Prediction of Compressive Strength of Concrete and Rock Using an Elementary Instance-Based Learning Algorithm
The use of machine learning techniques to predict material strength is becoming popular. However, not much attention has been paid to instance-based learning (IBL) algorithms. Therefore, in order to predict material strength, as the direct method by conducting tests is time-consuming and expensive a...
Saved in:
Main Author: | Shun-Chieh Hsieh |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | Advances in Civil Engineering |
Online Access: | http://dx.doi.org/10.1155/2021/6658932 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm
by: Rafiqul Islam, et al.
Published: (2024-12-01) -
Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete
by: Palika Chopra, et al.
Published: (2018-01-01) -
Corrigendum to “Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods”
by: M. Timur Cihan
Published: (2020-01-01) -
Compressive Strength Prediction of Self-Compacting Concrete-A Bat Optimization Algorithm Based ANNs
by: Amir Andalib, et al.
Published: (2022-01-01) -
Comparative use of different AI methods for the prediction of concrete compressive strength
by: Mouhamadou Amar
Published: (2025-03-01)