Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete
The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/11/2/44 |
| _version_ | 1797406803616595968 |
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| author | Fernando A. N. Silva João M. P. Q. Delgado Rosely S. Cavalcanti António C. Azevedo Ana S. Guimarães Antonio G. B. Lima |
| author_facet | Fernando A. N. Silva João M. P. Q. Delgado Rosely S. Cavalcanti António C. Azevedo Ana S. Guimarães Antonio G. B. Lima |
| author_sort | Fernando A. N. Silva |
| collection | DOAJ |
| description | The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model. |
| first_indexed | 2024-03-09T03:31:54Z |
| format | Article |
| id | doaj.art-d5c55e0b5da14fb49288be73201db6a7 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T03:31:54Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-d5c55e0b5da14fb49288be73201db6a72023-12-03T14:55:06ZengMDPI AGBuildings2075-53092021-01-011124410.3390/buildings11020044Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of ConcreteFernando A. N. Silva0João M. P. Q. Delgado1Rosely S. Cavalcanti2António C. Azevedo3Ana S. Guimarães4Antonio G. B. Lima5Departamento de Engenharia Civil, Universidade Católica de Pernambuco, Recife PE 50050-900, BrazilCONSTRUCT-LFC, Department of Civil Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalDepartamento de Engenharia Civil, Universidade Católica de Pernambuco, Recife PE 50050-900, BrazilCONSTRUCT-LFC, Department of Civil Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalCONSTRUCT-LFC, Department of Civil Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalDepartment of Mechanical Engineering, Federal University of Campina Grande, Campina Grande 58429-900, BrazilThe work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.https://www.mdpi.com/2075-5309/11/2/44artificial neural networkscompressive strengthconcretenondestructive testingproperties of concrete |
| spellingShingle | Fernando A. N. Silva João M. P. Q. Delgado Rosely S. Cavalcanti António C. Azevedo Ana S. Guimarães Antonio G. B. Lima Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete Buildings artificial neural networks compressive strength concrete nondestructive testing properties of concrete |
| title | Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete |
| title_full | Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete |
| title_fullStr | Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete |
| title_full_unstemmed | Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete |
| title_short | Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete |
| title_sort | use of nondestructive testing of ultrasound and artificial neural networks to estimate compressive strength of concrete |
| topic | artificial neural networks compressive strength concrete nondestructive testing properties of concrete |
| url | https://www.mdpi.com/2075-5309/11/2/44 |
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