Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Automation and Machine Learning
  4. Vol 4, Issue 3, 2023

A Study of Strength Prediction of Multifiber Concrete Based on Improved Stacking

Download as PDF

DOI: 10.23977/autml.2023.040312 | Downloads: 4 | Views: 199

Author(s)

Ke Chao 1

Affiliation(s)

1 School of Urban Construction, Yangtze University, Jingzhou, 434000, China

Corresponding Author

Ke Chao

ABSTRACT

In order to solve the expected problem of concrete strength in actual engineering, a mixed concrete database was constructed. Machine learning method was used to use multiple single learners and stacking integrated model. The results showed that the effect of stacking integrated model was far better than that of single learner, and the stacking model was improved. Obtain excellent prediction model of mixed concrete. The performance indexes of MAE, RMES and R2 of this model were significantly better than that of any single learner, and were significantly improved compared with the ordinary stacking model. It provides a reference for the prediction model of civil engineering industry in the future.

KEYWORDS

Fiber reinforced concrete; Machine learning; Mechanical properties; stacking

CITE THIS PAPER

Ke Chao, A Study of Strength Prediction of Multifiber Concrete Based on Improved Stacking. Automation and Machine Learning (2023) Vol. 4: 93-98. DOI: http://dx.doi.org/10.23977/autml.2023.040312.

REFERENCES

[1] Hou Huiwei, Zheng Dongjian, Huang Hanbing, Fan Bowei. Deformation Prediction Model of Hyperbolic Arch Dam in Concrete Considering the Correlation of Adjacent Points[J]. Journal of Hydroelectric Engineering, 2022, 40(02): 105-109.
[2] Zhou Hao, Chen Bin, Meng Meili. Concrete Mix Ratio Optimization Based on SVM-ABC Model[J]. Journal of Hydroelectric Engineering, 2021, 39(06): 127-130.
[3] Li Tiejun, Hu Yi, Liu Xi, Wu Xianguo, Wang Lei. Research on Concrete Permeability Prediction Model Based on Support Vector Machine and Random Forest [J]. Journal of Hydroelectric Engineering, 2021, 39(02): 119-122+118.
[4] Han Z. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan [J]. Landslides, 2020, 17(3):641-658.
[5] Li Pengqin, Zhang Changsheng, Li Yingna, Li Chuan. Photovoltaic Power Prediction with Improved Stacking Algorithm [J]. Journal of Applied Sciences, 2022, 40(02): 288-301.
[6] Yoo D Y, Yoon Y S, Banthia N. Flexural response of steel-fiber-reinforced concrete beams: Effects of strength, fiber content, and strain-rate [J]. Cement & Concrete Composites, 2015, 64(2):84-92.
[7] Atis C D, Karahan O. Properties of steel fiber reinforced fly ash concrete [J]. Construction & Building Materials, 2009, 23(1):392-399.
[8] Yoon E S, Park S B. An Experimental Study on the Mechanical Properties and Long-Term Deformations of High-Strength Steel Fiber Reinforced Concrete [J]. Journal of the Korean Society of Civil Engineers A, 2006, 26(2A):401-409.
[9] Lee J H, Cho B, Choi E. Flexural capacity of fiber reinforced concrete with a consideration of concrete strength and fiber content [J]. Construction and Building Materials, 2017, 138(MAY1):222-231.
[10] Soulioti D V, Barkoula N M, Paipetis A, et al. Effects of Fibre Geometry and Volume Fraction on the Flexural Behaviour of Steel‐Fibre Reinforced Concrete[J]. Strain, 2009, 47.
[11] Dvorkin L, Bordiuzhenko O, Tekle B H, et al. A Method for the Design of Concrete with Combined Steel and Basalt Fiber [J]. Applied Sciences, 2021, 11(19): 8850.
[12] Hasan A, Maroof N, Ibrahim Y. Effects of Polypropylene Fiber Content on Strength and Workability Properties of Concrete[J]. Polytechnic Journal, 2019, 9(1):7-12
[13]  Fard A, Soheili H, Movafagh S R, et al. Combined effect of glass fiber and polypropylene fiber on mechanical properties of self-compacting concrete. 2016.
[14] Xu L, Li B, Ding X, et al. experimental investigation on damage behavior of polypropylene fiber reinforced concrete under compression international journal of concrete structures and materials [J]. 2019.
[15] Wang Zhizhao. Experimental Study on the Comprehensive Performance of Polypropylene Fiber Reinforced Concrete [D]. Zhejiang University, 2004.
[16] Shan Fengzhi. Research on the Mechanical Properties of Polypropylene Fiber Reinforced Concrete and Its Application in Bridge Deck Pavement [D]. Southwest Jiaotong University, 2005.
[17] Huang Gongxue. Experimental Study on the Physical and Mechanical Properties of Polypropylene Fiber Reinforced Concrete (Mortar)[D]. Zhengzhou University, 2005.
[18] Li Huaming. Study on the Performance of Polypropylene Fiber Reinforced Concrete and Its Application in Tunnel Engineering [D]. Southwest Jiaotong University, 2005.
[19] Hao Zhuan. Research and Application of Mechanical Characteristics of Polypropylene Fiber Reinforced Concrete Panels [D]. Northwest A&F University, 2007.
[20] Yu Chun. Study on the Performance of High-Performance Polypropylene Fiber Reinforced Concrete[D]. Chongqing Jiaotong University, 2009.
[21] Li Y, Zhang Q, Kamiński P, et al. Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques[J]. Materials, 2022, 15(12): 4209.
[22] Eredm RT, Kantar E, Gücüyen E, Anil O. Estimation of compression strength of polypropylene fibre reinforced concrete using artificial neural networks. Comput Concr 2013; 12: 613–625.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 1628
Visits: 68432

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.