Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach
Abstract A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic...
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| Format: | Article |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-54715-4 |
| _version_ | 1797274619511570432 |
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| author | Vahid Shafaie Majid Movahedi Rad |
| author_facet | Vahid Shafaie Majid Movahedi Rad |
| author_sort | Vahid Shafaie |
| collection | DOAJ |
| description | Abstract A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic Algorithm (GA) was seamlessly integrated with PFC3D, a prominent Discrete Element Modeling (DEM) software package. This integration facilitates the exchange of micro-parameter values, where MATLAB’s GA optimizes these parameters, which are then input into PFC3D to simulate the behavior of CSCC mix designs. The calibration process is fully automated through a MATLAB script, complemented by a fish script in PFC, allowing for an efficient and precise calibration mechanism that automatically terminates based on predefined criteria. Central to this approach is the Uniaxial Compressive Strength (UCS) test, which forms the foundation of the calibration process. A distinguishing aspect of this study was the incorporation of pigment effects, reflecting the cohesive behavior of cementitious components, into the micro-parameters influencing the cohesion coefficient within DEM. This innovative approach ensured significant alignment between simulations and observed macro properties, as evidenced by fitness values consistently exceeding 0.94. This investigation not only expanded the understanding of CSCC dynamics but also contributed significantly to the discourse on advanced concrete simulation methodologies, underscoring the importance of multi-objective optimization in such studies. |
| first_indexed | 2024-03-07T15:00:53Z |
| format | Article |
| id | doaj.art-e9681159613d42699b5e28964853c5eb |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:00:53Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e9681159613d42699b5e28964853c5eb2024-03-05T19:09:50ZengNature PortfolioScientific Reports2045-23222024-02-0114111610.1038/s41598-024-54715-4Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approachVahid Shafaie0Majid Movahedi Rad1Department of Structural and Geotechnical Engineering, Széchenyi István UniversityDepartment of Structural and Geotechnical Engineering, Széchenyi István UniversityAbstract A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic Algorithm (GA) was seamlessly integrated with PFC3D, a prominent Discrete Element Modeling (DEM) software package. This integration facilitates the exchange of micro-parameter values, where MATLAB’s GA optimizes these parameters, which are then input into PFC3D to simulate the behavior of CSCC mix designs. The calibration process is fully automated through a MATLAB script, complemented by a fish script in PFC, allowing for an efficient and precise calibration mechanism that automatically terminates based on predefined criteria. Central to this approach is the Uniaxial Compressive Strength (UCS) test, which forms the foundation of the calibration process. A distinguishing aspect of this study was the incorporation of pigment effects, reflecting the cohesive behavior of cementitious components, into the micro-parameters influencing the cohesion coefficient within DEM. This innovative approach ensured significant alignment between simulations and observed macro properties, as evidenced by fitness values consistently exceeding 0.94. This investigation not only expanded the understanding of CSCC dynamics but also contributed significantly to the discourse on advanced concrete simulation methodologies, underscoring the importance of multi-objective optimization in such studies.https://doi.org/10.1038/s41598-024-54715-4Colored self-compacting concrete (CSCC)Genetic algorithm (GA)Automated calibrationUCS testPFC3DMulti-objective optimization |
| spellingShingle | Vahid Shafaie Majid Movahedi Rad Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach Scientific Reports Colored self-compacting concrete (CSCC) Genetic algorithm (GA) Automated calibration UCS test PFC3D Multi-objective optimization |
| title | Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach |
| title_full | Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach |
| title_fullStr | Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach |
| title_full_unstemmed | Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach |
| title_short | Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach |
| title_sort | multi objective genetic algorithm calibration of colored self compacting concrete using dem an integrated parallel approach |
| topic | Colored self-compacting concrete (CSCC) Genetic algorithm (GA) Automated calibration UCS test PFC3D Multi-objective optimization |
| url | https://doi.org/10.1038/s41598-024-54715-4 |
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