Buch, Englisch, 320 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 626 g
Buch, Englisch, 320 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 626 g
Reihe: Smart and Intelligent Computing in Engineering
ISBN: 978-1-032-01302-2
Verlag: CRC Press
Computational intelligence (CI) in concrete technology has not yet been fully explored worldwide because of some limitations in data sets. This book discusses the selection and separation of data sets, performance evaluation parameters for different types of concrete and related materials, and sensitivity analysis related to various CI techniques. Fundamental concepts and essential analysis for CI techniques such as artificial neural network, fuzzy system, support vector machine, and how they work together for resolving real-life problems, are explained.
Features:
- It is the first book on this fast-growing research field.
- It discusses the use of various computation intelligence techniques in concrete technology applications.
- It explains the effectiveness of the methods used and the wide range of available techniques.
- It integrates a wide range of disciplines from civil engineering, construction technology, and concrete technology to computation intelligence, soft computing, data science, computer science, and so on.
- It brings together the experiences of contributors from around the world who are doing research in this field and explores the different aspects of their research.
The technical content included is beneficial for researchers as well as practicing engineers in the concrete and construction industry.
Zielgruppe
Academic, Postgraduate, Professional, Professional Practice & Development, and Undergraduate Advanced
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Bauingenieurwesen Baukonstruktion, Baufachmaterialien
- Technische Wissenschaften Bauingenieurwesen Bauingenieurwesen
- Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Geowissenschaften Umweltwissenschaften Umwelttechnik
Weitere Infos & Material
1. Usage of Computational Intelligence Techniques in Concrete Technology, 2. Developing Random Forest Random Tree and Linear Regression Model to Predict Compressive Strength of Concrete Using Glass Fiber, 3. Prediction of Compressive Strength at Elevated Temperatures Using Machine Learning Methods, 4. Implementation of Machine Learning Approaches to Evaluate Flexural Strength of Concrete With Glass Fiber, 5. A Comparative Study Using ANFIS and ANN for Determining the Compressive Strength of Concrete, 6. Prediction of Concrete Mix Compressive Strength Using Waste Marble Powder: A Comparison of ANN, RF, RT, and LR models, 7. Using GA to Predict the Compressive Strength of Concrete Containing Nano-Silica, 8. Evaluation of Models by Soft Computing Techniques for the Prediction of Compressive Strength of Concrete Using Steel Fibre, 9. Using Regression Model to Estimate the Splitting Tensile Strength for the Concrete With Basalt Fiber Reinforced Concrete, 10. Prediction of Compressive Strength of Self-Compacting Concrete Containing Silica’s Using Soft Computing Techniques, 11. Using Soft Computing Techniques to Predict the Values of Compressive Strength of Concrete with Basalt Fiber Reinforced Concrete, 12. Soft Computing-Based Prediction of Compressive Strength of High Strength Concrete, 13. Forecasting Compressive Strength of Concrete Containing Nano-Silica Using Particle Swarm Optimization Algorithm and Genetic Algorithm, 14. Prediction of Ultrasonic Pulse Velocity of Concrete, 15. Evaluation of ANN and Tree-Based Techniques for Predicting the Compressive Strength of Granite Powder Reinforced Concrete, 16. Predicting Recycled Aggregates Compressive Strength in High-Performance Concrete Using Artificial Neural Networks, 17. Compressive Strength Prediction and Analysis of Concrete Using Hybrid Artificial Neural Networks