Advances in nano research

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Machine learning-based techniques to facilitate the production of stone nano powder-reinforced manufactured-sand concrete

  • Zanyu Huang (College of Economics and Management Engineering, Beijing Institute of Civil Engineering and Architecture) ;
  • Qiuyue Han (College of Economics and Management Engineering, Beijing Institute of Civil Engineering and Architecture) ;
  • Adil Hussein Mohammed (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil) ;
  • Arsalan Mahmoodzadeh (IRO, Civil Engineering Department, University of Halabja) ;
  • Nejib Ghazouani (Department of Civil Engineering, College of Engineering, Northern Border University) ;
  • Shtwai Alsubai (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Abed Alanazi (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Abdullah Alqahtani (Software Engineering Department, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University)
  • Received : 2023.05.18
  • Accepted : 2023.09.13
  • Published : 2023.12.25
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Abstract

This study aims to examine four machine learning (ML)-based models for their potential to estimate the splitting tensile strength (STS) of manufactured sand concrete (MSC). The ML models were trained and tested based on 310 experimental data points. Stone nanopowder content (SNPC), curing age (CA), and water-to-cement (W/C) ratio were also studied for their impacts on the STS of MSC. According to the results, the support vector regression (SVR) model had the highest correlation with experimental data. Still, all of the optimized ML models showed promise in estimating the STS of MSC. Both ML and laboratory results showed that MSC with 10% SNPC improved the STS of MSC.

Keywords

Acknowledgement

A. The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number "NBU-FFR-2023-0109". B. This study is supported via funding from Prince Satam bin Abdulaziz University project number (PSAU/2023/R/1444).

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