Academic Journal
Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence
| العنوان: | Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence |
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| المؤلفون: | Tu T. Nguyen, Hoa Pham Duy, Tung Pham Thanh, Hoang Hiep Vu |
| المصدر: | Advances in Civil Engineering, Vol 2020 (2020) |
| بيانات النشر: | Hindawi Limited, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Engineering (General). Civil engineering (General) |
| مصطلحات موضوعية: | Engineering (General). Civil engineering (General), TA1-2040 |
| الوصف: | This paper describes the application of two artificial intelligence- (AI-) based methods to predict the 28-day compressive strength of fiber-reinforced high-strength self-compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sources were used for training, validation, and testing models. Various AI models were developed with different training algorithms and a number of nodes in the hidden layer to obtain the optimal model for the FRHSSCC data. It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. Specifically, the overall coefficient of determination (R2) of the ANN and ANFIS models was 0.9742 and 0.9584, respectively. The sensitivity analysis was also conducted with the ANN model to investigate the effects of input parameters on the output. The results from the sensitivity analysis revealed that the compressive strength of FRHSSCC at 28 days was more sensitive with the changes of water by cement ratio (WCR) parameter and insensitive with varying amounts of fiber (VOF). Finally, it can be concluded that the application of artificial intelligence shows the great potential in the prediction of compressive strength of FRHSSCC. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1687-8086 1687-8094 |
| Relation: | https://doaj.org/toc/1687-8086; https://doaj.org/toc/1687-8094 |
| DOI: | 10.1155/2020/3012139 |
| URL الوصول: | https://doaj.org/article/2db3857d26904be9b46f7819c519ef54 |
| رقم الانضمام: | edsdoj.2db3857d26904be9b46f7819c519ef54 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 16878086 16878094 |
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| DOI: | 10.1155/2020/3012139 |