Differential evolution–based integrated model for predicting concrete slumps
Concrete slump, a crucial indicator of fluidity, directly affects the pumpability and construction efficiency of concrete. Conventionally, concrete slumps are assessed through multiple test iterations conducted by skilled professionals to obtain accurate results. In this study, a predictive model wa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098624000417 |
| _version_ | 1797259909937496064 |
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| author | Yansheng Liu Ruyan Li Qian Liu Zhen Tian Yuwei Yuan Yufei Hou |
| author_facet | Yansheng Liu Ruyan Li Qian Liu Zhen Tian Yuwei Yuan Yufei Hou |
| author_sort | Yansheng Liu |
| collection | DOAJ |
| description | Concrete slump, a crucial indicator of fluidity, directly affects the pumpability and construction efficiency of concrete. Conventionally, concrete slumps are assessed through multiple test iterations conducted by skilled professionals to obtain accurate results. In this study, a predictive model was established to predict concrete slumps directly based on concrete mix proportions, thereby mitigating labor and time costs. An open-source dataset was used in this study. The selected water-cement ratio ranged from 0.29 to 0.66. Cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate were employed as input parameters. Furthermore, the performances of a variety of algorithms in predicting concrete slumps were analyzed and compared. The algorithms included SVM, KNN, Extra-Trees, Gradient Boosting, Decision Tree, Elastic Net, Lasso, Ridge, Random Forest, Bagging, AdaBoost, and XGBoost. Through a comprehensive comparison of the prediction performance of these models, AdaBoost, Bagging, Extra-Trees, and Random Forest were adopted as base models. Moreover, the SVM algorithm optimized using Differential Evolution was employed as a secondary model to construct an enhanced integrated prediction model. The final prediction model boasts an MSE of 2.099984, MAE of 1.225597, RMSE of 1.449132, and R2 of 0.970418. Compared to conventional prediction models, the proposed model has the potential to substantially improve prediction performance. |
| first_indexed | 2024-03-07T22:53:58Z |
| format | Article |
| id | doaj.art-0f8e7d79e1cc49b8888358eaa634edd8 |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-04-24T23:16:55Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-0f8e7d79e1cc49b8888358eaa634edd82024-03-17T07:54:30ZengElsevierEngineering Science and Technology, an International Journal2215-09862024-03-0151101655Differential evolution–based integrated model for predicting concrete slumpsYansheng Liu0Ruyan Li1Qian Liu2Zhen Tian3Yuwei Yuan4Yufei Hou5School of Resources and Environmental Engineering, Shanghai Polytechnic University, No.2360 Jinhai Road, Shanghai 200120, PR China; Shanghai Collaborative Innovation Centre for WEEE Recycling, No.2360 Jinhai Road, Shanghai 200120, PR ChinaSchool of Resources and Environmental Engineering, Shanghai Polytechnic University, No.2360 Jinhai Road, Shanghai 200120, PR China; Shanghai Collaborative Innovation Centre for WEEE Recycling, No.2360 Jinhai Road, Shanghai 200120, PR China; Corresponding author at: School of Resources and Environmental Engineering, Shanghai Polytechnic University, No.2360 Jinhai Road, Shanghai 200120, PR China.School of Resources and Environmental Engineering, Shanghai Polytechnic University, No.2360 Jinhai Road, Shanghai 200120, PR China; Shanghai Collaborative Innovation Centre for WEEE Recycling, No.2360 Jinhai Road, Shanghai 200120, PR ChinaSchool of Resources and Environmental Engineering, Shanghai Polytechnic University, No.2360 Jinhai Road, Shanghai 200120, PR China; Shanghai Collaborative Innovation Centre for WEEE Recycling, No.2360 Jinhai Road, Shanghai 200120, PR ChinaSchool of Resources and Environmental Engineering, Shanghai Polytechnic University, No.2360 Jinhai Road, Shanghai 200120, PR China; Shanghai Collaborative Innovation Centre for WEEE Recycling, No.2360 Jinhai Road, Shanghai 200120, PR ChinaSchool of Resources and Environmental Engineering, Shanghai Polytechnic University, No.2360 Jinhai Road, Shanghai 200120, PR China; Shanghai Collaborative Innovation Centre for WEEE Recycling, No.2360 Jinhai Road, Shanghai 200120, PR ChinaConcrete slump, a crucial indicator of fluidity, directly affects the pumpability and construction efficiency of concrete. Conventionally, concrete slumps are assessed through multiple test iterations conducted by skilled professionals to obtain accurate results. In this study, a predictive model was established to predict concrete slumps directly based on concrete mix proportions, thereby mitigating labor and time costs. An open-source dataset was used in this study. The selected water-cement ratio ranged from 0.29 to 0.66. Cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate were employed as input parameters. Furthermore, the performances of a variety of algorithms in predicting concrete slumps were analyzed and compared. The algorithms included SVM, KNN, Extra-Trees, Gradient Boosting, Decision Tree, Elastic Net, Lasso, Ridge, Random Forest, Bagging, AdaBoost, and XGBoost. Through a comprehensive comparison of the prediction performance of these models, AdaBoost, Bagging, Extra-Trees, and Random Forest were adopted as base models. Moreover, the SVM algorithm optimized using Differential Evolution was employed as a secondary model to construct an enhanced integrated prediction model. The final prediction model boasts an MSE of 2.099984, MAE of 1.225597, RMSE of 1.449132, and R2 of 0.970418. Compared to conventional prediction models, the proposed model has the potential to substantially improve prediction performance.http://www.sciencedirect.com/science/article/pii/S2215098624000417Concrete slumpFly ashMachine learningModel predictionData MiningComparison |
| spellingShingle | Yansheng Liu Ruyan Li Qian Liu Zhen Tian Yuwei Yuan Yufei Hou Differential evolution–based integrated model for predicting concrete slumps Engineering Science and Technology, an International Journal Concrete slump Fly ash Machine learning Model prediction Data Mining Comparison |
| title | Differential evolution–based integrated model for predicting concrete slumps |
| title_full | Differential evolution–based integrated model for predicting concrete slumps |
| title_fullStr | Differential evolution–based integrated model for predicting concrete slumps |
| title_full_unstemmed | Differential evolution–based integrated model for predicting concrete slumps |
| title_short | Differential evolution–based integrated model for predicting concrete slumps |
| title_sort | differential evolution based integrated model for predicting concrete slumps |
| topic | Concrete slump Fly ash Machine learning Model prediction Data Mining Comparison |
| url | http://www.sciencedirect.com/science/article/pii/S2215098624000417 |
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