Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Investigation of the Flexural Strength of Steel Fiber Reinforced Geopolymer Concrete Using Machine Learning Techniques
Geopolymer composites have been studied and applied in many construction fields because of good performance in mechanical properties, workability as well as durability after long using time. The development of artificial intelligence proposes some methods which can predict and determine efficiently the performance of concrete structures through experimental data. The prediction and validation of the performance of fiber reinforced geopolymer composites by machine learning is evaluated in this research. The proposed models use artificial neural network ANN, deep neural network DNN, and 245 experimental datasets with 9 input variables. The validation of machine learning approaches shows the effectiveness of predictive methods with 90%, and 85% in ANN, and DNN respectively. The proposed models can be applied for designing the standard mix for steel fiber reinforced geopolymer concrete.
Investigation of the Flexural Strength of Steel Fiber Reinforced Geopolymer Concrete Using Machine Learning Techniques
Geopolymer composites have been studied and applied in many construction fields because of good performance in mechanical properties, workability as well as durability after long using time. The development of artificial intelligence proposes some methods which can predict and determine efficiently the performance of concrete structures through experimental data. The prediction and validation of the performance of fiber reinforced geopolymer composites by machine learning is evaluated in this research. The proposed models use artificial neural network ANN, deep neural network DNN, and 245 experimental datasets with 9 input variables. The validation of machine learning approaches shows the effectiveness of predictive methods with 90%, and 85% in ANN, and DNN respectively. The proposed models can be applied for designing the standard mix for steel fiber reinforced geopolymer concrete.
Investigation of the Flexural Strength of Steel Fiber Reinforced Geopolymer Concrete Using Machine Learning Techniques
Lecture Notes in Civil Engineering
Reddy, J. N. (Herausgeber:in) / Wang, Chien Ming (Herausgeber:in) / Luong, Van Hai (Herausgeber:in) / Le, Anh Tuan (Herausgeber:in) / Pham, Khoa V. A. (Autor:in) / Nguyen, Tan Khoa (Autor:in) / Nguyen, Thien Thanh (Autor:in) / Le, Anh Tuan (Autor:in)
The International Conference on Sustainable Civil Engineering and Architecture ; 2023 ; Da Nang City, Vietnam
12.12.2023
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Steel fiber , Geopolymer concrete , Artificial neural network , Deep neural network , Flexural strength Energy , Sustainable Architecture/Green Buildings , Structural Materials , Geotechnical Engineering & Applied Earth Sciences , Building Construction and Design , Construction Management , Engineering
| BASE | 2017
| BASE | 2021
Flexural Strength Assessment of Steel Fiber Reinforced Concrete
| British Library Online Contents | 1999