibration approach to identify parameters from experimental data, the resulting parameters significantly enhance the accuracy of the model. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. The advantages of the Bayesian calibration over a deterministic parameter fit are demonstrated. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced, which significantly improves the model’s prediction. Finally, the total uncertainty of the model is quantified in a predictive setting. A sensitivity analysis is performed to assess how changes in the input variables of the model affect the peak load, facilitating the identification of a concise set of highly influential parameters. The present approach can be used for calibration and uncertainty quantification for other complex computational mechanics models. It should also facilitate the designing of interface materials under uncertainty.