Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sharma, Nitisha | - |
dc.contributor.author | Thakur, Mohindra Singh | - |
dc.contributor.author | Sihag, Parveen | - |
dc.contributor.author | Malik, Mohammad Abdul | - |
dc.contributor.author | Kumar, Raj | - |
dc.contributor.author | Abbas, Mohamed | - |
dc.contributor.author | Saleel, Chanduveetil Ahamed | - |
dc.date.accessioned | 2023-07-11T06:40:55Z | - |
dc.date.available | 2023-07-11T06:40:55Z | - |
dc.date.created | 2023-07-11 | - |
dc.date.issued | 2022-09-01 | - |
dc.identifier.issn | 1996-1944 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88427 | - |
dc.description.abstract | The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | MATERIALS | - |
dc.title | Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000851818100001 | - |
dc.identifier.doi | 10.3390/ma15175811 | - |
dc.identifier.bibliographicCitation | MATERIALS, v.15, no.17 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85137926246 | - |
dc.citation.title | MATERIALS | - |
dc.citation.volume | 15 | - |
dc.citation.number | 17 | - |
dc.contributor.affiliatedAuthor | Kumar, Raj | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | concrete | - |
dc.subject.keywordAuthor | compressive strength | - |
dc.subject.keywordAuthor | flexural strength | - |
dc.subject.keywordAuthor | support vector machines | - |
dc.subject.keywordAuthor | gaussian processes | - |
dc.subject.keywordAuthor | linear regression | - |
dc.subject.keywordPlus | SELF-COMPACTING CONCRETE | - |
dc.subject.keywordPlus | SUPPORT VECTOR MACHINES | - |
dc.subject.keywordPlus | COMPRESSIVE STRENGTH | - |
dc.subject.keywordPlus | FLY-ASH | - |
dc.subject.keywordPlus | MECHANICAL-PROPERTIES | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | CEMENT | - |
dc.subject.keywordPlus | DUST | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Metallurgy & Metallurgical Engineering | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Physical | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Metallurgy & Metallurgical Engineering | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.relation.journalWebOfScienceCategory | Physics, Condensed Matter | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.