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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
444-449

Prediction of concrete compressive strength based on tuna swarm algorithm optimization extreme learning machine

Zhang Bowu1
Geng Xiuli1,2
1. Business School, University of Shanghai for Science of Technology, Shanghai 200093, China
2. School of Intelligent Emergency Management, University of Shanghai for Science of Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0237

Abstract

The compressive strength of concrete is a crucial parameter in the design and evaluation of building structures, as it directly impacts the quality and safety of buildings. To solve the current problem that the prediction of concrete compressive strength by the existing machine learning models suffers from issues such as long prediction times and low precision, which cannot meet the real-time and accuracy requirements of the prediction on construction sites, this paper proposed a novel method for predicting concrete compressive strength based on the tuna swarm optimization algorithm(TSO-ELM). The proposed method utilized TSO to optimize the offset value of the connection weight in the initial parameters of the ELM hidden layer, thereby enhancing the prediction accuracy of ELM. In the simulation experiment, it verified the prediction speed of ELM, the optimization ability of TSO, and the generalization of the TSO-ELM model using two sets of concrete data. The results demonstrate that the proposed method can significantly improve prediction speed and accuracy with fewer iterations and better generalization, providing a new approach for the timely prediction of concrete compressive strength in field construction.

Foundation Support

国家自然科学基金面上项目(72271164)
教育部人文社会科学研究规划基金资助项目(19YJA630021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0237
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Algorithm Research & Explore
Pages: 444-449
Serial Number: 1001-3695(2024)02-019-0444-06

Publish History

[2023-08-03] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

张博吾, 耿秀丽. 基于金枪鱼群算法优化极限学习机的混凝土抗压强度预测 [J]. 计算机应用研究, 2024, 41 (2): 444-449. (Zhang Bowu, Geng Xiuli. Prediction of concrete compressive strength based on tuna swarm algorithm optimization extreme learning machine [J]. Application Research of Computers, 2024, 41 (2): 444-449. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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