Performance Assessment Criteria Research Articles

Mobile Robot for Security Applications in Remotely Operated Advanced Reactors

This review paper addresses the escalating operation and maintenance costs of nuclear power plants, primarily attributed to rising labor costs and intensified competition from renewable energy sources. The paper proposes a paradigm shift towards a technology-centric approach, leveraging mobile and automated robots for physical security, aiming to replace labor-intensive methods. Focusing on the human–robot interaction principle, the review conducts a state-of-the-art analysis of dog robots’ potential in infrastructure security and remote inspection within human–robot shared environments. Additionally, this paper surveys research on the capabilities of mobile robots, exploring their applications in various industries, including disaster response, exploration, surveillance, and environmental conservation. This study emphasizes the crucial role of autonomous mobility and manipulation in robots for diverse tasks, and discusses the formalization of problems, performance assessment criteria, and operational capabilities. It provides a comprehensive comparison of three prominent robotic platforms (SPOT, Ghost Robotics, and ANYmal Robotics) across various parameters, shedding light on their suitability for different applications. This review culminates in a research roadmap, delineating experiments and parameters for assessing dog robots’ performance in safeguarding nuclear power plants, offering a structured approach for future research endeavors.

Implementation Of Balance Score Card To Improve Employee Performance At PT Gaya Makmur Tractors

To evaluate a company's success, performance evaluations must be conducted using acceptable performance assessment criteria. Performance appraisal also plays a vital role because it seeks to encourage employees to achieve efficiency and effectiveness in a company. Performance assessments help multinationals evaluate global human resource efficacy for optimization and improvement. It can be said that the Balanced Scorecard can help companies communicate the company's business strategy and as a company goal to achieve the vision and mission. Assessment of performance measurement through the balanced scorecard is an important factor in the company. Apart from being used to assess a company's success, performance measurement can also be used as a basis for determining a company's reward system, for example, to determine employee salary levels and appropriate rewards. Management can assess previous times using business efficiency data as the instrument. Traditional performance assessment, which primarily takes the financial sector into account, is currently what is most frequently utilized in businesses. Measuring performance with this system causes the company's orientation to only short-term profits and tends to ignore the long-term survival of the company. Performance measurements that focus on the financial sector are unable to estimate the performance of the company's intangible assets and intellectual assets (human resources). Additionally, this method of assessing achievement is less capable of entirely driving the organization on a better path and pays less consideration to the external sector and its history. Data analysis was carried out descriptively qualitatively. Using a deductive approach, the conclusion is reached by moving from the general to the specific, particularly those that are connected to the research issue, namely Smart Performance from an Islamic Human Resource Management Perspective. According to the findings, PT Gaya Makmur Tractors was urged to prepare for the worldwide marketplace by changes in performance activities. The key to competition in the global market is total quality which includes emphasis on product quality, cost or price quality, service quality, timely delivery quality, and other satisfactions that continue to develop to provide continuous satisfaction to customers. A crucial element of the business is performance measurement or assessment. Apart from being used to assess a company's success, performance measurement can also be used as a basis for determining a company's reward system, for example, to determine employee salary levels and appropriate rewards. Management can also use company performance measurements as a tool to evaluate the previous period

Exploring human resource management in the top five global hospitals: a comparative study.

The pivotal role of Human Resource Management (HRM) in hospital administration has been acknowledged in research, yet the examination of HRM practices in the world's premier hospitals has been scant. This study explored how the world's leading hospitals attain operational efficiency by optimizing human resource allocation and melding development strategies into their HRM frameworks. A comparative analysis of the HRM frameworks in the top five global hospitals was undertaken to offer a reference model for other hospitals. This research offers a comparative exploration of the HRM frameworks utilized by the top five hospitals globally, underscoring both shared and distinct elements. Using a multi-case study methodology, the research scrutinized each hospital's HRM framework across six modules, drawing literature from publicly accessible sources, including websites, annual reports, and pertinent English-language scholarly literature from platforms such as Google Scholar, PubMed, Medline, and Web of Science. The analyzed hospitals exhibited inconsistent HRM frameworks, yet all manifested potent organizational cultural attributes and maintained robust employee training and welfare policies. The design of the HR systems was strategically aligned with the hospitals' objectives, and the study established that maintaining a sustainable talent system is pivotal to achieving hospital excellence. The HRM frameworks of the five analyzed hospitals align with their developmental strategies and exhibit unique organizational cultural attributes. All five hospitals heavily prioritize aligning employee development with overall hospital growth and place a spotlight on fostering a healthy working environment and nurturing employees' sense of achievement. While compensation is a notable performance influencer, it is not rigorously tied to workload in these hospitals, with employees receiving mid-to-upper industry-range compensation. Performance assessment criteria focus on job quality and aligning employee actions with organizational values. Comprehensive welfare and protection are afforded to employees across all five hospitals.

THE RELATIONSHIP BETWEEN PREVIOUS QUALIFICATION CHARACTERISTICS AND UNIVERSITY GRADUATES USING ASSOCIATION RULES

This study investigates the correlation factors between previous qualification and graduates of private universities using association rules mining. The experiments were conducted on a real dataset comprising graduates from three private universities. FP-Growth was utilized to create association rules in the experiments. The performance assessment criteria were a minimum support of 0.10 and a minimum confidence of 0.95. The findings suggest a strong relationship between high-achieving pharmacy graduates and whose previous education was a diploma in pharmacy.

Research on pressure transmission and sealing performance of pyrophyllite in a cubic large chamber static high-pressure device

Owing to the need for a hydrostatic high-pressure cubic large cavity (hexahedral top) press used in high-pressure research and production of superhard material, two kinds of pyrophyllite powder compacts (A and B) from pyrophyllite mine in South Africa are prepared, and compared with the domestic yellow pyrophyllite powder compacts (Mentougou, Beijing) produced by the same process, to establish experimental methods and physical criteria for evaluating the pressure transmission and sealing performance of pyrophyllite. During the experiment, standard pressure materials such as Bi, Tl, and Ba are used to <i>in-situ</i> calibrate the pressure at the central positions and sealing edges of the pyrophyllite pressure chambers from the three aforementioned compacts under normal pressure conditions. Additionally, the silver melting point method is employed to obtain the corresponding relationship between chamber pressure at high temperature and system loading when using these three types of pyrophyllite as load-transmitting sealing materials. The results show that under the same hydraulic pressure loading, the difference in pressure at the central position between South African pyrophyllite B powder blocks and domestically produced pyrophyllite powder blocks does not exceed 0.1 GPa. Furthermore, in pressurization process and depressurization processe, the differences in pressure between the central position and the sealing edge of the pyrophyllite blocks are notably similar. Compared with South African pyrophyllite A powder blocks, pyrophyllite B powder blocks exhibit a closer resemblance to domestically produced pyrophyllite powder blocks in terms of high-temperature load transmission and sealing performance. Pyrophyllite B powder blocks from South Africa have the potential to serve as a substitute for domestically produced pyrophyllite without changing the existing superhard material synthesis process, making them promising candidates for use as load-transmitting media and sealing materials. These research findings hold significant academic importance in the realms of high-pressure research and superhard material production. They provide valuable insights into the selection of suitable transmission and sealing materials and the optimization of high-pressure experimental conditions. Additionally, this study presents robust method and criteria for experimental procedures and performance assessment.

A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation

The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems.

Implementation of PM-JAY in India: a qualitative study exploring the role of competency, organizational and leadership drivers shaping early roll-out of publicly funded health insurance in three Indian states

BackgroundThe Pradhan Mantri Jan Arogya Yojana (PM-JAY), a publicly funded health insurance scheme, was launched in India in September 2018 to provide financial access to health services for poor Indians. PM-JAY design enables state-level program adaptations to facilitate implementation in a decentralized health implementation space. This study examines the competency, organizational, and leadership approaches affecting PM-JAY implementation in three contextually different Indian states.MethodsWe used a framework on implementation drivers (competency, organizational, and leadership) to understand factors facilitating or hampering implementation experiences in three PM-JAY models: third-party administrator in Uttar Pradesh, insurance in Chhattisgarh, and hybrid in Tamil Nadu. We adopted a qualitative exploratory approach and conducted 92 interviews with national, state, district, and hospital stakeholders involved in program design and implementation in Delhi, three state capitals, and two anonymized districts in each state, between February and April 2019. We used a deductive approach to content analysis and interpreted coded material to identify linkages between organizational features, drivers, and contextual elements affecting implementation.Results and conclusionPM-JAY guideline flexibilities enabled implementation in very different states through state-adapted implementation models. These models utilized contextually relevant adaptations for staff and facility competencies and organizational and facilitative administration, which had considerable scope for improvement in terms of recruitment, competency development, programmatic implementation support, and rationalizing the joint needs of the program and implementers. Adaptations also created structural barriers in staff interactions and challenged implicit power asymmetries and organizational culture, indicating a need for aligning staff hierarchies and incentive structures. At the same time, specific adaptations such as decentralizing staff selection and task shifting (all models); sharing of claims processing between the insurer and state agency (insurance and hybrid model); and using stringent empanelment, accreditation, monitoring, and benchmarking criteria for performance assessment, and reserving secondary care benefit packages for public hospitals (both in the hybrid model) contributed to successful implementation. Contextual elements such as institutional memory of previous schemes and underlying state capacities influenced all aspects of implementation, including leadership styles and autonomy. These variations make comparisons across models difficult, yet highlight constraints and opportunities for cross-learning and optimizing implementation to achieve universal health coverage in decentralized contexts.

Handlungsnahe Kompetenzmessung in der Lehrkräftebildungsforschung – Eine Übersichtsarbeit zu Eigenschaften von Messinstrumenten in der DACH-Region.

AbstractIn recent years, a new branch of teacher competence research has emerged in which competence is measured close to teaching performance. Such so-called performance assessment approaches are gaining increasing attention, but the research field is still fragmented. A lack of overview and varying reporting practices interfere with its coherent development. This scoping literature review provides an overview of recent advances and the current state of performance assessment instruments in teacher education research in German-speaking countries. We examined assessment instruments that provide objective, standardised measurement procedures based on observable behaviour regarding teaching demands. Based on 20 assessment instruments, a category system with 14 categories was inductively developed, capturing their characteristics in terms of context, test methods, and alignment with criteria for performance assessment. Despite the considerable variation, three types of teacher performance assessment instruments could be identified through qualitative and exploratory statistical analyses. The results show continuity as well as development compared to previous reviews and provide suggestions on advancing the still-emerging research field. For example, they can be used to foster the coherence of the research field by providing information on typical instrument differences and similarities as well as essential reporting demands.

Secure communication through reliable S-box design: A proposed approach using coset graphs and matrix operations

Protection of sensitive information has been always the major security concern since decades to withstand against illegitimate access and usage. Substitution-boxes (S-boxes) are vital components of any modern day cryptographic system that allows us to ensure its resistance to attacks. The prime problem with creating S-box is that we are generally unable to discover a consistent distribution among its numerous features to withstand diverse cryptanalysis attacks. The majority of S-boxes investigated in the literature has good cryptographic defenses against some attacks but are susceptible to others. Keeping these considerations in mind, this paper proposes a novel approach for S-box design based on a pair of coset graphs and a newly defined operation of row and column vectors on a square matrix. Several standard performance assessment criteria are used to evaluate the reliability of proposed approach, and the results demonstrate that the developed S-box satisfies all criterions for being robust for secure communication and encryption.

Hierarchical‐linked batch‐to‐batch optimization based on transfer learning of synthesis process

AbstractIn this work, a hierarchical‐linked batch‐to‐batch optimization based on transfer learning is proposed to realize the effective optimization of a new synthesis process. Optimization efficiency is especially crucial for batch processes to improve the product quality and maximize the economic benefits. The traditional hierarchical optimization method can achieve a better effect, but it may lead to low efficiency since it requires more iterations. To further improve the optimization efficiency of a new batch process with high operational cost, a hierarchical‐linked batch‐to‐batch optimization based on transfer learning is proposed in this work. By introducing the linkage between hierarchies, the available information transmitting between hierarchies is addressed to assist and accelerate the modelling and optimization process. A performance assessment criterion based on the prior knowledge of similar processes is also proposed to further improve the optimization effect. Finally, the performance of the proposed method is verified through a simulation study of the cobalt oxalate synthesis process.

Advancing a Performance Management Tool for Service Delivery in Local Government

Various strategic plans compiled by the local levels of government seek to advance the need to integrate strategic planning frameworks and improve service delivery and related content through the application of performance management tools. From the perspective of municipalities, all policies and plans at all levels of government need to be articulated with empirical based plan-level formulation and in compliance with set performance assessment criteria. Using South Africa as a case study, the methodology of the paper included a theoretical analysis of available (but applicable) performance management tools and processes. The focus and outcome were the development of a simplified performance management tool that supports elementary prioritization of projects and the setting of standards to address the growing needs, service backlogs, and informality challenges, thereby enhancing sustainable planning and development applicable to developing countries. The study findings highlight, firstly, the prevalence of persistent service delivery gaps in local government. Secondly, local government performance management and strategic plan implementation is constrained by measurement data, standards, and information gaps. Thirdly, inadequate legislative frameworks and strategic instruments applicable to local government in developing countries adds another layer of performance management gap dimensions. Fourthly, there is misalignment between policies, legislative provisions, and local needs in terms of a set of applicable measurement tools and standards relevant in a developing country. Fifthly, an oversight role gap exists in terms of clearly defining the roles and scope of responsibilities concerning performance management. These above-mentioned shortcomings read together constitute a service delivery performance management tools gap that needs to be overcome if improved service delivery is to be achieved. In view of the above-stated considerations, a simplified performance management tool to enhance service delivery in local government had to be developed. The developed simplified performance management tool assumes that the application of performance management assessment processes is to be aligned throughout all spheres of government.

Kibria–Lukman estimator for the Conway–Maxwell Poisson regression model: Simulation and applications

The Conway–Maxwell Poisson (COMP) regression model is one of the count data models to account for over– and under–dispersion. In regression analysis, when the explanatory variables are correlated, when there is multicollinearity problem, this inflates the standard error of the maximum likelihood estimates. The Kibria–Lukman estimator was provided to handle the effect of multicollinearity in the linear regression model. Therefore, we proposed to extend this estimator to the COMP model to overcome this problem in the COMP model. The proposed estimator mitigates the adverse effect of multicollinearity on the standard error of the estimates. We used the mean squared error (MSE) as the performance assessment criterion to assess the performance of the proposed estimator and others. Also, we compared the proposed estimator theoretically with other estimators (the ridge and Liu estimators). We employed a simulation study and two life applications to study the performance of the proposed estimator. The simulation study and applications results showed the superiority of the proposed estimator because the MSE of the proposed estimator is smaller than the other estimators.

Thermal and flow performance analysis of a concentrated linear Fresnel solar collector with transverse ribs.

This article deals with the impact of including transverse ribs within the absorber tube of the concentrated linear Fresnel collector (CLFRC) system with a secondary compound parabolic collector (CPC) on thermal and flow performance coefficients. The enhancement rates of heat transfer due to varying governing parameters were compared and analyzed parametrically at Reynolds numbers in the range 5,000-13,000, employing water as the heat transfer fluid. Simulations were performed to solve the governing equations using the finite volume method (FVM) under various boundary conditions. For all Reynolds numbers, the average Nusselt number in the circular tube in the CLFRC system with ribs was found to be larger than that of the plain absorber tube. Also, the inclusion of transverse ribs inside the absorber tube increases the average Nusselt number by approximately 115% at Re = 5,000 and 175% at Re = 13,000. For all Reynolds numbers, the skin friction coefficient of the circular tube with ribs in the CLFRC system is larger than that of the plain absorber tube. The coefficient of surface friction reduces as the Reynolds number increases. The performance assessment criterion was found to vary between 1.8 and 1.9 as the Reynolds number increases.

ARM for Analyzing Factors Influencing Vaccinations During the COVID-19 Outbreak

This article investigates factors influencing Coronavirus 2019 (COVID-19) vaccinations and public concerns using association rule mining (ARM). The experiment was conducted in Phuket at the beginning of 2022 when many people were still not vaccinated. The online questionnaire comprises 26 questions with a sample of 403 participants living in Phuket, Thailand. Among the participants, 377 people were vaccinated. The experiment is based on two main ARM methods: Apriori and FP-Growth. The performance assessment criteria were 0.5 and 0.6 minimum support and 0.9 and 1.0 minimum confidence. The most appropriate values are based on 0.5 minimum support and 1.0 minimum confidence with lift > 1. The experimental results reveal factors influencing COVID-19 vaccinations in association rules. For example, most vaccinated people received Sinovac. They are female and knowledgeable about vaccines. They are concerned about severe illness or death and live in the Mueang district more than others. They are also concerned about injection site pain, nausea, vomiting, numbness, and hemiplegia. Enhancing herd immunity is another compelling reason for people to be vaccinated. In the meantime, these derived rules can be reversed to explain why many people are not vaccinated. For instance, the most available vaccine is Sinovac, and several vaccination points are in the Mueang district.

Health System Performance Assessment: Does Germany provide good access to healthcare?

Abstract Background Providing equal access to health care is a major goal of health systems and a criterion for health system performance assessment (HSPA). The first systematic HSPA for Germany has been piloted in 2021. Access is one dimension of the conceptual framework (others are, e.g., population health, quality, and efficiency), which will be analysed in the following. Methods Nine indicators to measure access were selected based on a systematic search of established instruments in (inter)national HSPA initiatives. Included indicators are availability and accessibility of services (e.g., waiting times) and financial risk protection, among others. Other criteria for the inclusion of indicators were data availability and international comparability. Indicators were evaluated in terms of their trend over time (2000-2020), in international comparison (e.g., Austria, Denmark, France), and according to various equity categories (e.g., age, gender, region). Results The indicator access to palliative care could not be evaluated due to lack of data. Overall, access is good in Germany. Internationally, Germany performs better than average on most of the indicators, and its performance has improved over time. Physician density in the inpatient and outpatient sectors has increased since 2000 and is above the average of comparator countries. For some specialties, physician density in rural areas is lower than in urban areas, but the gap has decreased in recent years and does not apply to primary care. Furthermore, only 0.3% of the total population report having foregone care, although they had considered it necessary. Conclusions Nine indicators were identified and calculated to assess the performance of the German health system in terms of access to healthcare. Access can be assessed as predominantly positive, but inequities exist. Identified gaps and future extensions, e.g., additional data sources, can provide impetus for evidence-based policy management. Key messages • First systematic Health System Performance Assessment for Germany has been piloted. • Access to health care is good in Germany, both over time and in international comparison, but inequities exist.

A Novel Method for Parkinson's Disease Diagnosis Utilizing Treatment Protocols.

It makes no difference whether a person is male or female when it comes to neurodegenerative disorders; both sexes are equally susceptible to their devastating effects. Sometimes, it is unclear why a person in their life got a condition that is well-known in the world, such as Parkinson's disease. Other times, it is evident why the individual obtained the ailment (PD). In modern times, a variety of cutting-edge algorithms that are based on treatment protocols have been developed for the purpose of diagnosing Parkinson's disease. The approach that is presented in this article is the most current one; it was created using deep learning, and it can predict how severely Parkinson's disease would affect a patient. In order to diagnose this condition, it is necessary to conduct a comprehensive medical history, a history of any past treatments, physical exams, and certain blood tests and brain films. Because they are less time-consuming and costly, diagnoses are becoming an increasingly important part of medical practice. The diagnosis of Parkinson's disease by the physician is supported by the findings of the present research, which analyzed the voices of 253 participants. Preprocessing is done in order to get the most accurate results possible from the data. In order to carry out the technique of balancing, a methodical sampling approach was used to choose the data that would afterwards be evaluated. Using a feature selection approach that was determined by the magnitude of the label's influence, many data groups were created and organized. DT, SVM, and kNN are three methods that are used in classification algorithms and performance assessment criteria. The model was developed as a result of selecting the classification method and data group that had the greatest performance value. This decision led to the creation of the model. During the process of building the model, the SVM technique was used, and data comprising 45% of the original data set were utilized. The information was arranged in descending order of significance, beginning with the most pertinent. In addition to achieving exceptional outcomes in every other aspect of the project, the performance accuracy target was successfully met at 86 percent. As a consequence of this, it has been decided that the physician will be provided with medical decision support with the assistance of the data set obtained from the speech recordings of the individual who may have Parkinson's disease and the model that has been developed. This has led to the conclusion that medical decision support will be offered to the physician.

Acknowledgement and compensation: a study of graphic design national awards

Methods for performance assessment criteria and thecompensation policy are means to recognize and strengthen bonds.National design awards assess professional performance and can beunderstood as the outcomes of making an effort to achieve excellence.It investigates how the assessment of employee performance in theeditorial area is linked to the systems of acknowledgement in graphicdesign awards. The method comprises bibliographic review,questionnaire and exploratory qualitative analysis. The results indicatethat, in addition to professional acknowledgement, the awards enablereflections on "value" in editorial design, and may raise discussionsabout design, market, innovation, technology, semantics, amongothers.

Development and Evaluation of a Passive Multiloop Wearable Hand Device for Natural Motion

Abstract This article describes the development and evaluation of our passively actuated closed-loop articulated wearable (CLAW) that uses a common slider to passively drive its exo-fingers for use in physical training of people with limited hand mobility. Our design approach utilizes physiological tasks for dimensional synthesis and yields a variety of design candidates that fulfill the desired fingertip precision grasping trajectory. Once it is ensured that the synthesized fingertip motion is close to the physiological fingertip grasping trajectories, performance assessment criteria related to user–device interference and natural joint angle movement are taken into account. After the most preferred design for each finger is chosen, minor modifications are made related to substituting the backbone chain with the wearer’s limb to provide the skeletal structure for the customized passive device. Subsequently, we evaluate it for natural joint motion based on a novel design candidate assessment method. A hand prototype is printed, and its preliminary performance regarding natural joint motion, wearability, and scalability are assessed. The pilot experimental test on a range of healthy subjects with different hand/finger sizes shows that the CLAW hand is easy to operate and guides the user’s fingers without causing any discomfort. It also ensures both precision and power grasping in a natural manner. This study establishes the importance of incorporating novel design candidate assessment techniques, based on human finger kinematic models, on a conceptual design level that can assist in finding design candidates for natural joint motion coordination.

Prediction and sensitivity analysis of self compacting concrete slump flow by random forest algorithm

Self-compacting concrete (SCC) is a construction material with many advantages, including high performance and the capacity to self-compact without mechanical vibration. As a result, SCC is widely used in construction, especially at locations where concrete structures are difficult to construct. Filling ability is one of the three basic requirements that must be met when designing the SCC mix. The slump flow (SF) is used to determine the SCC mixture's filling capacity. As a result, it is critical to estimate this number fast and precisely. The purpose of this study is to propose the use of a random forest (RF) model to predict the SF of SCC and to assess the effect of input parameters on output parameters. The study constructed the RF model using a dataset of 507 experimental results collected, which is the biggest data collection compared to previous studies on this subject. Additionally, a 10-fold cross-validation approach is used to improve the model's prediction performance. As a result, the performance assessment criteria for the testing dataset have values of RMSE = 59.5664 mm, MAE = 32.4483 mm, and R = 0.8614, respectively. This result shows that the RF model is an effective tool in predicting the SF of SCC.

Prediction and sensitivity analysis of self compacting concrete slump flow by random forest algorithm

Self-compacting concrete (SCC) is a construction material with many advantages, including high performance and the capacity to self-compact without mechanical vibration. As a result, SCC is widely used in construction, especially at locations where concrete structures are difficult to construct. Filling ability is one of the three basic requirements that must be met when designing the SCC mix. The slump flow (SF) is used to determine the SCC mixture's filling capacity. As a result, it is critical to estimate this number fast and precisely. The purpose of this study is to propose the use of a random forest (RF) model to predict the SF of SCC and to assess the effect of input parameters on output parameters. The study constructed the RF model using a dataset of 507 experimental results collected, which is the biggest data collection compared to previous studies on this subject. Additionally, a 10-fold cross-validation approach is used to improve the model's prediction performance. As a result, the performance assessment criteria for the testing dataset have values of RMSE = 59.5664 mm, MAE = 32.4483 mm, and R = 0.8614, respectively. This result shows that the RF model is an effective tool in predicting the SF of SCC.