Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms
Industrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporatin...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-10-01
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| Series: | Frontiers in Built Environment |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2024.1453451/full |
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| author | Carlos Roberto López Paredes Cesar García Kennedy C. Onyelowe Kennedy C. Onyelowe Maria Gabriela Zuniga Rodriguez Tammineni Gnananandarao Alexis Ivan Andrade Valle Nancy Velasco Greys Carolina Herrera Morales |
| author_facet | Carlos Roberto López Paredes Cesar García Kennedy C. Onyelowe Kennedy C. Onyelowe Maria Gabriela Zuniga Rodriguez Tammineni Gnananandarao Alexis Ivan Andrade Valle Nancy Velasco Greys Carolina Herrera Morales |
| author_sort | Carlos Roberto López Paredes |
| collection | DOAJ |
| description | Industrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. Considering the concerning environmental ramifications associated with concrete production and its utilization in construction activities, there is a pressing need to perform predictive model exercise. Furthermore, given the prevalent reliance of concrete production professionals on laboratory experiments, it is imperative to propose smart equations aimed at diminishing this dependency. These equations should be applicable for use in the design, construction, and performance assessment of concrete infrastructure, thereby reflecting the multi-objective nature of this research endeavour. It has been proposed by previous research works that the addition of FA and SF in concrete has a reduction impact on the environmental influence indicators due to reduced cement use. The artificial neural network (ANN) and the M5P models were applied in this exercise to predict the compressive strength of FA- and SF-mixed concrete also considering the impact of water reducing agent in the concrete. A sensitivity analysis was also conducted to determine the impact of the concrete components on the strength of the concrete. At the end, closed-form equations were proposed by the ANN and M5P with performance indices which outperformed previous models conducted on the same database size. The result of the sensitivity analysis showed that FA is most impactful of all the studied components thereby emphasizing the importance of adding industrial wastes in concrete production for improved mechanical properties and reduced carbon footprint in the concrete construction activities. Also, the M5P and ANN models with R2 of 0.99 showed a potential for use as decisive models to predict the compressive strength of FA- and SF-mixed concrete. |
| format | Article |
| id | doaj-art-1cb85e7fab8542f58b75fc20e816d4d2 |
| institution | OA Journals |
| issn | 2297-3362 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Built Environment |
| spelling | doaj-art-1cb85e7fab8542f58b75fc20e816d4d22025-08-20T02:00:41ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622024-10-011010.3389/fbuil.2024.14534511453451Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithmsCarlos Roberto López Paredes0Cesar García1Kennedy C. Onyelowe2Kennedy C. Onyelowe3Maria Gabriela Zuniga Rodriguez4Tammineni Gnananandarao5Alexis Ivan Andrade Valle6Nancy Velasco7Greys Carolina Herrera Morales8Escuela Superior Politécnica de Chimborazo (ESPOCH), Sede Orellana, Riobamba, EcuadorFacultad de Ingeniería, Arquitectura, Universidad Nacional de Chimborazo (UNACH), Riobamba, EcuadorDepartment of Civil Engineering, Michael Okpara University of Agriculture, Umudike, NigeriaDepartment of Civil Engineering, Kampala International University, Kampala, UgandaFacultad de Ingenieria, Ingenieria Civil, Universidad Nacional de Chimborazo (UNACH), Riobamba, EcuadorDepartment of Civil Engineering, Aditya College of Engineering and Technology, Surampalem, Andhra Pradesh, IndiaProgram in Architecture, Heritage and the City, Universitat Politecnica de Valencia, Valencia, SpainFacultad de Informatica y Electronica, Escuela Superior Politecnica de Chimborazo (ESPOCH), Riobamba, EcuadorFacultad de Ciencias, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba, EcuadorIndustrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. Considering the concerning environmental ramifications associated with concrete production and its utilization in construction activities, there is a pressing need to perform predictive model exercise. Furthermore, given the prevalent reliance of concrete production professionals on laboratory experiments, it is imperative to propose smart equations aimed at diminishing this dependency. These equations should be applicable for use in the design, construction, and performance assessment of concrete infrastructure, thereby reflecting the multi-objective nature of this research endeavour. It has been proposed by previous research works that the addition of FA and SF in concrete has a reduction impact on the environmental influence indicators due to reduced cement use. The artificial neural network (ANN) and the M5P models were applied in this exercise to predict the compressive strength of FA- and SF-mixed concrete also considering the impact of water reducing agent in the concrete. A sensitivity analysis was also conducted to determine the impact of the concrete components on the strength of the concrete. At the end, closed-form equations were proposed by the ANN and M5P with performance indices which outperformed previous models conducted on the same database size. The result of the sensitivity analysis showed that FA is most impactful of all the studied components thereby emphasizing the importance of adding industrial wastes in concrete production for improved mechanical properties and reduced carbon footprint in the concrete construction activities. Also, the M5P and ANN models with R2 of 0.99 showed a potential for use as decisive models to predict the compressive strength of FA- and SF-mixed concrete.https://www.frontiersin.org/articles/10.3389/fbuil.2024.1453451/fullgreen concreteindustrial wastescompressive strengthM5PANNsensitivity analysis |
| spellingShingle | Carlos Roberto López Paredes Cesar García Kennedy C. Onyelowe Kennedy C. Onyelowe Maria Gabriela Zuniga Rodriguez Tammineni Gnananandarao Alexis Ivan Andrade Valle Nancy Velasco Greys Carolina Herrera Morales Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms Frontiers in Built Environment green concrete industrial wastes compressive strength M5P ANN sensitivity analysis |
| title | Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms |
| title_full | Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms |
| title_fullStr | Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms |
| title_full_unstemmed | Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms |
| title_short | Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms |
| title_sort | evaluating the impact of industrial wastes on the compressive strength of concrete using closed form machine learning algorithms |
| topic | green concrete industrial wastes compressive strength M5P ANN sensitivity analysis |
| url | https://www.frontiersin.org/articles/10.3389/fbuil.2024.1453451/full |
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