Analysis of Piezoresistive Properties of Cement Composites with Fly Ash and Carbon Nanotubes Using Transformer Algorithm

Jonghyeok Kim; Jinho Bang; Haemin Jeon

doi:10.7734/COSEIK.2023.36.6.415

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2023 Vol.36, Issue 6 Preview Page

Research Paper

Analysis of Piezoresistive Properties of Cement Composites with Fly Ash and Carbon Nanotubes Using Transformer Algorithm 트랜스포머 알고리즘을 활용한 탄소나노튜브와 플라이애시 혼입 시멘트 복합재료의 압저항 특성 분석

31 December 2023. pp. 415-421

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Abstract

In this study, the piezoresistive properties of cementitious composites enhanced with carbon nanotubes for improved electrical conductivity were analyzed using a deep learning-based transformer algorithm. Experimental execution was performed in parallel for acquisition of training data. Previous studies on mixture design, specimen fabrication, chemical composition analysis, and piezoresistive performance testing are also reviewed in this paper. Notably, specimens in which fly ash substituted 50% of the binder material were fabricated and evaluated in this study, in addition to carbon nanotube-infused specimens, thereby exploring the potential enhancement of piezoresistive characteristics in conductive cementitious materials. The experimental results showed more stable piezoresistive responses in specimens with fly-ash substituted binder. The transformer model was trained using 80% of the gathered data, with the remaining 20% employed for validation. The analytical outcomes were generally consistent with empirical measurements, yielding an average absolute error and root mean square error between 0.069 to 0.074 and 0.124 to 0.132, respectively.

Keywords

cement composites

deep learning

transformation algorithm

carbon nanotube

piezoresistive

fly ash

본 논문에서는 시멘트에 탄소나노튜브를 혼입하여 전기 전도성을 향상시킨 복합재료의 압저항 특성을 딥러닝 기반 트랜스포머 알고리즘을 적용하여 분석하였다. 훈련 데이터 확보를 위한 실험수행을 병행하였으며, 기존 연구문헌을 참조하여 배합설정, 시편제작, 화학조성 분석, 압저항 성능측정 실험을 수행하였다. 특히 본 연구에서는 탄소나노튜브 혼입 시편뿐 아니라 플라이애시를 바인더 대비 50% 대체한 시편에 대한 제작 및 성능평가를 함께 수행하여, 전도성 시멘트 복합재료의 압저항 특성 향상 가능성을 탐구하였다. 실험결과, 플라이애시 대체 바인더의 경우 보다 안정적인 압저항 특성결과가 관찰되었으며, 측정된 데이터의 80%를 이용하여 트랜스포머 모델을 훈련시키고 나머지 20%를 통해 검증하였다. 해석 결과는 실험적 측정과 대체로 부합하였으며, 평균 절대 오차 및 평균 제곱근 오차는 각각 0.069~0.074와 0.124~0.132을 나타내었다.

키워드

시멘트 복합재료

딥러닝

트랜스포메이션 알고리즘

탄소나노튜브

압저항

플라이애시

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Information

Publisher :Computational Structural Engineering Institute of Korea
Publisher(Ko) :한국전산구조공학회
Journal Title :Journal of the Computational Structural Engineering Institute of Korea
Journal Title(Ko) :한국전산구조공학회 논문집
Volume : 36
No :6
Pages :415-421
Received Date : 2023-11-07
Revised Date : 2023-11-09
Accepted Date : 2023-11-09
DOI :https://doi.org/10.7734/COSEIK.2023.36.6.415

Journal of the Computational Structural Engineering Institute of Korea ISSN:1229-3059(Print) 2287-2302(Online) 한국전산구조공학회 논문집

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