Prediction of Compressive Strength of Concrete with Manufactured Sand by Ensemble Classification and Regression Tree Method-Reference-Cited by-同舟云学术

Prediction of Compressive Strength of Concrete with Manufactured Sand by Ensemble Classification and Regression Tree Method

Published:2021-07 Issue:7 Volume:33 Page:
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

Ren Qiang¹^ORCID,Ding Luchuan²^ORCID,Dai Xiaodi³,Jiang Zhengwu⁴,De Schutter Geert⁵

Affiliation:

1. Ph.D. Candidate, Key Laboratory of Advanced Civil Engineering Materials of Ministry of Education, School of Materials Science and Engineering, Tongji Univ., Shanghai 201804, China. ORCID: .

2. Ph.D. Candidate, Magnel-Vandepitte Laboratory, Dept. of Structural Engineering and Building Materials, Ghent Univ., Technologiepark-Zwijnaarde 60, Ghent 9052, Belgium. ORCID: .

3. Ph.D. Candidate, Magnel-Vandepitte Laboratory, Dept. of Structural Engineering and Building Materials, Ghent Univ., Technologiepark-Zwijnaarde 60, Ghent 9052, Belgium.

4. Professor, Key Laboratory of Advanced Civil Engineering Materials of Ministry of Education, School of Materials Science and Engineering, Tongji Univ., Shanghai 201804, China (corresponding author).

5. Professor, Magnel-Vandepitte Laboratory, Dept. of Structural Engineering and Building Materials, Ghent Univ., Technologiepark-Zwijnaarde 60, Ghent 9052, Belgium.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29MT.1943-5533.0003741

Reference72 articles.

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2. Modeling the compressive strength of high-strength concrete: An extreme learning approach

3. Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network

4. Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers

5. Effect of partial and total replacement of siliceous river sand with limestone crushed sand on the durability of mortars exposed to chemical solutions

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