Your browser does not fully support modern features. Please upgrade for a smoother experience.
Encyclopedia Insights
More  >>
encyclopedia>
Ongoing
Encyclopedia User Research Invitation: Share Your Insights on Academic Encyclopedias
Encyclopedia MDPI is conducting a targeted survey to identify the specific barriers hindering efficient research. We invite you to spend 3 minutes defining the priorities for our next generation of structured knowledge tools: 2026 Academic Encyclopedia Survey What This Study Explores As scholars’ approaches to accessing and engaging with knowledge evolve, we seek to better understand the role of structured resources in research and learning. Through this study, we aim to explore: What challenges scholars encounter when using structured knowledge entries; Where existing knowledge platforms may fall short of expectations; How structured knowledge resources can better support academic needs. The insights gained will help guide improvements to Encyclopedia MDPI. Contact Us If you have any questions or would like to participate in, please feel free to contact us at office@encyclopedia.pub. Kind regards,Encyclopedia Editorial Office  
Announcement 19 Mar 2026
encyclopedia>
How Machine Learning Models Predict Concrete Strength
Concrete is one of the most widely used construction materials in modern infrastructure, playing a central role in buildings, bridges, and large-scale civil engineering projects. Its performance directly affects structural safety, durability, and service life, with compressive strength serving as the key indicator of load-bearing capacity. In practice, compressive strength is typically measured through standardized laboratory tests. However, these tests require curing periods of up to 28 days, limiting their usefulness for early-stage decision-making in construction. To address this limitation, machine learning has emerged as a data-driven approach for modeling the complex relationships between concrete composition and mechanical performance. In this context, a recent study published in MDPI Materials, “Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction” evaluates different machine learning models for estimating concrete strength and compares their predictive performance. 1. Factors Affecting Concrete Compressive Strength Accurate prediction of concrete compressive strength plays a critical role in ensuring structural safety and improving construction efficiency. In engineering practice, reliable strength estimation supports mix design optimization, quality control, and decision-making throughout different stages of construction. However, predicting compressive strength is a complex task due to the heterogeneous nature of concrete. Its behavior is influenced by multiple interacting factors, and small changes in material proportions can significantly affect final performance. This complexity makes it difficult to establish simple or universal predictive rules. In particular, concrete strength is affected by a combination of mixture and processing parameters, such as cement content, water-to-cement ratio, aggregate characteristics, supplementary cementitious materials, chemical admixtures, and curing conditions. These factors do not act independently; instead, they interact in highly nonlinear ways, further increasing the difficulty of accurate prediction. Traditional empirical models often struggle to capture these nonlinear relationships, especially when dealing with large and diverse datasets. As a result, there is a growing need for more advanced modeling techniques capable of handling complex multivariable interactions and improving predictive reliability in real-world engineering applications. 2. A Data-Driven Solution: Machine Learning Approaches To address these limitations, this study explores the application of machine learning (ML) models for predicting concrete compressive strength. Machine learning offers a data-driven framework in which algorithms learn patterns from historical data and use them to make predictions for new inputs. Concrete strength is influenced by multiple interacting variables, including: Cement content Water content Fine and coarse aggregates Fly ash and blast furnace slag Superplasticizer dosage Age of concrete The relationship between these variables and compressive strength is highly nonlinear and complex. Traditional empirical models, such as Abrams’ law, are often insufficient to fully capture these interactions. Machine learning models, in contrast, are well-suited for identifying nonlinear relationships within multivariable datasets. By training on experimental data, these models learn to map material compositions and curing age to compressive strength outcomes, improving prediction accuracy through iterative optimization. 3. Study Design: Model Comparison Framework The primary objective of this study is to compare the predictive performance of four machine learning models for estimating concrete compressive strength. The models evaluated include: Artificial Neural Network (ANN) Support Vector Machine (SVM) Regression Tree (RT) Multiple Linear Regression (MLR) A dataset containing 1030 experimental samples was used for model development and evaluation. Prior to training, the dataset was preprocessed to ensure data quality and consistency. The data were then divided into training and testing subsets: 70% for training 30% for testing For the Artificial Neural Network model, an additional validation split was applied to improve generalization: 70% training 15% validation 15% testing All models were implemented using MATLAB, providing a structured environment for simulation and evaluation. 4. Model Evaluation: Performance Metrics To ensure a fair and comprehensive comparison, all models were assessed using four standard performance metrics: Mean Absolute Deviation (MAD) Root Mean Square Error (RMSE) Mean Absolute Percentage Error (MAPE) Coefficient of Correlation (R) These metrics collectively measure prediction error magnitude, relative deviation, and the strength of agreement between predicted and actual values. By applying multiple evaluation criteria, the study ensures a balanced assessment of model accuracy and reliability rather than relying on a single metric. 5. Key Findings: ANN Shows Superior Predictive Performance The comparative analysis reveals clear differences in the predictive capabilities of the four models. Among all models tested, the Artificial Neural Network (ANN) consistently demonstrated the highest level of accuracy and overall performance. Key observations include: ANN achieved the best results across multiple evaluation metrics It effectively captured nonlinear relationships between input variables and compressive strength SVM, Regression Tree, and Multiple Linear Regression showed comparatively lower predictive performance The superior performance of ANN can be attributed to its ability to model complex, nonlinear interactions through layered network structures and iterative learning processes. Unlike linear models, ANN can adaptively adjust internal parameters during training, enabling it to better approximate real-world material behavior. 6. Practical Implications: Toward Faster Construction Decision-Making The findings of this study have important implications for construction engineering and materials science. By applying machine learning models, particularly ANN, practitioners can improve the efficiency of concrete strength estimation and reduce reliance on time-intensive laboratory testing. Key practical benefits include: Reduced waiting time: Strength predictions can be made without waiting for 28-day test results Improved planning efficiency: Early predictions support better scheduling and project management Simplified evaluation process: Strength can be estimated directly from mix composition and curing age Enhanced decision-making: Enables optimization of concrete mix designs before full-scale implementation Importantly, this approach allows compressive strength estimation at earlier stages of production, using readily available material input parameters. This significantly improves responsiveness in construction workflows. 7. Why ANN Performs Better The Artificial Neural Network stands out due to its ability to learn from complex datasets with nonlinear relationships. Through iterative training, the model continuously adjusts its internal weights to minimize prediction error. This adaptive learning mechanism allows ANN to: Capture subtle interactions between multiple input variables Improve predictive accuracy with sufficient training data Handle nonlinear relationships more effectively than traditional statistical models As a result, ANN demonstrates strong suitability for engineering problems where material behavior is influenced by multiple interacting factors, such as concrete strength prediction. 8. Conclusion This study demonstrates the effectiveness of machine learning techniques in predicting concrete compressive strength and highlights the comparative advantages of different modeling approaches. While all four models—ANN, SVM, Regression Tree, and Multiple Linear Regression—provide useful predictive capabilities, the Artificial Neural Network consistently delivers the highest accuracy and most reliable performance. Overall, the results suggest that machine learning, particularly ANN-based approaches, offers a promising alternative to traditional experimental-only methods, enabling faster, more efficient, and more data-driven decision-making in concrete engineering. For more information about topic, you can view the online video entitled "Performance Comparison of Machine Learning Models for Concrete Strength".
Blog 27 Mar 2026
encyclopedia>
Academic Video Service Update: Pricing Changes, Vouchers, and New Scholar Profile
Dear Valued Customers, We are writing to inform you of upcoming changes to our video services that will take effect on February 3, 2026, at 00:00 (Swiss time). 1. Price Adjustment We will be discontinuing the discounts previously offered for our video services, and prices will revert to their original rates. Video Abstract: 600 CHF Short Take: 500 CHF Scholar Interview: 400 CHF 2. Introduction of Vouchers We are excited to announce that we will now support the use of MDPI Reviewer Vouchers and Video Production Vouchers. The relevant usage rules are as follows: Each video may be redeemed using one Reviewer Voucher or one Video Production Voucher. MDPI Reviewer Vouchers can only be used by the voucher owner, while Video Production Vouchers have no user restriction. 3. New Scholar Profile We are introducing a new type of video product: the Scholar Profile. This creates a personalized showcase for scholars by documenting their academic journeys and highlighting their achievements. The price for this service is 500 CHF. We appreciate your understanding and continued support during this transition. If you have any questions or require further clarification, please do not hesitate to contact our customer service team. Thank you for choosing our services. Sincerely, MDPI Author Services
Announcement 02 Feb 2026
encyclopedia>
Best Video Abstract Awards
The Encyclopedia platform, together with the journals Biology and Nutrients, launches the Best Video Abstract Awards to increase the visibility and reach of published research and to inspire researchers to explore the power of visual storytelling. Video abstracts have become an increasingly important medium for scientific communication. By integrating narration, visualizations, animations, and experimental footage, they make complex research more accessible, engaging, and memorable. This initiative recognizes video abstracts that are not only scientifically rigorous but also creatively compelling and educational, thereby promoting broader dissemination and deeper community engagement. To learn more about the awards or to participate directly, please visit the event page via the links provided below. https://encyclopedia.pub/best-video-abstract-award 1. Event Duration 9 February 2026 – 2 February 2027 2. Awards Biology Best Video Abstract AwardOpen to video abstracts based on papers published in Biology between 1 January 2024 and 31 December 2025. This award will be granted to two video abstracts based on the evaluation of the Award Evaluation Committee. Nutrients Best Video Abstract AwardOpen to video abstracts based on papers published in Nutrients between 1 January 2024 and 31 December 2025. This award will be granted to two video abstracts based on the evaluation of the Award Evaluation Committee. Prize For each journal award, the winner will receive: CHF 500 A voucher waiving the Article Processing Charges (APCs) for one journal submission (subject to peer review, valid for one year) A free Academic Video Service production (no matter where the paper is published), valid for one year. An electronic certificate Participant Incentive All participants will receive a CHF 100 discount voucher for the Encyclopedia Academic Video Service. 3. Participation The event will be conducted in three stages. Submission Stage 9 February 2026 – 31 August 2026 Independent Submission Authors may create and submit video abstracts independently using their own tools and creative approach. Professional Support Option Authors who do not currently have a video abstract but intend to apply for the award may opt for the Academic Video Service, which offers a one-stop, end-to-end solution covering script development, animation, voiceover recording, and editing. Please submit your video abstract here: https://encyclopedia.pub/user/video_add?activity=b57ab0910b456a5e4eebd960867ce205 Or place your video service order here: https://encyclopedia.pub/user/video_service_order All video abstracts will be assessed by the editorial team for editorial suitability and overall quality. Submissions that meet the guidelines will be assessed equally. Voting Stage 1 November 2026 – 31 December 2026 Public voting will be conducted during this period. Voting results and video performance metrics, including views, likes, shares, and collections, will contribute to the final evaluation. Winner Announcement 2 February 2027 Final winners will be determined based on a combined assessment of public voting results and a comprehensive evaluation by the Award Evaluation Committee, which carries the primary weight in the final decision. Winners will be announced on the Encyclopedia platform and journal websites. 4. Others If you have any other questions, please contact office@encyclopedia.pub
Announcement 09 Feb 2026
encyclopedia>
Encyclopedia Journal Accepted for Coverage in Scopus
We are pleased to announce that in August 2025, Encyclopedia (ISSN 2673-8392) has been selected for coverage in the Scopus indexing database. We would like to extend our sincerest gratitude to all the authors, reviewers, and editors who have contributed to this journal and made this achievement possible. Encyclopedia is an international, peer-reviewed, open-access journal that mainly publishes high-quality entries, field guides, reviews, and systematic reviews in all scientific areas. Original research articles in the area of Encyclopedia Studies are also welcome. Our goal is to form a comprehensive record of scientific development and provide reference information not only for researchers but also for the general public interested in accurate and advanced knowledge on specific topics. To make the most of this opportunity, we would like to invite you to submit your research to the journal. We warmly welcome you to consider Encyclopedia as a platform for publishing your work. If you have a paper concerning any topic within the scope of the journal, please submit it via the journal’s website: https://www.mdpi.com/journal/encyclopedia.
Announcement 28 Aug 2025
Featured Images
More  >>
Thin-Kerf Laser Processing of C<sub>f</sub>SiC Composites
Thin-Kerf Laser Processing of CfSiC Composites
Encyclopedia Editorial Office
  • 21 Jan 2026
Y Addition Optimizes Mg-9Al-1Zn Alloy Properties
Y Addition Optimizes Mg-9Al-1Zn Alloy Properties
Encyclopedia Editorial Office
  • 20 Jan 2026
A Unified Microstructure Model for Superalloy Disk Forging
A Unified Microstructure Model for Superalloy Disk Forging
Encyclopedia Editorial Office
  • 12 Jan 2026
Shoebill
Shoebill
Encyclopedia Editorial Office
  • 13 Nov 2025
Journal Encyclopedia
More  >>
Peer Reviewed
Media-Based Cultural Diversity Education: Television as an Informal Actor in the Construction of Cultural Difference
Encyclopedia 2026, 6(4), 73; https://doi.org/10.3390/encyclopedia6040073

Media-based cultural diversity education is approached here as an analytical synthesis that brings together established research traditions in media and communication studies, including mediatization, representation, and framing. It refers to the process through which media are understood to function as informal educational environments that shape how audiences learn about and interpret cultural differences. In contemporary mediatized societies, media institutions, including television and digital platforms, are understood to shape public understandings of diversity through the selection, framing, and visual representation of minority groups. Television is widely regarded as a particularly influential medium because of its wide reach and its institutional role in producing authoritative narratives about social reality. Through news reporting, documentaries, and other factual programming, television has been shown to circulate meanings about cultural diversity and provide audiences with interpretive frameworks through which minority groups are publicly understood. These communicative practices have been shown to influence how audiences perceive cultural difference, interpret social issues, and negotiate questions of belonging within society. By organizing narratives, frames, and visual repertoires through which cultural groups are portrayed, television has been shown to contribute to the formation of shared social knowledge about diversity and about relationships between majority and minority communities. In this sense, television can be understood not only as a channel of information but also as a cultural institution that shapes symbolic boundaries between social groups and influences perceptions of inclusion and exclusion. As an illustrative context, this entry also refers to representations of Roma communities in Central European media environments, where antigypsyism may be understood as a mediated cultural process embedded in everyday media communication.

Read More >>
Peer Reviewed
Successful School Leadership and Critical Pathways to Improve Student Learning
Encyclopedia 2026, 6(4), 72; https://doi.org/10.3390/encyclopedia6040072

Since the late 1970s, scholars began to develop effective school leadership models that enhance student achievement. Recent research over the past three decades has consistently shown that school leadership influences student learning mainly through indirect mechanisms rather than direct effects. The purpose of this narrative review is to synthesize the major and emerging research on school leadership models and their indirect effects on student learning and to identify the critical pathways through which school leaders successfully improve student outcomes. This narrative review delineates and presents six key pathways by which school leaders promote student learning and counteract the negative effects of students’ socio-economic status. In addition, the review highlights what distinguishes successful school principals from others and the complexity of school leadership.

Read More >>
See what people are saying about us
Shlomi Agmon
Encyclopedia Video provides potential readers with a tool to quickly understand what the work is about. That is important for casualreaders, whose time is thus spared, and for investedreaders, for whom it makes the decision to say "yes, I want to read the paper" much simpler.
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
Ignacio Cea
For the video abstracts, the papers and authors could gain more visibility and increase citations. Also, it means a more diverse and interesting way of communicating research, which is something valuable in itself.
Center for Research, Innovation and Creation, and Faculty of Religious Sciences and Philosophy, Temuco Catholic University
Melvin R. Pete Hayden
Thank the video production crew for making such a wonderful video. The narrations have been significantly added to the video! Congratulations on such an outstanding job of Encyclopedia Video team.
University of Missouri School of Medicine, United States
Academic Video Service
Feedback