Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression-Reference-Cited by-同舟云学术

Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression

Published:2023-07-01 Issue: Volume:2023 Page:1-22
ISSN:1687-8442
Container-title:Advances in Materials Science and Engineering
language:en
Short-container-title:Advances in Materials Science and Engineering

Author:

Rajprasad J.¹^ORCID,Priya Rachel P.²^ORCID,Arulselvan S.³,Arul D.³,Ramesh Kumar G.⁴,Pallavi H. J.⁵,Sivaraja M.⁶,Singh Vinay Kumar⁷,Gebreamlak Getachew⁸^ORCID

Affiliation:

1. Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, Tamilnadu, India

2. Department of Structural Engineering, Institute of Civil Engineering, Saveetha School of Engineering, Saveetha Institute of Medical Science and Technology, Thandalam, Chennai, India

3. Department of Civil Engineering, Coimbatore Institute of Technology, Coimbatore 641014, India

4. Department of Civil Engineering, Dr. N.G.P. Institute of Technology, Coimbatore 641048, India

5. Department of Civil Engineering, Global Academy of Technology, Bengaluru 560098, India

6. Department of Civil Engineering, Nehru Institute of Technology, Coimbatore 641105, India

7. Civil Engineering Department, Madan Mohan Malviya University of Technology, Gorakhpur, Uttar Pradesh 273010, India

8. Department of Mechanical Engineering, College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia

Abstract

The paper proposes a deep hybrid forest regression-based modeling method for measuring the compressive strength (CS) of concrete. Then, the reduced feature vector is used as input to train multiple subforest models (SFM), the predicted values are selected from multiple subforests via the KNN (K-nearest neighbor) method to combine them to obtain the layer regression vector (LRV), and it is combined with the reduced feature vector to obtain the improved LRV, then the output of this layer is taken; second, the regression vector (RV) of the input layer enhancement layer is used as input to obtain the output of the second layer FM, and the steps are repeated until the output of the input layer FM is complete. Finally, the output of the FM of the first layer is obtained. Several SFMs are trained and the result is obtained. The final prognosis is obtained by arithmetically averaging the forecast results of the SFMs of this layer.

Publisher

Hindawi Limited

Subject

General Engineering,General Materials Science

Link

http://downloads.hindawi.com/journals/amse/2023/3766214.pdf

Reference37 articles.

1. Machine learning techniques in concrete mix design;P. Ziolkowski;Materials,2019

2. Operational Optimization and Feedback Control for Complex Industrial Processes

3. Data-driven Soft Sensors in the process industry

4. Prediction of concrete compressive strength and slump by machine learning methods;M. Timur Cihan;Advances in Civil Engineering,2019

5. The prediction analysis of properties of recycled aggregate permeable concrete based on back-propagation neural network