Applications of Computational Intelligence in Concrete Technology

ISBN-13: 9781032013022 / Angielski / Twarda / 2022 / 328 str.

Sakshi Gupta; Parveen Sihag; Mohindra Singh Thakur

Opis

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. The technical contents included is beneficial for researchers as well as practicing engineers in concrete and construction industry.

Spis treści

1. Editorial Introduction for 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