Assessment Process Research Articles

Penerapan Sistem Penilaian E-Kinerja dalam Perspektif Indeks Kepuasan Masyarakat pada Kantor Imigrasi Kelas I Non TPI Karawang

Employee performance assessment serves as a crucial tool in gauging individual effectiveness and contribution, while also providing feedback and development strategies to enhance future performance and productivity. The Karawang Immigration Office has implemented an e-performance appraisal system that takes into account the public satisfaction index. This system incorporates data from public satisfaction surveys, employee performance data, and regulatory data as inputs. The assessment process utilizes a scoring method tailored to the employee's performance indicators. This research aims to explore the e-performance appraisal system at the Karawang Immigration Office from the public satisfaction perspective. Employing a qualitative descriptive approach, the study utilizes three data collection methods: interviews, observations, and document studies. The findings indicate that the implementation of the e-performance appraisal system based on the public satisfaction index at the Karawang Immigration Office has been successful. The system encourages improved employee performance and public satisfaction with the Karawang Immigration Office.

Advancing urban climate adaptation through knowledge exchange: A case study of New York City

Cities are experiencing increasingly extreme and longer-lasting hazards due to climate change. In response, urban decision-makers use climate assessments to inform their adaptation programs. To effectively inform planning, climate assessments must be responsive to community priorities and align with information needs. However, there are few empirical studies evaluating the co-production and exchange of climate information within climate assessment processes. To address this gap, we evaluated the Climate Knowledge Exchange (CKE) piloted in 2020 by the Mayor's Office of Climate and Environmental Justice (then the Mayor's Office of Resiliency) in New York City. The CKE is an ongoing engagement process that asked: What are the research gaps and barriers to adaptation in New York City and how can we design inclusive approaches to knowledge exchange that advance equitable adaptation? To answer this question, we facilitated group discussions, distributed surveys, and reviewed adaptation plans to assess the state of climate knowledge. The findings indicate a need for contextually and culturally sensitive approaches to the co-development of actionable knowledge. Additionally, there is a need to reduce barriers to equitable adaptation and to advance the practice of climate-informed decision-making. Our findings advance understanding of the potential role of climate knowledge exchange in adaptation planning.

Effect of Mastery Learning Combined with Mind Mapping Technique on the Students’ Sculpture Performance Ability for Junior University Students

Background and Aims: The Chinese government has issued a series of policy documents to support the development of the cultural industry, providing policy guarantees and guidance for the development of sculpture education. Traditional teaching methods usually use unified teaching content and teaching methods, which cannot meet the individual needs of students. The comprehensive application of "Mastery Learning with Mind Mapping" theory and methods can provide solid technical support and rich teaching methods for the reform of sculpture education. Through the application of personalized learning plans and mind mapping, students' learning content and space can be expanded, and their learning efficiency and learning quality can be improved. This comprehensive approach can not only meet students' learning needs but also stimulate their learning interest and creativity and promote their all-round development. Therefore, the promotion and application of this method in sculpture education are of great significance and value. This study aims to explore and evaluate the impact of mastery learning combined with mind mapping technique on college students' sculpture learning achievements and performance ability through mastery learning combined with mind mapping technique. Materials and Methods: The instruments for the experiment were four lesson plans. To evaluate the quality of a curriculum plan, it can be assessed using a Likert five-point scale. The quality of the scale can be measured using the Item-Objective Congruence (IOC) The assessment process involves analyzing each component of the lesson plan, attributing scores on the Likert scale, and then calculating the average score and the standard deviation (SD) for each component. The instruments for collecting data were two instruments needed to collect the sampling data, as the detailed list showed: sculpture score assessment and sculpture performance competency assessment. The sample was 30 students (one class) among the 120 junior students in the 2023 academic year. The procedures for data collection are as follows, the sample learning achievement before and after learning through Mastery Learning with Mind mapping and sculpture performance ability after learning through Mastery Learning with Mind mapping with the established 70 percent Results: The study evaluated the effectiveness of employing mastery learning combined with mind mapping techniques to enhance students' learning achievement and sculpture performance ability. Significant improvement was observed, with posttest scores surpassing pretest scores (t =13.06*, p =0.05). Furthermore, students' sculpture performance ability scores after mastering learning with mind mapping (81.1%) exceeded the 70% criterion, with statistical significance (t =11.05*, p =0.001). Conclusion: Compared with previous related studies, the results of this study showed that using mastery learning combined with mind mapping technique teaching had a significant positive effect on the sophomore research on sculpture performance ability. This is consistent with the findings of Chen Linsheng (2022), who found that the use of mind-mapping techniques in art education can improve students' learning achievement. Combining the findings of previous studies and the results of this study, we can conclude that mastery learning combined with mind mapping technique teaching has positive effects on the ability of sculpture performance in sophomore students. However, further research still needs to explore the impact of different teaching methods on students of different grades and specialties to get a more comprehensive understanding of the effects of mastery learning combined with mind-mapping technique teaching.

Implementation Of Differentiated Learning In Class Iv Fiqh Subjects Material On Procedures For Prayer In Sickness At Mi Muhammadiyah Semondo Gombong Kebumen

The research objectives are: (1) to find out how differentiated learning is implemented in the Merdeka Curriculum in terms of content, process, product and learning environment in class IV fiqh subjects at MI Muhammadiyah Semondo; (2) to determine the supporting and inhibiting factors faced in implementing differentiated learning. This research is in the form of Qualitative Research. The students studied were class IV MI Muhammadiyah Semondo with a total of 50 children. The method used in this research is a qualitative method using field research. The data collected was implementation data, supporting and inhibiting factors for differentiated learning in class IV fiqh subjects at MI Muhammadiyah Semondo. Data collection techniques in this research used observation, interviews and documentation. The data analysis used is inductive/qualitative data analysis. The data analysis techniques used are data collection, data condensation, data presentation, and drawing conclusions. Based on the research results, it can be concluded that: (1) implementing differentiated learning in class IV fiqh subjects at MI Muhammadiyah Semondo, the teacher begins by carrying out an initial assessment to determine the students' learning styles. Next, the teacher prepares learning steps that contain differentiation of content, processes, products and learning environments, which are in accordance with the needs and interests of students; (2) supporting factors for differentiated learning, namely the existence of a policy of implementing an independent curriculum, support from the madrasa head, and a supportive learning environment. While the inhibiting factors are managing time to be effective and difficulties in the assessment process, the solution is to make clear learning plans and detailed assessment rubrics.

Classification of Brain Infarction using Deep Learning techniques on Magnetic Resonance Imaging (MRI)

Background: This research concentrated on employing Convolutional Neural Networks (CNNs) to classify brain infarctions. A stroke occurs due to a blockage or hemorrhage in the blood vessels, which disrupts or diminishes the blood supply to the brain, leading to the death of brain cells. With the progress in machine learning techniques in medical imaging, early detection of stroke has become increasingly feasible, playing a crucial role in the diagnosis and treatment of this potentially fatal condition. Objective: Building an intelligent CNN model to classify types of brain infarction using up algorithms Median Filter preserving edges and efficient minimization of noise with durability against flowing noise, Fuzzy C – Mean to Image Segmentation and Recognition of Region of Interest for infarction location and size of the infarction for brain infarction Identification, GLCM, and FOSF. Methodology: The research was carried out in a private hospital; in Baghdad, Iraq, on 60 patients with brain infarction. Included were 9 hyperacute (>6 hours post brain infarction), 19 acute (7-72 hours), 12 subacute (4-7 days), and 20 chronic (< 15 days). We have introduced a Convolutional Neural Network (CNN) model as a solution to predict the likelihood of a patient experiencing a stroke at an early stage, aiming for maximize effectiveness and precision. Results: The ROC curve demonstrates that the average apparent diffusion coefficient (aveg (ADC) mm²/s) and relative apparent diffusion coefficient (rADC%) are two reliable and effective measures for monitoring brain infarction developments. High sensitivity indicates a strong ability to detect true positives (cases of infarction), while the shape and position of the curve suggest that average (ADC) mm²/s also maintains a reasonable specificity, minimizing false positives. This balance results in high overall diagnostic accuracy. Discussion: Both rACD (%) and aveg (ADC) mm²/s are robust indicators for the imaging monitoring of brain infarction developments. The inclusion of AI in the assessment process may result marginal improvements in specificity and accuracy.

Lessons learned from remote, early-literacy instruction

The COVID-19 pandemic rapidly shifted primary-grade literacy instruction which had rarely been implemented online before the pandemic. Remote early literacy instruction is thus an emerging field of research, and research is needed to understand the affordances and limitations of this crisis-driven instruction and how it may inform early literacy instruction moving forward, both in remote and traditional settings. This mixed methods case study examined how 106 novice primary-grade teachers in the United States implemented literacy instruction in the remote platform during the COVID-19 pandemic with a desire to understand both successful and challenging literacy practices. The main data sources entailed 106 teacher interviews conducted using a semi-structured interview protocol and teacher self-ratings of their implementation of evidence-based literacy practices. Qualitative analyses of teachers’ perspectives yielded findings that remote early literacy instruction increased the involvement of families, required teachers to navigate multiple boundaries to implement literacy instruction, remote instruction was most conducive to teacher-led literacy instruction, and resulted in teachers’ difficulty knowing and addressing children’s literacy needs. Quantitative data analysis of Likert-scale questions about teachers’ early literacy instructional practices revealed teachers reported their highest quality literacy instructional practices as read alouds, collaboration with children’s families, and building an effective learning community for remote literacy instruction. Teachers rated their remote implementation of writing instruction, literacy assessment processes, and differentiation of literacy instruction as lower quality. The findings add to the literature by providing an in-depth understanding of remote early literacy instruction, successes and challenges reported by teachers providing literacy instruction to primary-aged children, and implications for post-pandemic instruction.

Assessment and Rehabilitation Strategies for Sports-Related Musculoskeletal Injuries

This study provides a comprehensive analysis of assessment methods and rehabilitation techniques for sports activities-related musculoskeletal accidents. Understanding the complexity and variety of injuries incurred in sports, this paper synthesizes the modern-day evidence-based practices and protocols used to assess such injuries. Assessment processes embody a multidimensional exam, consisting of physical examination techniques, imaging modalities, and practical checks, aiming to diagnose the extent and nature of the harm precisely. Furthermore, the evaluation examines into rehabilitation strategies tailor-made to precise accidents, emphasizing the importance of a holistic approach that integrates therapeutic physical activities, manual therapy, neuromuscular re-schooling, and modern loading protocols. A minimum of 200 participants, comprising athletes with various types of musculoskeletal injuries, will be recruited to ensure diversity and generalizability of findings. Participants will be randomly assigned to intervention and control groups using computer-generated random numbers to ensure an unbiased allocation process. Stratification based on injury type and severity will be employed as a criteria, ensuring a balanced distribution of participants across different categories. Participants will undergo rehabilitation sessions three times per week for a duration of eight weeks, ensuring a structured and progressive approach to musculoskeletal injury recovery. Special attention is given to individualized rehabilitation plans that consider the athlete's sport-specific needs, biomechanics, and psychological aspects, facilitating the most beneficial restoration and secure return to play. The paper concludes by highlighting the ongoing advancements in generation and treatment modalities that similarly enhance the evaluation accuracy and efficacy of rehabilitation interventions for sports-related musculoskeletal injuries, emphasizing the importance of a comprehensive and customized method in the management of such injuries.

Assessment of Students and Graduates ‘perceptions on Effectiveness of Nursing Curriculum and their Preparedness in a Selected School of Nursing and Midwifery in Rwanda

The study aimed to evaluate the perceptions of students and graduates regarding the effectiveness of the nursing curriculum at the School of Nursing and Midwifery, College of Medicine and Health Sciences, University of Rwanda, in preparing them for professional practice. Two key research questions were addressed: the extent to which the curriculum prepares students and graduates as nurse professionals, and specific areas where they feel more or less prepared. Guided by constructivist theory and utilizing a cross-sectional design, the study sampled 153 respondents (61.7%) from a population of 248, including 90 students and 47 graduates. Findings revealed that a robust theoretical foundation in essential areas is crucial for nursing practice readiness. Most nursing-specific modules were deemed highly relevant, and all functional modules were considered significant for developing generic skills. The curriculum delivery was characterized by student-centered strategies, with students expressing moderate satisfaction with the assessment process. The mean preparedness score for students was (3.56), indicating that (85.8%) felt moderately prepared for their future careers, while the mean score for graduates was (3.85), showing that (63.8%) felt well-prepared.

Instructors’ Beliefs About ESP Assessment: A Case Study

With the world's increasing globalization, English has become the most widely used language for communication, and English for Specific Purposes (ESP) has begun to receive more attention in the twenty-first century due to the effects of globalization and an increase in the need for skilled workers. English for Specific Purposes is a specialized branch of English language learning that focuses on teaching English in particular fields. The assessment process must be customized to the unique characteristics of English for Specific Purposes (ESP), and teacher beliefs can significantly impact on how students are taught and assessed in this subject. Previous research has concentrated on ESP course design and needs assessment in the field. However, teachers' beliefs regarding ESP assessment have not received enough attention. As a result, the study's goal is to investigate the instructors' beliefs regarding ESP assessment in higher education. Three English instructors in a state university’s College of Foreign Languages serve as a sample for this qualitative case study. Semi-structured interviews and seven weeks of reflective journaling were used to gather the data. According to the results, participants believe that ESP assessment has specificity, and it is valuable and accountable. It is also believed that ESP assessment lacks students’ motivation and requires adaptation. The school policy, student motivation, and teacher performance in ESP assessment have influenced these beliefs. To conclude, teacher participation and education are necessary for ESP assessment.

Empowering Teachers Through Digital Assessment: Enhancing Competence and Efficiency with Google Forms in Primary Education

This study focuses on analyzing the effectiveness of training on the use of Google Form as an online exam medium in improving teacher competence and the efficiency of the assessment process at SD Negeri Besuk Agung, Probolinggo. The main objective of this study is to identify how this training can improve teachers' skills in using digital technology, as well as its impact on student motivation and participation. This study uses a qualitative approach with a case study method, where data is collected through in-depth interviews, participatory observations, and document analysis. The results of the study show that Google Form training significantly increases teachers' confidence and competence in using this technology, as well as speeds up the assessment process. However, challenges such as limited infrastructure and access to technology among students still need to be overcome. The implications of this study emphasize the importance of infrastructure support and ongoing training to maximize the benefits of technology in education. This research makes an important contribution to the basic education literature and offers practical guidance for technology adoption in resource-constrained schools.

Financial risk analysis of Public-Private Partnership (PPP) projects case study on Pandaan - Malang Toll Road

This study aims to analyze the financial risks faced by the Pandaan-Malang Toll Road PPP (Public-Private Partnership) project in Indonesia. Data collection techniques include semi-structured in-depth interviews with parties related to the PPP project, including the Directorate General of Infrastructure Financing, the Tolling Road Management Agency (government), and Business Entities (PT Jasamarga Pandaan Malang). The collected data will be analyzed to determine the financial risk factors faced by PPP projects and the financial controls carried out to overcome financial risks in PPP. The results show that the risk of errors in forecasting in the calculation of demand, cost, and revenue projections is a risk that almost often occurs. The Government emphasizes the importance of optimizing the role of PII (Persero) or Indonesia Infrastructure Guarantee Fund (IIGF) as an Infrastructure Guarantee Business Entity (BUPI) to ensure financial sustainability and minimize the impact of sudden shocks on the State Budget due to the provision of Government Guarantees for cooperation projects. This study provides insights or frameworks that can be applied to similar projects, helping to improve risk assessment and decision-making processes in future infrastructure developments.

MyoActivation®, a Structured Assessment and Therapeutic Process for Adolescents With Myofascial Dysfunction and Chronic Low Back Pain: A Case Series

myoActivation®, a Structured Assessment and Therapeutic Process for Adolescents With Myofascial Dysfunction and Chronic Low Back Pain: A Case Series

University Education in Kenya: Stakeholder Perspectives on Assessment Quality

Industry demand for skills whose application is dependent on changing technological innovations has been on the rise in Kenya. This has put a spotlight on professionals engaged in higher education and training roles. Society expects higher education institutions to not only provide opportunities for learning, but to deliver education in forms that are relevant to socio-economic productive processes. Decisions on teaching and examinations which were previously accepted on the basis of competence and professional discretion are now being challenged, based on the dynamic demands of the labour market. Universities are required to legitimize and communicate results of their activities to the public, which effectively means transferring control of education to a larger stakeholder constituency. Of interest to this research therefore, was the role of facilities and the effectiveness of internal institutional mechanisms for assuring assessment quality to stakeholders. This study sought to explore the extent to which universities in Kenya have embraced reforms to meet demand for increased quality and accountability in education assessment. A sample of 524 respondents from both public and private universities was selected for use in the study. Both qualitative and quantitative data was collected using questionnaires and interview guides. Qualitative data was transcribed, and presented in themes. Quantitative data was analysed using descriptive and inferential statistics (percentages, means, Chi square (X2)) to determine the effectiveness of quality assessment processes. All statistical inferences were done at α = 0.05, This study stands to benefit university managers by providing evidence on the state of assessment quality at institutional level. This can help empower students, and other stakeholders involved in supporting university education, to consider quality among criteria for support, and to help foster competition among institutions

Fall risk prediction using temporal gait features and machine learning approaches

IntroductionFalls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible.MethodsThis study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers.ResultsTwo experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model’s ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task.DiscussionThe findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model’s generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals.

Predicting improvement in biofeedback gait training using short-term spectral features from minimum foot clearance data

Stroke rehabilitation interventions require multiple training sessions and repeated assessments to evaluate the improvements from training. Biofeedback-based treadmill training often involves 10 or more sessions to determine its effectiveness. The training and assessment process incurs time, labor, and cost to determine whether the training produces positive outcomes. Predicting the effectiveness of gait training based on baseline minimum foot clearance (MFC) data would be highly beneficial, potentially saving resources, costs, and patient time. This work proposes novel features using the Short-term Fourier Transform (STFT)-based magnitude spectrum of MFC data to predict the effectiveness of biofeedback training. This approach enables tracking non-stationary dynamics and capturing stride-to-stride MFC value fluctuations, providing a compact representation for efficient processing compared to time-domain analysis alone. The proposed STFT-based features outperform existing wavelet, histogram, and Poincaré-based features with a maximum accuracy of 95%, F1 score of 96%, sensitivity of 93.33% and specificity of 100%. The proposed features are also statistically significant (p<0.001) compared to the descriptive statistical features extracted from the MFC series and the tone and entropy features extracted from the MFC percentage index series. The study found that short-term spectral components and the windowed mean value (DC value) possess predictive capabilities regarding the success of biofeedback training. The higher spectral amplitude and lower variance in the lower frequency zone indicate lower chances of improvement, while the lower spectral amplitude and higher variance indicate higher chances of improvement.

Knowledge representation to support EMDAP implementation in advanced reactor licensing applications

The Data, Information, Knowledge, and Wisdom (DIKW) pyramid depicts the general principle of a decision-making process. Various levels of abstraction are required to formalize the available data/information/knowledge with respect to the decision being supported. The Evaluation Model Development and Assessment Process (EMDAP) was developed by the United States Nuclear Regulatory Commission (US NRC) to guide the development and assessment of Evaluation Models (EMs) for transient and accident analysis of nuclear power plants under design basis accidents. It concerns the decision of adequacy of an EM with respect to the purpose of analysis (NPP application). However, lack of data, scaling issues, modeling limitations, computation, and experimental overhead are some of the key factors that challenge the EM adequacy decision in EMDAP. Phenomena Identification and Ranking Table (PIRT) which is used for complexity resolution can also introduce noise due to bias and variance in expert opinion and judgment. The uncertainty in the decision due to these issues and challenges can be minimized by enhancing the EMDAP from the DIKW perspective. In this work, we formulate a systematic scheme for knowledge representation, and classification and characterization of evidence to enhance clarity and transparency in the EMDAP implementation. The proposed scheme is governed by the concept of the value of information, where we contextualize, classify, characterize, and evaluate the evidence (relevant data/knowledge/information related to EM development, verification, validation, and uncertainty quantification) with respect to the relevant decision attributes or elements of EMDAP. We make use of the Predictive Capability Maturity Model (PCMM), Argumentation technique, and process quality assurance for knowledge representation to support EMDAP implementation in advanced reactor licensing applications. The main objective of the proposed scheme is to support decision-making by making it more transparent, traceable, and accountable. The illustration of the proposed scheme is provided using a system simulation code (SAS4A/SASSYS-1) as the primary EM and a generic sodium fast reactor under loss of flow accident as the target reactor application.EMDAP involves different elements that require expert input and insights. However, the process of expert elicitation may require significant resources and time. To address these issues, we identify generative artificial intelligence models as potential tool which (after systematic testing and capabilities analysis) can be used for domain specific expert elicitation and information extraction for efficient implementation of EMDAP.

Transfer condition assessment of gas turbines via double multi-task Gaussian process

Predictive maintenance and health management are crucial means to enhance the operational reliability and safety of gas turbines, with time series condition assessment being a pivotal step in this process. However, the complexity of gas turbine and the harsh operating conditions raise the high cost and significant difficulty in acquiring data via sensors, which pose challenges for the conventional data-driven time series modeling methods. These result in the scarcity of available historical data and the deteriorate of condition assessment accuracy. To this end, this paper proposes an end-to-end, probabilistic transfer learning approach based on multi-task Gaussian process (MTGP) for the condition assessment of gas turbines with limited sensor data. For achieving effective transfer condition assessment, the proposed method, named double MTGP (dMTGP), employs (i) a recurrent neural networks (RNN) projector to extract the explicit evolution patterns, (ii) adopts the MTGP module to transfer knowledge from the source domain to the target domain, and (iii) presents a recursive MTGP strategy to have shared knowledge transferred cross outputs. Finally, the superiority and robustness of the proposed method are comprehensively validated against state-of-the-art competitors on four practical gas turbine units.

Embedding assessment flexibilities for future authentic learning

ABSTRACT Studies reporting flexible assessment strategies and their impact across different modes of study remain limited with little emphasis placed on the role these arrangements play in devising authentic assessment processes. This paper synthesises recent research work depicting flexible design principles and practice strategies for how educators might effectively and with confidence move assessment forward in ways that prepare students for a multitude of future possibilities. Evidence is presented for how such assessment arrangements can be created to allow for a valid and authentic representation of student performance in increasingly digital learning environments. Authentically future-facing assessment in higher education needs to be adaptable, ideally offering an element of flexibility and agency to students which helps them to continuously connect what they are learning – ideas to thinking, principles to problems, theory to practice, learning to life.

Multi-index evaluation on reinforcement programs for frame structures based on life cycle theory and time-varying reliability

Under the mandates of economic and environmental sustainability, the consideration of reinforcement engineering must extend beyond mere enhancement effects and short-term project costs. As a long-term engineering activity, reinforcement significantly impacts urban economies and environments. Although extensive research has evaluated the costs or environmental impacts of reinforcement engineering based on the whole life cycle theory, existing studies often focus on single indicators without adequately integrating them with other domains. Using a reinforced concrete frame structure as a case study, this paper develops a combined evaluation decision-making model integrating the Analytic Hierarchy Process for subjective assessment and an enhanced Rough Set method for objective evaluation. This model comprehensively assesses the seismic performance, economic costs, and environmental impacts of various reinforcement strategies. The results indicate that the combined strategy of enlarging column sections and carbon fiber reinforced polymer strengthening beams performs best in seismic performance, long-term economic benefits, and environmental impacts. Grounded in practical projects, this study adjusts the whole life cycle cost model to better align with engineering maintenance realities and introduces Rough Set theory to address deficiencies in objective indicator weighting. This research offers insights and guidance for leveraging information technology in resolving multi-criteria evaluation decision-making challenges in reinforcement engineering solutions.

Fatigue assessment of structural components through the Effective Critical Plane factor

The integrity assessment of structural components under complex loading conditions relies on the evaluation of the fatigue damage typically arising from stress concentrations, such as geometric irregularities, notches, weld beads, grooves etc.. Various methodologies, including the Notch Stress Approach (NSA), the Theory of Critical Distances (TCD), the Strain Energy Density (SED), and the Critical Plane (CP) concept, have been pivotal in assessing fatigue strength for notched and welded components. Recent works combine some of the above mentioned methodologies, while other works propose to vary the embedded parameters accounting for the loading type or the fatigue lives, trying to improve the accuracy of the fatigue assessment process. This paper introduces a novel approach, the Effective Critical Plane (ECP), which is founded on the critical plane concept. The CP factor is, however, calculated starting from an averaged, over a small volume, stress–strain field. The size of the averaging volume is assumed to be a material parameter and is determined by a best fitting procedure over different experimental data sets. The novel approach is illustrated by means of the Fatemi-Socie and the Smith-Watson-Topper CP damage factors. Its potential application to other CP formulations is straightforward, as well. Literature experimental data for low carbon steel specimens possessing different notches and loading conditions are used to validate the method’s capability in accurately determining the fatigue life and to set the radius of the averaging volume for the given material and CP parameter. A spherical volume or circular area are used in case of fully 3D or 2D numerical models, respectively. Results are compared to those of some of already existing methods, namely SED, TCD and the Modified Wöhler Curve Method.