Model Building Research Articles

FOSSIL FUEL EMISSION PREDICTION SYSTEM USING MULTIPLE KERNEL GAUSSIAN PROCESS

<p>According to the United States Environmental Protection Agency, 81% of greenhouse gas emission is due to Carbon-dioxide. When fossil fuels, solid waste, trees, and wood products are burnt, Carbon-dioxide emission occurs. The concentration of Carbon-dioxide in the atmosphere is reduced when it is absorbed by plants as a part of the biological carbon cycle. The major sources of CO2 emission are from fossil fuel such as coal, natural gas, oil, cement production, gas flares used in industrial plants and bunker fuels used in ships. Increase in CO2 emission leads to the increase in global warming. Climate change, change in seasonal events and decrease in agricultural productivity are the major impacts of global warming. Hence it is important to study and analyse CO2 emission based on the recorded data across the globe. Further the major source of emission must be detected and alert messages must be sent to the pollution control boards in various countries to take necessary remedial actions. This work focuses on predicting the CO2 emission in the near future in various countries based upon the “Fossil-Fuel CO2 Emissions by Nation” dataset recorded by Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory. This system comprises of data cleaning, data normalization, optimization, and model building. The model built using Multiple Kernel Gaussian Process (MKGP) will predict the concentration of CO2 that may be present in the atmosphere in upcoming years. Based on the prediction the major source of CO2 emission is identified. We have studied the effects of Radial Basis Function kernel, Rational Quadratic, Periodic kernel and combinations of the kernels on India USA and China dataset. We have proposed a Multi-Kernel Gaussian Process for predicting the fossil fuel emission (MKGP - FFE). It has been inferred that combination of kernels performed well when compared to individual kernels in most of the cases.</p>

Analysis of Scattering Mechanisms in SAR Image Simulations of Japanese Wooden Buildings Damaged by Earthquake

The difficulty in identifying collapsed houses and damaged structures in synthetic aperture radar (SAR) images after natural disasters represents a significant challenge in the monitoring of urban structural deformation using SAR. SAR image simulation was conducted on a three-dimensional model of a typical wooden building in Japan to analyze the scattering mechanism of the structure in collapsed and uncollapsed states. Based on the physical properties of the buildings, a correlation was established between the simulated SAR image feature signals and the geometric structures of the buildings. The findings indicate that SAR scattering is more uniform for uncollapsed structures, which is predominantly influenced by their geometry. At low incidence angles, single reflections were the predominant phenomenon, whereas at high incidence angles, multiple reflections became more prevalent. The uncollapsed building’s facade formed a dihedral angle, exhibiting bright lines in the SAR image. Multiple reflections occurred at the edges of the building and floor junctions. These findings follow the theoretical predictions. In the case of the collapsed buildings, multiple reflections occurred with greater frequency, and irregular scattering was observed. Notwithstanding the augmented scattering pathways, some walls nevertheless manifested single reflections. The collapsed structures demonstrated a reduced sensitivity to alterations in the angle of incidence.

Community‐Driven Health Promotion: Evaluation of a Rural Microgrant Program

ABSTRACTIntroductionMicrogrants for health promotion have the potential to engage communities in designing and implementing place‐based interventions to improve health and social outcomes. However, the evidence base around microgrants for health promotion is limited. This study presents the evaluation of a health service‐funded microgrant program for health promotion in rural Australia.MethodsThe evaluation framework was developed through a participatory approach and involved collaborative logic model building and co‐prioritized evaluation questions with health service staff and grant recipients. Evaluation questions focused on participation, short‐term outcomes, and the perceived value of program activities. Qualitative methods (reflexive thematic analysis) were used to answer the evaluation questions. Data sources included semi‐structured interviews with grantees (n = 11) and the health service's health promotion team (n = 4), electronic field notes kept by the health promotion team (n = 50 documents), electronic progress reports completed by grantees (n = 6) and information and feedback forums (n = 2).ResultsSince the program's inception in 2019, the health service has received 22 grant applications of which 15 were approved and 14 disbursed. Evaluation results show that grantees were community members with multiple roles, often with previous experience in applying for grants. Expected outcomes have been partially met, especially with regard to the program's aim of community empowerment. The most notable impact was the enhancement of participants' perception of and relationship with the health promotion team, as well as the creation of opportunities for community members such as exposure to art and bridging of social groups.ConclusionMicrogrants represent a feasible way to increase health opportunities and foster community participation in the planning and delivery of health promotion programs. The key program activities identified and suggested improvements can help guide program replication and adaptation by other small organizations.Public ContributionCommunity members who had previously received a health service grant were invited to participate in collaborative workshops and follow‐up surveys to codesign the grant program evaluation framework, co‐prioritize evaluation questions and guide the results' dissemination plan.

Open-Source Software for Building-Integrated Photovoltaic Tiling for Novelty Architecture

Novelty architecture buildings can be tiled with conventional rectangular solar photovoltaic (PV) modules with both close-packed cells or partially transparent modules, vastly increasing renewable energy, reducing carbon emissions, and allowing for positive energy buildings. To enable this potential, in this study, for the first time, two open-source programs were developed and integrated to provide a foundation for designing and coating real-life novelty architecture buildings and objects with solar PV modules. First, a tiling algorithm was proposed and integrated into Blender that can generate solar PV modules on the face of any 3D model, and an augmented Python version of SAM was developed to simulate the performance of the resultant irregularly shaped PV systems. The integrated open-source software was used to analyze the energy performance of seven different novelty BIPVs located across the globe. The buildings’ energy performance was compared to conventional ground-based PV systems, and the results showed that the conventional arrays generate more energy per unit power than the BIPVs. The analysis reveals that the more complex the building model geometry, the less energy the building generates; however, the novelty BIPV power and energy densities far surpass conventional ground-based PV. The real estate savings observed were substantial, reaching 170% in one case where the BIPV reached 750 m in height. The BIPVs’ energy production is optimized by orienting the building via rotation and only needs to be carried out a single time for replication anywhere globally. The results show that the energy yield of the BIPV increases as the building becomes more detailed while the total power and energy decrease, indicating the need for the careful balancing of priorities in building design. Finally, the energy simulations demonstrate the potential for net-positive energy buildings and contribute to net-zero-emission cities. The findings indicate that BIPVs are not only appropriate for conventional residential houses and commercial buildings, but also for historical building replicas or monuments in the future. Further studies are needed to investigate the structural, electrical, and socio-economic aspects of novelty-architecture BIPVs.

Review on the Application of Living PSA in Nuclear Power

With the increasing standards of safety management in nuclear power plants, Living Probabilistic Safety Assessment (Living PSA) technology has begin to play an increasingly important role in their operation. This paper aims to provide an overview of the application and development of Living Probabilistic Safety Assessment (Living PSA) technology in nuclear power plant safety monitoring and risk assessment, examining the key technologies and future challenges. Initially, we summarize the current safety needs in regard to nuclear power, examine the policy on configuration risk management technology for nuclear power plants, and outline its importance and development process in nuclear power plant safety management. Subsequently, we discuss the basic principle of Living PSAs and the working method of risk monitoring based on Living PSAs, including information monitoring data collection, online identification, real-time model updating, and risk calculation. Within the Living PSA framework, model development is not merely about creating a theoretical or static representation; it is a dynamic and ongoing process that involves a deep understanding and precise simulation of the behavior of nuclear power plant systems and components. This represents the main research efforts in Living PSAs at present. Additionally, this paper identifies the key technologies of Living PSAs in an in-depth manner, such as the reliability-model-updating technology and model building in dynamic reliability analyses, including the fault tree model, multi-layer flow model, GO-FLOW model etc. The paper lists the work of some scholars in this area in recent years, which helps readers and researchers to clearly understand the current progress of Living PSA technologies in terms of model establishment and updating. Finally, the paper summarizes the challenges and future development of Living PSA and emphasizes the possible problems in data quality, human factor engineering, and the development of Living PSA technologies in the future. In the future, Living PSAs will provide more solid support for the realization of safer and more economical methods of operating nuclear power plants.

Building Trustworthy AI: The Role of Patient and Public Involvement in Healthcare AI Development

AI is helping researchers make great strides in healthcare. However, there is a trust deficit in AI when applied to critical areas like healthcare. Hence communicating the beneficial medical applications of AI and engaging the public with healthcare AI research is critical. One way to achieve this is by getting the community involved in co-designing better AI systems in healthcare projects. We argue that AI algorithms for healthcare should be co-designed with patients and healthcare workers, so that they are useful and trustworthy. We suggest a roadmap for this collaborative approach in AI model building. This will involve actively including patients with lived experience of a disease, as well as creating a research advisory group to walk patients through the process of AI model building. We suggest formulating and scoping a problem, and then generating a hypothesis that patients and scientists agree on. The road to building trustworthy AI systems may become easier if all stakeholders are involved in co-creating AI models.

A Planar Feature-Preserving Texture Defragmentation Method for 3D Urban Building Models

Oblique photogrammetry-based 3D modeling is widely used for large-scale urban reconstruction. However, textures generated with photogrammetric techniques often exhibit scattered and irregular characteristics, which lead to significant challenges with texture seams and UV map discontinuities, increasing storage requirements and affecting rendering quality. In this paper, we propose a planar feature-preserving texture defragmentation method designed specifically for urban building models. Our approach leverages the multi-planar topology of buildings to optimize texture merging and reduce fragmentation. The proposed approach is composed of three main stages: the extraction of planar features from texture fragments; the employment of these planar elements as guiding constraints for merging adjacent texture fragments within a two-dimensional texture space; and an enhanced texture-packing algorithm designed for more regular texture charts to systematically generate refined texture atlas. Experiments on various urban building models demonstrate that our method significantly improves texture continuity and storage efficiency compared to traditional approaches, with both quantitative and qualitative validation.

Diffuse radio sky models using large-scale shapelets

Sky models used in radio interferometric data-processing primarily consist of compact and discrete radio sources. When there is a need to model large-scale diffuse structure such as the Galaxy, specialized source models are sought after for the sake of simplicity and computational efficiency. We propose the use of shapelet basis functions for modeling the large-scale diffuse structure in various radio interferometric data-processing pipelines. The conventional source model construction using shapelet basis functions is restricted to using images of smaller size due to limitations in computational resources such as memory. We propose a novel shapelet decomposition method to lift this restriction, enabling the use of images of millions of pixels (as well as a wide spectral bandwidth) for building models of large-scale diffuse structure. Furthermore, the application of direction-dependent errors onto diffuse sky models is an expensive operation that is often performed as a convolution. We propose using some specific properties of shapelet basis functions to apply these direction-dependent errors as a product of the model coefficients, which avoids the need for convolution. We provide results based on simulations and real observations. In order to measure the efficacy of our proposed method in modeling large-scale diffuse structure, we considered the direction-dependent calibration of simulated as well as real LOFAR observations that have a significant number of diffuse large-scale structure. The results show that by including large-scale shapelet models of the diffuse sky, we are able to overcome a major problem of existing calibration techniques, which do not model this large-scale diffuse structure, that is, the suppression of this large-scale diffuse structure because the model is incomplete.

Laboratory Studies on Scaled Physical Building Models Founded on Sand–Rubber Tire Shred Mixtures

Laboratory Studies on Scaled Physical Building Models Founded on Sand–Rubber Tire Shred Mixtures

Exergetic port-Hamiltonian systems for multibody dynamics

AbstractMultibody dynamics simulation plays an important role in various fields, including mechanical engineering, robotics, and biomechanics. Setting up computational models however becomes increasingly challenging as systems grow in size and complexity. Especially the consistent combination of models across different physical domains still requires significant effort. This motivates the study of formal languages that enable a compositional approach to modeling multiphysical systems with basic guarantees. The paper shows how multibody systems, or more precisely assemblies of rigid bodies connected by lower kinematic pairs, can be described as Exergetic Port-Hamiltonian Systems (EPHS). The EPHS modeling language features a straightforward graphical syntax for expressing the energy-based interconnection of hierarchically nested subsystems. This reduces cognitive load and facilitates clearer communication among experts, nonexperts, and computational tools. Hierarchical nesting of systems enables abstraction of lower-level details and promotes the reuse of models at different levels of complexity. At the lowest level, there are three basic kinds of systems, representing energy storage and reversible/irreversible energy exchange. The structured approach guarantees fundamental properties of macroscopic systems, such as conservation of energy and nonnegative entropy production. In combination with the compositional syntax, this makes building and modifying models simpler and less error-prone.

Triangulation for causal loop diagrams: constructing biopsychosocial models using group model building, literature review, and causal discovery

AbstractThe complex nature of many health problems necessitates the use of systems thinking tools like causal loop diagrams (CLDs) to visualize the underlying causal network and facilitate computational simulations of potential interventions. However, the construction of CLDs is limited by the constraints and biases of specific sources of evidence. To address this, we propose a triangulation approach that integrates expert and theory-driven group model building, literature review, and data-driven causal discovery. We demonstrate the utility of this triangulation approach using a case example focused on the trajectory of depressive symptoms in response to a stressor in healthy adults. After triangulation with causal discovery, the CLD exhibited (1) greater comprehensiveness, encompassing multiple research fields; (2) a modified feedback structure; and (3) increased transparency regarding the uncertainty of evidence in the model structure. These findings suggest that triangulation can produce higher-quality CLDs, potentially advancing our understanding of complex diseases.

Uncertainty quantification for seismic response using dimensionality reduction‐based stochastic simulator

AbstractThis paper introduces a stochastic simulator for seismic uncertainty quantification, which is crucial for performance‐based earthquake engineering. The proposed simulator extends the recently developed dimensionality reduction‐based surrogate modeling method (DR‐SM) to address high‐dimensional ground motion uncertainties and the high computational demands associated with nonlinear response history analyses. By integrating physics‐based dimensionality reduction with multivariate conditional distribution models, the proposed simulator efficiently propagates seismic input into multivariate response quantities of interest. The simulator can incorporate both aleatory and epistemic uncertainties and does not assume distribution models for the seismic responses. The method is demonstrated through three finite element building models subjected to synthetic and recorded ground motions. The proposed method effectively predicts multivariate seismic responses and quantifies uncertainties, including correlations among responses.

Academic Entrepreneurial Orientation for Sustainable Development Goals in Higher Education in Indonesia: A Confirmatory Factor Analysis

Objective: This study aims to create a model for measuring the academic entrepreneurial orientation of higher education lecturers in Indonesia. Theoretical Framework: Academic entrepreneurship theory describes the perspective of commercialization of research in higher education to benefit society. This theoretical framework highlights the importance of individual entrepreneurial orientation in commercialization and academic entrepreneurship in higher education. Method: This research uses a quantitative survey. The target population was lecturers who have conducted research with commercial potential at eleven state universities in Indonesia. The research consisted of 330 samples, according to the ideal number of samples for structural equation modeling (SEM) analysis. Data analysis was conducted using confirmatory factor analysis and SEM techniques using the AMOS 21 program. Results and Discussion: The study shows that 15 factors contribute to lecturers' academic entrepreneurial orientation, proving to measure and explain their existence in the model and significantly supporting it. Research Implications: This study's findings can be a practical guide for universities to measure the entrepreneurial orientation of lecturers and develop it in the future. For the academic world, this research can inspire further research in the field of entrepreneurship, especially academic entrepreneurship. Originality/Value: This study presents a new model for building and measuring the academic entrepreneurial orientation of higher education lecturers in Indonesia in support of sustainable development goals.

EcoCommons Australia Virtual Laboratories with Cloud Computing: Meeting Diverse User Needs for Ecological Modeling and Decision-making

Biodiversity decline and climate change are among the most important environmental issues society faces. Information to address these issues has benefited from increasing big data, advances in cloud computing, and subsequent new tools for analytics. Accessing such tools is streamlined by virtual laboratories for ecological analysis, like the ‘Biodiversity and Climate Change Virtual Laboratory’ (BCCVL) and ‘ecocloud’. These platforms help reduce time and effort spent on developing programming skills, data acquisition and curation, plus model building. Recently this functionality was extended, producing EcoCommons Australia—a web-based ecological modeling platform for environmental problem-solving—with upgraded infrastructure and improved ensemble modeling, post-model analysis, workflow transparency and reproducibility. We outline our user-centered approach to systems design, from initial surveys of stakeholder needs to user involvement in testing, and collaboration with specialists. We illustrate EcoCommons and compare model evaluation statistics through four cases studies, highlighting how the modular platform meets users’ needs.

Developing an individual depression risk score based on traditional risk factors and routine biochemical markers

BackgroundEarly identification of individuals at high risk for depression is essential for effective implementation of interventions. This study utilized the UK Biobank database to construct an individual depression risk score using nomogram and explored the potential of traditional risk factors and routine biochemical markers for the auxiliary diagnosis of individual depression. MethodsA total of 369,407 participants were included in the study and divided into training and testing sets. LASSO regression was employed to select predictive variables for depression from 16 traditional risk factors and 28 routine biochemical markers. Following variable selection, two multivariable logistic regression models were constructed. Nomograms were then generated to visualize the relationships between these variables and depression risk, and to facilitate the calculation of individual depression risk scores. ResultsTwelve traditional risk factors and nine biochemical markers were selected for model building. Model 1, using only traditional risk factors, achieved the area under the curve (AUC) of 0.913 (95 % CI: 0.910–0.915), while Model 2, incorporating both traditional and routine biochemical markers, yielded an AUC of 0.914 (95 % CI: 0.912–0.917). Based on optimal cut-off values, Model 1 exhibited a sensitivity of 81.99 % and a specificity of 83.76 %, while Model 2 demonstrated a sensitivity of 81.54 % and a specificity of 84.31 %. LimitationsExternal validation is still needed to confirm the model's generalizability. ConclusionsWhile the depression risk scoring model built using traditional risk factors effectively identifies high-risk individuals for depression and demonstrates good clinical performance, incorporating routine biochemical markers did not significantly improve the model's performance.

Physics-informed ensemble learning with residual modeling for enhanced building energy prediction

Accurate modeling of building energy use is important to support a diverse spectrum of its downstream applications, such as building energy efficiency assessment, resilience analysis, smart control, etc. As mainstream approaches of building energy modeling, physics-based modeling builds on different fidelities of physics rules yet are usually compromised in modeling accuracy due to insufficiency of physics rules to capture real-world dynamics and incomplete input information. Data-driven approaches are computationally efficient, but black box (uninterpretable) in nature. For improved modeling of building energy use, this work proposes a physics-informed ensemble learning approach in building energy prediction through residual modeling. Specifically, we first analyze the components of building energy use data. Evidence suggests that the building energy use data can be decomposed into the physics-driven part, occupant-driven part, and white noise. Second, high-fidelity physics-based building models (EnergyPlus) and low-fidelity ones (RC models) are developed to capture the physics-driven part while time series methods are explored as the residual modeling approach to capture the occupant-driven discrepancies between physics-based simulation and measured building energy use (i.e., residuals). Finally, the physics-informed ensemble learning is proposed to integrate physics-based and data-driven models for enhanced accuracy of building energy modeling. Results demonstrate 40–90% decrease of discrepancies between modeling and field observations compared to traditional physics-based modeling methods. Moreover, when the training dataset size is small, the proposed ensemble model overperforms the pure data-driven models, demonstrating its higher robustness in extrapolation scenarios. This work makes fundamental contributions to the development of convergent modeling approaches in the building modeling field.

A recommendation model for optimizing transfer learning hyper-parameter settings in building heat load prediction with limited data samples

The transfer learning method has gained increasing attention in the domain of building load prediction, particularly in scenarios with limited data samples. Its core principle involves leveraging knowledge obtained from abundant data in source buildings to aid the learning process of models for the target buildings. Existing research has predominantly concentrated on optimizing the selection of source building data to improve transfer learning effectiveness, while the optimization of transfer learning hyper-parameter settings is often neglected. This study proposes a recommendation model tailored for transfer learning hyper-parameter settings in the context of small sample prediction for building heat loads. The objective is to automatically suggest suitable transfer learning hyper-parameter combination based on the specific features of the building heat load data samples. In this study, 200 real building profiles were utilized to generate the input–output dataset required for the recommendation model. By employing data mining techniques such as clustering and classification, the correlation between the features of source building data and the most effective transfer learning hyper-parameter combination is investigated. The developed recommendation model for optimal transfer learning hyper-parameter settings achieves a classification accuracy of 90.5%，and the performance evaluation was conducted using an additional dataset of 30 source buildings. The results show that by employing this recommendation model, the prediction error of the target buildings can be reduced by 0.12% to 6.64% compared to the conventional method of empirically determining transfer learning hyper-parameter settings.

Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumps

The flexibility of electrical heating devices can help address the issues arising from the growing presence of unpredictable renewable energy sources in the energy system. In particular, heat pumps offer an effective solution by employing smart control methods that adjust the heat pump’s power output in reaction to demand response signals. This paper combines imitation learning based on an artificial neural network with an intelligent control approach for heat pumps. We train the model using the output data of an optimization problem to determine the optimal operation schedule of a heat pump. The objective is to minimize the electricity cost with a time-variable electricity tariff while keeping the building temperature within acceptable boundaries. We evaluate our developed novel method, PSC-ANN, on various multi-family buildings with differing insulation levels that utilize an underfloor heating system as thermal storage. The results show that PSC-ANN outperforms a positively evaluated intelligent control approach from the literature and a conventional control approach. Further, our experiments reveal that a trained imitation learning model for a specific building is also applicable to other similar buildings without the need to train it again with new data. Our developed approach also reduces the execution time compared to optimally solving the corresponding optimization problem. PSC-ANN can be integrated into multiple buildings, enabling them to better utilize renewable energy sources by adjusting their electricity consumption in response to volatile external signals.

Aerodynamic and codification study of low-rise buildings: Part II – Partially elevated structures

This study constitutes part II of an extensive study where the aerodynamics of elevated buildings is investigated using large-scale wind tunnel testing. Part II focuses on the case of elevated buildings with partially enclosed spaces beneath the elevated floor. For this purpose, four 1:10 scaled models of elevated single-story gable-roof buildings were selected, three of which were partially elevated with enclosed regions covering areas ranging from 19% to 54% of the model footprint. The tests aimed to examine the effect of the enclosed regions below the floor on the distribution of the localized peak pressure coefficients on the walls, floor, and roof surfaces of the models. Furthermore, the study evaluates the ASCE 7–22 provisions and the proposed provisions, presented by the authors in Part I, for estimating external wind pressure coefficients acting on the floor, roof, and walls of elevated buildings. The results indicate that enclosed regions below the floor significantly alter the aerodynamics of elevated low-rise buildings and could increase the wind-induced loads on the roof and walls of such structures, with the increase in some cases exceeding 80%. Furthermore, the results align well with the proposed modifications by the authors in Part I to the ASCE 7 provisions for estimating the external pressure coefficients for the various zones of low-rise buildings, addressing the underestimation issues previously identified within the current ASCE standard.

Frequency Range Optimization for Linear Viscoelastic Characterization of Burger's Model

The linear viscoelastic behavior of materials is represented using mechanical models of choice, which are further utilized in different numerical investigations, such as finite element simulations and discrete element simulations. Burger's model is one of the widely adopted mechanical models and remains highly favored in contemporary research due to its multiple advantages. Specifically, it excels in representing long-term creep and stress relaxation behavior in a relatively simplified manner. Accurate identification of the long-term behavior for the viscoelastic material, particularly asphalt concrete, is crucial, as it serves as a key indicator of asphalt pavement performance over its service life. However, past research studies show that the parameters of Burger's model should be back-calculated from experimental data only within a limited range of frequency, otherwise, the parameters fail to represent the true material behavior. To the best of the authors’ knowledge, there is no approach for researchers to obtain the critical frequency range in which the experiments should be performed. Therefore, this study proposes a novel framework to find the critical frequency range to obtain appropriate model parameters of Burger's model, to better characterize the viscoelastic behavior of the materials. To examine the framework, asphalt concrete mixtures are used as examples in this study. Necessary laboratory tests including complex modulus tests and stress relaxation tests, are performed on two distinctive types of asphalt concrete mixtures. The generalized Maxwell model with different number of Maxwell chains are used to evaluate the performance of Burger's model. Furthermore, since commercially available finite element packages generally do not have a direct built-in Burger's model, the article shows a way of implementing Burger's model in finite element simulation. The simulations corresponding to the laboratory tests are carried out in both frequency domain and time domain to thoroughly evaluate the performance of Burger's model. The optimal frequency range of 0.1-20 Hz for the examined mixtures is found to significantly improve the accuracy of the descriptive master curve. The results also suggest that the generalized Maxwell model requires a minimum of four Maxwell chains to maintain good performance in accurately characterizing the behavior of asphalt mixtures. However, adding more Maxwell chains beyond a critical limit may not provide significant benefits. Finite element simulations demonstrate that the stress relaxation behavior predicted by the obtained Burger's model parameters aligns more closely with experimental data over longer time intervals. This makes Burger's model a strong choice for aiding in the design of simulations for studies focused on the long-term behavior of materials.