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High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model
Abstract The major different between High Performance Concrete (HPC) and conventional concrete is essentially the use of mineral and chemical admixture. These two admixtures made HPC mechanical behavior act differently compare to conventional concrete at microstructures level. Certain properties of HPC are not fully understood since the relationship between ingredients and concrete properties is highly nonlinear. Therefore, predicting HPC behavior is relatively difficult compared to predicting conventional concrete behavior. This paper proposes an Artificial Intelligence hybrid system to predict HPC compressive strength that fuses Fuzzy Logic (FL), weighted Support Vector Machines (wSVM) and fast messy genetic algorithms (fmGA) into an Evolutionary Fuzzy Support Vector Machine Inference Model for Time Series Data (EFSIMT). Validation results show that the EFSIMT achieves higher performance in comparison with Support Vector Machines (SVM) and obtains results comparable with Back-Propagation Neural Network (BPN). Hence, EFSIMT offers strong potential as a valuable predictive tool for HPC compressive strength.
High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model
Abstract The major different between High Performance Concrete (HPC) and conventional concrete is essentially the use of mineral and chemical admixture. These two admixtures made HPC mechanical behavior act differently compare to conventional concrete at microstructures level. Certain properties of HPC are not fully understood since the relationship between ingredients and concrete properties is highly nonlinear. Therefore, predicting HPC behavior is relatively difficult compared to predicting conventional concrete behavior. This paper proposes an Artificial Intelligence hybrid system to predict HPC compressive strength that fuses Fuzzy Logic (FL), weighted Support Vector Machines (wSVM) and fast messy genetic algorithms (fmGA) into an Evolutionary Fuzzy Support Vector Machine Inference Model for Time Series Data (EFSIMT). Validation results show that the EFSIMT achieves higher performance in comparison with Support Vector Machines (SVM) and obtains results comparable with Back-Propagation Neural Network (BPN). Hence, EFSIMT offers strong potential as a valuable predictive tool for HPC compressive strength.
High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model
Cheng, Min-Yuan (author) / Chou, Jui-Sheng (author) / Roy, Andreas F.V. (author) / Wu, Yu-Wei (author)
Automation in Construction ; 28 ; 106-115
2012-07-27
10 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2012
| British Library Conference Proceedings | 2005
Modelling Concrete Strength Using Support Vector Machines
| British Library Conference Proceedings | 2013