Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison
One of the largest sources of greenhouse gas (GHG) emissions is the construction concrete industry which has alone 50% of the world's emissions. One possible remedy to mitigate the effect of environmental issues is the use of waste and recycled material in concrete. Today, immense agricultural...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd.
2022
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| Online Access: | http://eprints.utm.my/102959/1/BawarIftikhar2022_PredictiveModelingofCompressiveStrength_compressed.pdf |
| _version_ | 1796867366612631552 |
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| author | Iftikhar, Bawar Alih, Sophia C. Vafaei, Mohammadreza Elkotb, Mohamed Abdelghany Shutaywi, Meshal Javed, Muhammad Faisal Deebani, Wejdan Khan, M. Ijaz Aslam, Fahid |
| author_facet | Iftikhar, Bawar Alih, Sophia C. Vafaei, Mohammadreza Elkotb, Mohamed Abdelghany Shutaywi, Meshal Javed, Muhammad Faisal Deebani, Wejdan Khan, M. Ijaz Aslam, Fahid |
| author_sort | Iftikhar, Bawar |
| collection | ePrints |
| description | One of the largest sources of greenhouse gas (GHG) emissions is the construction concrete industry which has alone 50% of the world's emissions. One possible remedy to mitigate the effect of environmental issues is the use of waste and recycled material in concrete. Today, immense agricultural waste is being used as a substitute for cement in the production of sustainable concrete. Therefore, this study is aimed to predict and develop an empirical formula of the compressive strength of rice husk ash (RHA) concrete using machine learning algorithms. Methods employed in this study includes gene expression programming (GEP) and Random Forest Regression (RFR). A reliable database of 192 data points was employed for developing the models. Most influential variables including age, cement, rice husk ash, water, super plasticizer, and aggregate were employed as input parameters in the development of RHA-based concrete models. Evaluation of models was performed using different statistical parameters. These statistical measures include mean absolute error (MAE), coefficient of determination (R2), performance index (ρ), root man square error (RMSE), relative squared error (RSE) and relative root mean square (RRMSE). The GEP model outperforms the RFR ensemble model in terms of robustness, with a greater correlation of R2 = 0.96 compared to RFR's R2 = 0.91. Ensemble modeling showed an enhancement of 1.62 percent for RFR compressive strength model when compared with individual RFR compressive strength model as illustrated by statistical parameters. Moreover, GEP model shows an enhancement of 37.33 percent in average error with an average error 2.35 MPa as compared to RFR model with average error of about 3.75 MPa. Cross validation was used as external check to avoid overfitting issues of the models and confirm the generalized model output. Parametric analysis was performed to determine the impact of the input parameters on the output. Cement and age were shown to have a substantial impact on the compressive strength of RHA concrete using sensitivity analysis. |
| first_indexed | 2024-03-05T21:26:10Z |
| format | Article |
| id | utm.eprints-102959 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:26:10Z |
| publishDate | 2022 |
| publisher | Elsevier Ltd. |
| record_format | dspace |
| spelling | utm.eprints-1029592023-10-12T08:25:39Z http://eprints.utm.my/102959/ Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison Iftikhar, Bawar Alih, Sophia C. Vafaei, Mohammadreza Elkotb, Mohamed Abdelghany Shutaywi, Meshal Javed, Muhammad Faisal Deebani, Wejdan Khan, M. Ijaz Aslam, Fahid TA Engineering (General). Civil engineering (General) One of the largest sources of greenhouse gas (GHG) emissions is the construction concrete industry which has alone 50% of the world's emissions. One possible remedy to mitigate the effect of environmental issues is the use of waste and recycled material in concrete. Today, immense agricultural waste is being used as a substitute for cement in the production of sustainable concrete. Therefore, this study is aimed to predict and develop an empirical formula of the compressive strength of rice husk ash (RHA) concrete using machine learning algorithms. Methods employed in this study includes gene expression programming (GEP) and Random Forest Regression (RFR). A reliable database of 192 data points was employed for developing the models. Most influential variables including age, cement, rice husk ash, water, super plasticizer, and aggregate were employed as input parameters in the development of RHA-based concrete models. Evaluation of models was performed using different statistical parameters. These statistical measures include mean absolute error (MAE), coefficient of determination (R2), performance index (ρ), root man square error (RMSE), relative squared error (RSE) and relative root mean square (RRMSE). The GEP model outperforms the RFR ensemble model in terms of robustness, with a greater correlation of R2 = 0.96 compared to RFR's R2 = 0.91. Ensemble modeling showed an enhancement of 1.62 percent for RFR compressive strength model when compared with individual RFR compressive strength model as illustrated by statistical parameters. Moreover, GEP model shows an enhancement of 37.33 percent in average error with an average error 2.35 MPa as compared to RFR model with average error of about 3.75 MPa. Cross validation was used as external check to avoid overfitting issues of the models and confirm the generalized model output. Parametric analysis was performed to determine the impact of the input parameters on the output. Cement and age were shown to have a substantial impact on the compressive strength of RHA concrete using sensitivity analysis. Elsevier Ltd. 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/102959/1/BawarIftikhar2022_PredictiveModelingofCompressiveStrength_compressed.pdf Iftikhar, Bawar and Alih, Sophia C. and Vafaei, Mohammadreza and Elkotb, Mohamed Abdelghany and Shutaywi, Meshal and Javed, Muhammad Faisal and Deebani, Wejdan and Khan, M. Ijaz and Aslam, Fahid (2022) Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison. Journal of Cleaner Production, 348 (131285). pp. 1-18. ISSN 0959-6526 http://dx.doi.org/10.1016/j.jclepro.2022.131285 DOI: 10.1016/j.jclepro.2022.131285 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Iftikhar, Bawar Alih, Sophia C. Vafaei, Mohammadreza Elkotb, Mohamed Abdelghany Shutaywi, Meshal Javed, Muhammad Faisal Deebani, Wejdan Khan, M. Ijaz Aslam, Fahid Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison |
| title | Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison |
| title_full | Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison |
| title_fullStr | Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison |
| title_full_unstemmed | Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison |
| title_short | Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison |
| title_sort | predictive modeling of compressive strength of sustainable rice husk ash concrete ensemble learner optimization and comparison |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/102959/1/BawarIftikhar2022_PredictiveModelingofCompressiveStrength_compressed.pdf |
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