Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm
Abstract Optimizing the selection of concrete proportion is the key to ensuring the strength, durability and cost-effectiveness of construction projects. However, the traditional mixed proportion method is costly, time-consuming and highly inconsistent. Therefore, this study aims to explore a new me...
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| Format: | Article |
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Springer
2025-03-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06603-3 |
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| author | Shuhong Zuo Benyi Liu |
| author_facet | Shuhong Zuo Benyi Liu |
| author_sort | Shuhong Zuo |
| collection | DOAJ |
| description | Abstract Optimizing the selection of concrete proportion is the key to ensuring the strength, durability and cost-effectiveness of construction projects. However, the traditional mixed proportion method is costly, time-consuming and highly inconsistent. Therefore, this study aims to explore a new method to optimize the existing specific mixing ratio. In this study, support vector machine regression algorithm is used to establish a function that maps the existing composition data to the strength of concrete, and accurately predicts the performance of concrete. Then the predicted performance index is passed to the genetic algorithm. The experimental results show that the fitness value of HPO–SVM is 0.021, which is significantly lower than that of SSA–SVM and PSO–SVM, indicating the superiority of HPO in optimizing SVM. In addition, the average error value of HPO–SVM in specific proportion prediction is 1.226, which verifies the stability and accuracy of the prediction. The results show that the enhanced genetic algorithm has obvious advantages in improving convergence speed and solution quality. The concrete produced by the optimized mixture ratio has stronger compressive strength and bending strength. This study provides not only new methodological achievements, but also has important application value and popularization prospects in engineering practice and education, which is helpful to realize the goal of sustainable development of the construction industry and conforms to the development trend of green environmental protection. |
| format | Article |
| id | doaj-art-655aed80f70d46af9c5d019043269e43 |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-655aed80f70d46af9c5d019043269e432025-08-20T03:05:45ZengSpringerDiscover Applied Sciences3004-92612025-03-017311810.1007/s42452-025-06603-3Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithmShuhong Zuo0Benyi Liu1School of Civil Engineering, Heilongjiang UniversityHeilongjiang Province Hydrology and Water Resources CenterAbstract Optimizing the selection of concrete proportion is the key to ensuring the strength, durability and cost-effectiveness of construction projects. However, the traditional mixed proportion method is costly, time-consuming and highly inconsistent. Therefore, this study aims to explore a new method to optimize the existing specific mixing ratio. In this study, support vector machine regression algorithm is used to establish a function that maps the existing composition data to the strength of concrete, and accurately predicts the performance of concrete. Then the predicted performance index is passed to the genetic algorithm. The experimental results show that the fitness value of HPO–SVM is 0.021, which is significantly lower than that of SSA–SVM and PSO–SVM, indicating the superiority of HPO in optimizing SVM. In addition, the average error value of HPO–SVM in specific proportion prediction is 1.226, which verifies the stability and accuracy of the prediction. The results show that the enhanced genetic algorithm has obvious advantages in improving convergence speed and solution quality. The concrete produced by the optimized mixture ratio has stronger compressive strength and bending strength. This study provides not only new methodological achievements, but also has important application value and popularization prospects in engineering practice and education, which is helpful to realize the goal of sustainable development of the construction industry and conforms to the development trend of green environmental protection.https://doi.org/10.1007/s42452-025-06603-3Concrete proportionProportional optimization methodSupport vector machine regressionEnhanced genetic algorithm |
| spellingShingle | Shuhong Zuo Benyi Liu Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm Discover Applied Sciences Concrete proportion Proportional optimization method Support vector machine regression Enhanced genetic algorithm |
| title | Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm |
| title_full | Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm |
| title_fullStr | Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm |
| title_full_unstemmed | Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm |
| title_short | Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm |
| title_sort | optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm |
| topic | Concrete proportion Proportional optimization method Support vector machine regression Enhanced genetic algorithm |
| url | https://doi.org/10.1007/s42452-025-06603-3 |
| work_keys_str_mv | AT shuhongzuo optimizationdesignofconcretemixproportionbasedonsupportvectormachineregressionandenhancedgeneticalgorithm AT benyiliu optimizationdesignofconcretemixproportionbasedonsupportvectormachineregressionandenhancedgeneticalgorithm |