Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models-Reference-Cited by-同舟云学术

Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models

Published:2019-11 Issue:11 Volume:31 Page:04019255
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

Cook Rachel¹,Lapeyre Jonathan¹,Ma Hongyan²,Kumar Aditya³^ORCID

Affiliation:

1. Graduate Student, Dept. of Materials Science and Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65409.

2. Assistant Professor, Dept. of Civil, Architectural, and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65409.

3. Assistant Professor, Dept. of Materials Science and Engineering, Missouri Univ. of Science and Technology, B49 McNutt Hall, 1400 N Bishop, Rolla, MO 65409 (corresponding author). ORCID: .

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.1943-5533.0002902

Reference69 articles.

1. Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete

2. Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm

3. Consistency of random forests and other averaging classifiers;Biau G.;J. Mach. Learn. Res.,2008

4. Bagging predictors

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