Performance Prediction Research Articles

A critical analysis and improvements of empirical models for predicting the performance of Electrical Submersible Pumps under viscous flow

One of the main challenges associated with Electrical Submersible Pumps (ESPs) system is the correct sizing of the pump. High viscosity fluids, emulsions, or compressible fluids significantly impact the pump’s ability to transfer energy to the fluid. Obtaining ESP characteristic curves in conditions resembling real applications is complex but essential for the correct design, operation, optimization, and safeguarding of ESP systems. The objective of this study is to critical assess the traditional methods for predicting the ESP performance under viscous flow. This study also improves these methods driven by ESP performance data. The results show that the original models have limitations because their databases are primarily based on data from conventional centrifugal pumps. Consequently, using these methods can result in significant errors in the sizing of the production system. The accuracy of the models improved significantly after modifying the methods. Reductions in errors were observed in all correction factors, and particularly in the prediction of global performance. The improvements were effective in mitigating the effects of increased deviations with the reduction in Reynolds number observed when applying the original models to predict flow rate and efficiency.

Brand Satisfaction, Brand Trust and Brand Loyalty : The Mediating Role of Brand Performance and The Moderating Role of Market Rationally in Culinary Industry

This research aims to determine whether brand performance predictions have a direct influence on brand loyalty, brand satisfaction and brand trust have a direct influence on brand loyalty, brand satisfaction and brand trust have a direct influence on brand performance and rational market have a direct influence on brand loyalty and market rationally in moderating brand satisfaction and brand trust directly on brand loyalty and brand performance predictions in mediating brand satisfaction and brand trust indirectly on brand loyalty at CV. Wijaya Kembar. The population and sample of 150 customers who bought brand products Wijaya Kembar in March 2024 are used in this quantitative study. This study employed a questionnaire for data collection, and SmartPLS3 was used for analysis. The results showed that market rationality cannot moderate the influence of brand satisfaction and brand trust directly on brand loyalty; market rationality has no direct and negative influence on brand loyalty; brand performance cannot mediate the influence of brand satisfaction and brand trust indirectly and negatively on brand loyalty; brand trust does not have a direct or positive effect on brand loyalty and brand performance; and brand trust does not have a direct or positive effect on brand loyalty but has a direct and positive effect on brand performance variable. at CV. Wijaya Kembar.

Early Performance Prediction of Students in Higher Education

Abstract: An essential component of any evaluation of an educational institution is assessing the learning outcomes of its pupils. Student performance is important when addressing issues with the learning process and is one of the main variables used to quantify learning outcomes. The area of study referred to as the idea of using data knowledge to improve educational systems gave rise to educational data mining, or EDM. The creation of techniques for analyzing data gathered from educational settings is known as EDM. This allows for a more thorough and accurate knowledge of pupils as well as an improvement in their academic performance. Assessing the learning outcomes of students is an essential component of assessing any educational establishment. When addressing issues with the learning process, student performance is an important metric utilized to quantify learning outcomes

Student Performance Prediction Using Machine Learning Algorithms

Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university’s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students’ performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model’s prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features.

CCTFv2: Modeling Cyber Competitions.

Cyber competitions are usually team activities, where team performance not only depends on the members' abilities but also on team collaboration. This seems intuitive, especially given that team formation is a well-studied discipline in competitive sports and project management, but unfortunately, team performance and team formation strategies are rarely studied in the context of cybersecurity and cyber competitions. Since cyber competitions are becoming more prevalent and organized, this gap becomes an opportunity to formalize the study of team performance in the context of cyber competitions. This work follows a cross-validating two-approach methodology. The first is the computational modeling of cyber competitions using Agent-Based Modeling. Team members are modeled, in NetLogo, as collaborating agents competing over a network in a red team/blue team match. Members' abilities, team interaction and network properties are parametrized (inputs), and the match score is reported as output. The second approach is grounded in the literature of team performance (not in the context of cyber competitions), where a theoretical framework is built in accordance with the literature. The results of the first approach are used to build a causal inference model using Structural Equation Modeling. Upon comparing the causal inference model to the theoretical model, they showed high resemblance, and this cross-validated both approaches. Two main findings are deduced: first, the body of literature studying teams remains valid and applicable in the context of cyber competitions. Second, coaches and researchers can test new team strategies computationally and achieve precise performance predictions. The targeted gap used methodology and findings which are novel to the study of cyber competitions.

An appraisal of statistical and probabilistic models in highway pavements

Accurate performance prediction is crucial for safe and efficient travel on highway pavements. Within pavement engineering, statistical models play a pivotal role in understanding pavement behavior and durability. This comprehensive study critically evaluates a spectrum of statistical models utilized in pavement engineering, encompassing mechanistic-empirical, Weibull distribution, Markov chain, regression, Bayesian networks, Monte Carlo simulation, artificial neural networks, support vector machines, random forest, decision tree, fuzzy logic, time series analysis, stochastic differential equations, copula, hidden semi-Markov, generalized linear, survival analysis, response surface methodology and extreme value theory models. The assessment meticulously examines equations, parameters, data prerequisites, advantages, limitations, and applicability of each model. Detailed discussions delve into the significance of equations and parameters, evaluating model performance in predicting pavement distress, performance assessment, design optimization, and life-cycle cost analysis. Key findings emphasize the critical aspects of accurate input parameters, calibration, validation, data availability, and model complexity. Strengths, limitations, and applicability across various pavement types, materials, and climate conditions are meticulously highlighted for each model. Recommendations are outlined to enhance the effectiveness of statistical models in pavement engineering. These suggestions encompass further research and development, standardized data collection, calibration and validation protocols, model integration, decision-making frameworks, collaborative efforts, and ongoing model evaluation. Implementing these recommendations is anticipated to enhance prediction accuracy and enable informed decision-making throughout highway pavement design, construction, maintenance, and management. This study is anticipated to serve as a valuable resource, providing guidance and insights for researchers, practitioners, and stakeholders engaged in asphalt engineering, facilitating the effective utilization of statistical models in real-world pavement projects.

Factor and Correlation Analysis for Predicting Marathon Race Performance Using Machine Learning Algorithms

A marathon race is a long-distance running event typically spanning 26.2 miles or 42.195 kilometers. It is a test of endurance, stamina, and mental fortitude, attracting participants from all walks of life, ranging from elite athletes to recreational runners. The origins of the marathon can be traced back to ancient Greece, where legend has it that a messenger named Pheidippides ran from the battlefield of Marathon to Athens to deliver news of victory before collapsing and dying from exhaustion. Machine learning has increasingly become a valuable tool in optimizing training strategies, performance prediction, and injury prevention for marathon runners. By analyzing vast amounts of data collected from wearable devices, training logs, and race results, machine learning algorithms can identify patterns, trends, and correlations that help runners improve their training regimens and race-day strategies. This paper introduces a novel approach, the Factor Analysis Probabilities Prediction Ranking Machine Learning (FA-PP-R-ML) methodology, for predicting marathon race outcomes. With historical race data, factor analysis, and machine learning techniques, the FA-PP-R-ML methodology aims to accurately estimate marathon finish times and rank predicted outcomes based on their probabilities. Through a comprehensive analysis of marathon race data, including training metrics, environmental conditions, and physiological parameters, the FA-PP-R-ML model identifies latent factors influencing race performance. Through factor analysis, latent factors influencing race performance are identified, with values ranging from 0.5 to 0.9. Machine learning algorithms utilize these factors to predict marathon finish times, resulting in accurate predictions with an average error of ±0.1 hours.

Prediction of half-marathon performance of male recreational marathon runners using nomogram.

Long-distance running is a popular competitive sport. We performed the current research as to develop an easily accessible and applicable model to predict half-marathon performance in male recreational half-marathon runners by nomogram. Male recreational half-marathon runners in Zhejiang Province, China were recruited. A set of literature-based and panel-reviewed questionnaires were used to assess the epidemiological conditions of the recruited runners. Descriptive and binary regression analyses were done for the profiling and identification of predictors related to higher half-marathon performance (completing time ≤ 105min). Participants were assigned to the training set (n = 141) and the testing set (n = 61) randomly. A nomogram was used to visually predict the half-marathon performance, and the receiver operating characteristic (ROC) was used to evaluate the predictive ability of the nomogram. A total of 202 participants (median age: 49 years; higher half-marathon performance: 33.7%) were included. After multivariate analysis, three variables remained as significant predictors: longer monthly running distance [adjusted odds ratio (AOR) = 0.992, 95% confidence interval (CI): 0.988 to 0.996, p < 0.001], faster mean training pace (AOR = 2.151, 95% CI: 1.275 to 3.630, p < 0.001), and better sleep quality [the Pittsburgh Sleep Quality Index (PSQI), AOR = 2.390, 95% CI: 1.164 to 4.907, p = 0.018]. The AUC of the training and testing sets in nomogram were 0.750 and 0.743, respectively. Further ternary and linear regression analyses corroborated the primary findings. This study developed a nomogram with good potential to predict the half-marathon performance of recreational runners. Our results suggest that longer monthly running distance, faster mean training pace and better sleep quality notably contribute to better half-marathon performance.

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

AbstractPlant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21st century. To address this challenge, we conducted linked yield trials of 752 public maize (Zea mays) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

Cardiovascular Fitness and Stride Acceleration in Race-Pace Workouts for the Prediction of Performance in Thoroughbreds.

In-training racehorse physiological data can be leveraged to further explore race-day performance prediction. To date, no large retrospective, observational study has analysed whether in-training speed and heart rate recovery can predict racehorse success. Speed (categorised as 'slow' to 'fast' according to the time taken to cover the last 600 m from a virtual finish line) and heart rate recovery (from gallop to 1 min after exercise) of flat racehorses (n = 485) of varying age, sex and type according to distance (e.g., sprinter, miler and stayer) were obtained using a fitness tracker from a single racing yard in Australia. Race-pace training sessions on turf comprised 'fast gallop' (n = 3418 sessions) or 'jumpout' (n = 1419). A posteriori racing information (n = 3810 races) for all 485 racehorses was extracted and combined with training data. Race performance was categorised as win/not-win or podium or not, each analysed by logistic regression. Colts (p < 0.001), stayers (p < 0.001) and being relatively fast over the last 600 m of a benchmark test in training (p < 0.008) were all predictive of race performance. Heart rate recovery after exercise (p = 0.21) and speed recorded at 600 m of a 1 km benchmark test in training (p = 0.94) were not predictive. In-training physiological data analytics used along with subjective experience may help trainers identify promising horses and improve decision-making.

Nonlinear impedance characteristics of a gas hydraulic energy absorber and performance prediction for railway transportation of valuable goods

It is meaningful to investigate the nonlinear impedance characteristics of gas hydraulic energy absorbers (GHEAs) and their potential application in modern railway transportation of valuable goods. Fluid-mechanics-based full parametric modelling of a GHEA used in a high-speed train is performed, both simulations with the developed mathematic model and physical experiments are carried out on the static and dynamic characteristics of the GHEA. The agreement between the results of the simulation and the experiment with tolerable deviations validates the nonlinear mathematic model. Longitudinal train dynamics (LTD) modelling and simulation of a Chinese rapid freight train with the validated GHEA model or the conventional MT-2 friction-type energy absorber model is finally conducted. The results show that when the train uses GHEAs instead of MT-2 energy absorbers, the coupler forces and vehicle accelerations of the train are significantly reduced, and the vehicles operate more steadily in situations such as train startup, emergency braking and shunting operations; thus, gas hydraulic energy absorber is protective and would be alternative for equipping rapid freight trains for the transportation of valuable goods. The obtained nonlinear mathematic model in terms of the structural, oil and gas property parameters of the GHEA is useful and meaningful in further parameter design optimization of GHEAs and optimal specification of GHEAs for rapid freight trains.

Development and validation of sloped ground pressure prediction model for a tracked tractor in hilly and mountainous environments

Tracked tractors are better suited for agricultural production in hilly environments because of their excellent stability and flotation. The ground pressure in these environments is highly irregular owing to the ground slope, which has led to a lack of theoretical foundation and data support for the development of hillside tracked tractors (HTTs). Therefore, accurate prediction of the ground pressure at the track-soil interface on inclined terrain is important. In this study, sloped ground pressure prediction model composed of sloped terrain soil pressure-sinkage model was developed to analyse the distribution characteristics of the ground pressure of tracked tractors in agricultural operations on hillside slopes. Considering the influence of the slope angle, soil conditions, operating conditions, and important parameters of the tracked tractors (including mass, centre of gravity, and track gauge), the ground pressure prediction model used harmonic oscillator functions and peak-valley control primitive functions. To validate the proposed model, ground pressure tests of the experimental tracked tractor were conducted at different slope angles and operating conditions in an experimental platform integrating a "soil-machine-crop" system. The results showed that the maximum error between the predicted results and the test data under various slopes and operating conditions was approximately 6.3%, with an average error of approximately 4.7%; the consistency between the test results and the simulated results indicate that the model established in this study is accurate and credible. Based on the ground pressure model, the developed tractive performance prediction model for HTTs had high prediction accuracy for the slip rate and traction force in the climbing traction test. The developed theoretical model and results provide practical guidance for the structural design of advanced HTTs and manoeuvrability optimization.

Performance Prediction and Optimization of Nanofluid-Based PV/T Using Numerical Simulation and Response Surface Methodology.

A numerical investigation was carried out in ANSYS Fluent® on a photovoltaic/thermal (PV/T) system with MXene/water nanofluid as heat transfer fluid (HTF). The interaction of different operating parameters (nanofluid mass fraction, mass flow rate, inlet temperature and incident radiation) on the output response of the system (thermal efficiency, electrical efficiency, thermal exergy efficiency, and electrical exergy efficiency) was studied using a predictive model generated using response surface methodology (RSM). The analysis of variance (ANOVA) method was used to evaluate the significance of input parameters affecting the energy and exergy efficiencies of the nanofluid-based PV/T system. The nanofluid mass flow rate was discovered to be having an impact on the thermal efficiency of the system. Electrical efficiency, thermal exergy efficiency, and electrical exergy efficiency were found to be greatly influenced by incident solar radiation. The percentage contribution of each factor on the output response was calculated. Input variables were optimized using the desirability function to maximize energy and exergy efficiency. The developed statistical model generated an optimum value for the mass flow rate (71.84 kgh-1), the mass fraction (0.2 wt%), incident radiation (581 Wm-2), and inlet temperature (20 °C). The highest overall energy and exergy efficiency predicted by the model were 81.67% and 18.6%, respectively.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

Prediction and Optimization of Heat Transfer Performance of Premixed Methane Impinging Flame Jet Using the Kriging Model and Genetic Algorithm

In practical applications, rapid prediction and optimization of heat transfer performance are essential for premixed methane impinging flame jets (PMIFJs). This study uses computational fluid dynamics (CFD) combined with a methane detailed chemical reaction mechanism (GRI–Mech 3.0) to study the equivalence ratio (ϕ), Reynolds number (Re) of the mixture, and the normalized nozzle–to–plate distance (H/d) on the heat transfer performance of PMIFJs. Moreover, the Kriging model (KM) was used to construct a prediction model of PMIFJ heat transfer performance. A genetic algorithm (GA) was used to determine the maximum likelihood function (MLE) of the model parameters for constructing KM and identify the points with the maximum root mean square error (RMSE) as the new infilled points for surrogate–based optimization (SBO). Combining these methods to analyze the simulation results, the results show that the global heat transfer performance of PMIFJs is enhanced with the increase in ϕ, the increase in Re, and the decrease in H/d. Sensitivity analysis points out that Re and ϕ significantly affect enhanced heat transfer, while H/d has a relatively small effect. In addition, GA was also used to search for the optimal heat transfer performance, and the global heat transfer performance at specific conditions was significantly enhanced. This study deepens the understanding of the heat transfer mechanism of impinging flame jets and provides an efficient method framework for practical applications.

Multi-objective optimization for gymnasium layout in early design stage: Based on genetic algorithm and neural network

The layout of gymnasium directly affects environment performance. The current methods are insufficient to provide quantified decision support for gymnasium layouts in the early design stages (EDS). This study proposes a framework for optimizing the layout of gymnasiums using a multi-objective optimization (MOO) method based on genetic algorithms (GA) and neural networks. The study tested the framework using a community sports arena as an example, and the results indicate: the final optimized solutions achieved a maximum reduction of 11.1 % in cooling energy consumption (CE) and 3.3 % in solar radiation (SR) compared to the earlier generations, along with a 0.9 % improvement in thermal comfort percentage (TCP). This framework promotes the development of algorithm-driven methods for stadium layout design, while the prediction model based on RBF neural networks can simultaneously provide effective performance predictions for similar design outcomes.

Generalized Weak Supervision for Neural Information Retrieval

Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can train NRMs via weak supervision, where a large dataset is automatically generated using an existing ranking model (called the weak labeler) for training NRMs. Weakly supervised NRMs can generalize from the observed data and significantly outperform the weak labeler. This paper generalizes this idea through an iterative re-labeling process, demonstrating that weakly supervised models can iteratively play the role of weak labeler and significantly improve ranking performance without using manually labeled data. The proposed Generalized Weak Supervision (GWS) solution is generic and orthogonal to the ranking model architecture. This paper offers four implementations of GWS: self-labeling, cross-labeling, joint cross- and self-labeling, and greedy multi-labeling. GWS also benefits from a query importance weighting mechanism based on query performance prediction methods to reduce noise in the generated training data. We further draw a theoretical connection between self-labeling and Expectation-Maximization. Our experiments on four retrieval benchmarks suggest that our implementations of GWS lead to substantial improvements compared to weak supervision if the weak labeler is sufficiently reliable.

Data Analysis of Student Academic Performance and Prediction of Student Academic Performance Based on Machine Learning Algorithms

With the development and popularization of education, the quality of education has become one of the key factors in the development of a country. And students' academic performance, as one of the important indicators of education quality, has been attracting much attention. This paper mines and analyzes the data affecting students' academic performance, and also conducts a predictive study of students' academic performance using logistic regression model. In this study, 30 indicators such as gender, age, family size, parental education, parental occupation, family relationship, health, and the number of drinks per this paperek and per month this paperre used as input variables, and students' academic performance was categorized into SUCCESS and FAIL, and the training and test sets this paperre divided according to the ratio of 7:3, and the logistic regression model was used for training and prediction. The results show that the logistic regression model has high prediction accuracy in predicting students' academic performance (whether they fail or not), with an accuracy of 95.8%, precision of 96.7%, recall of 95.1%, and F1 of 95.8%. This indicates that the logistic regression model has high accuracy and reliability in predicting students' academic performance. The results of this study are important for schools and educational organizations. Through the prediction of students' academic performance, schools can identify students' learning problems in time and take targeted measures to help students improve their academic performance. Meanwhile, this study also provides some useful reference information for individual students to help them better understand their learning situation, adjust their learning strategies in time and improve their learning efficiency. In the future, the method can be further explored and improved to enhance the accuracy and reliability of the prediction and to provide better support and assistance for students' learning and development.

Development of new materials for electrothermal metals using data driven and machine learning.

After adopting a combined approach of data-driven methods and machine learning, the prediction of material performance and the optimization of composition design can significantly reduce the development time of materials at a lower cost. In this research, we employed four machine learning algorithms, including linear regression, ridge regression, support vector regression, and backpropagation neural networks, to develop predictive models for the electrical performance data of titanium alloys. Our focus was on two key objectives: resistivity and the temperature coefficient of resistance (TCR). Subsequently, leveraging the results of feature selection, we conducted an analysis to discern the impact of alloying elements on these two electrical properties.The prediction results indicate that for the resistivity data prediction task, the radial basis function kernel-based support vector machine model performs the best, with a correlation coefficient above 0.995 and a percentage error within 2%, demonstrating high predictive capability. For the TCR data prediction task, the best-performing model is a backpropagation neural network with two hidden layers, also with a correlation coefficient above 0.995 and a percentage error within 3%, demonstrating good generalization ability. The feature selection results using random forest and Xgboost indicate that Al and Zr have a significant positive effect on resistivity, while Al, Zr, and V have a significant negative effect on TCR. The conclusion of the composition optimization design suggests that to achieve both high resistivity and TCR, it is recommended to set the Al content in the range of 1.5% to 2% and the Zr content in the range of 2.5% to 3%.

Prediction of full-scale crashworthiness performance based on component-level testing: application to bus structures

The growing interest in vehicle crashworthiness necessitates efficient methods to predict full-scale crash performance from component-level testing. This study introduces a framework for predicting vehicle crashworthiness using component-level tests. The framework employs a scaling procedure incorporating factors like total energy, deformation mode, and vehicle dimensions. The feasibility of this method was validated both experimentally and numerically in a study on the rollover performance of paratransit buses, utilizing two decades of data from full-scale and component testing of six buses. Wall-to-floor connections, prone to plastic deformation during rollovers, were tested for component bending. A rollover performance index was developed as a scaling procedure, establishing a clear correlation between full-scale and component tests. Additionally, extensive finite element simulations confirmed the framework applicability to numerical simulations, demonstrating its practical use in vehicle crashworthiness evaluations to foster resource-efficient design and reduce dependence on full-scale tests.