DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Jae Hong | - |
dc.contributor.advisor | 김재홍 | - |
dc.contributor.author | Shestakova, Yekaterina | - |
dc.date.accessioned | 2023-06-21T19:30:44Z | - |
dc.date.available | 2023-06-21T19:30:44Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1008129&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/307484 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 건설및환경공학과, 2022.8,[iii, 65 p. :] | - |
dc.description.abstract | The performance of cement dispersants and their influence on the rheological behavior of concrete can be evaluated using a variety of methods, including slump test or ICAR rheometer viscosity measurements | - |
dc.description.abstract | however, they are considered labor, time, and resource- ineffective. Thus, unsupervised machine learning algorithms are suggested to be used to develop an algorithm capable of proposing candidates for further testing, avoiding the necessity to test numerous cement dispersants under investigation. A model is based on Principal Component Analysis (PCA) and K-means clustering algorithms, the results of which are used to detect the cement dispersants located on the established data boundary. Its accuracy is evaluated based on the Type II error and J-score quantification that allows selecting a dataset built upon the testing procedure with lower experimental costs. As a result, the combination of Fourier-Transform Infrared Spectroscopy (FT-IR) and Gel Filtration Chromatography (GFC) test results provided the highest accuracy in identifying special PCE-based admixtures, reducing the number of cement dispersants proposed as candidates for prospective analysis by more than a half. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Cement dispersants▼aConcrete▼aRheology▼aExperimental costs▼aUnsupervised machine learning▼aData boundary | - |
dc.subject | 시멘트 분산제,▼a콘크리트,▼a레올로지,▼a비지도 기계학습,▼a데이터 경계 | - |
dc.title | Application of machine learning algorithms to the simplification of the performance evaluation process of cement dispersants | - |
dc.title.alternative | 시멘트 분산제의 성능 평가 과정 간소화를 위한 기계 학습 알고리즘 적용 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :건설및환경공학과, | - |
dc.contributor.alternativeauthor | 예카테리나 셰스타코바 | - |
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