Decision tree models for the estimation of geo-polymer concrete compressive strength
The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of a...
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AIMS Press
2024-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024061?viewType=HTML |
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| author | Ji Zhou Zhanlin Su Shahab Hosseini Qiong Tian Yijun Lu Hao Luo Xingquan Xu Chupeng Chen Jiandong Huang |
| author_facet | Ji Zhou Zhanlin Su Shahab Hosseini Qiong Tian Yijun Lu Hao Luo Xingquan Xu Chupeng Chen Jiandong Huang |
| author_sort | Ji Zhou |
| collection | DOAJ |
| description | The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash–Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R<sup>2</sup> of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a<sup>20</sup> of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R<sup>2</sup> of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a<sup>20</sup> of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R<sup>2</sup> were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models. |
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| institution | Directory Open Access Journal |
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| publishDate | 2024-01-01 |
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| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-6570e835d6a64b05b9d318446c97e0bc2024-02-04T01:30:58ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-01-012111413144410.3934/mbe.2024061Decision tree models for the estimation of geo-polymer concrete compressive strengthJi Zhou0Zhanlin Su1 Shahab Hosseini2Qiong Tian3Yijun Lu 4Hao Luo5Xingquan Xu 6Chupeng Chen 7Jiandong Huang81. College of Civil and Environmental Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China2. Shandong Energy Group Xinwen Mining Co., Ltd., Taian 271233, China3. Faculty of the Engineering, Tarbiat Modares University, Jalal AleAhmad, Nasr, Tehran, Iran1. College of Civil and Environmental Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China4. School of Civil Engineering, Guangzhou University, Guangzhou 510006, China4. School of Civil Engineering, Guangzhou University, Guangzhou 510006, China5. Guangdong Hualu Transport Technology Co., Ltd, Guangzhou, China4. School of Civil Engineering, Guangzhou University, Guangzhou 510006, China 5. Guangdong Hualu Transport Technology Co., Ltd, Guangzhou, China4. School of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaThe green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash–Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R<sup>2</sup> of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a<sup>20</sup> of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R<sup>2</sup> of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a<sup>20</sup> of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R<sup>2</sup> were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.https://www.aimspress.com/article/doi/10.3934/mbe.2024061?viewType=HTMLgeo-polymer concretecompressive strengthsuper learnerextreme gradient boostingdecision treerandom forest |
| spellingShingle | Ji Zhou Zhanlin Su Shahab Hosseini Qiong Tian Yijun Lu Hao Luo Xingquan Xu Chupeng Chen Jiandong Huang Decision tree models for the estimation of geo-polymer concrete compressive strength Mathematical Biosciences and Engineering geo-polymer concrete compressive strength super learner extreme gradient boosting decision tree random forest |
| title | Decision tree models for the estimation of geo-polymer concrete compressive strength |
| title_full | Decision tree models for the estimation of geo-polymer concrete compressive strength |
| title_fullStr | Decision tree models for the estimation of geo-polymer concrete compressive strength |
| title_full_unstemmed | Decision tree models for the estimation of geo-polymer concrete compressive strength |
| title_short | Decision tree models for the estimation of geo-polymer concrete compressive strength |
| title_sort | decision tree models for the estimation of geo polymer concrete compressive strength |
| topic | geo-polymer concrete compressive strength super learner extreme gradient boosting decision tree random forest |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024061?viewType=HTML |
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