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Artificial Intelligence-based Compressive Strength Prediction of Medium to High Strength Concrete
https://orcid.org/http://orcid.org/0000-0002-6740-4523
Predicting the compressive strength of concrete before casting is a requisite criterion. This research develops an artificial intelligence-based model for compressive strength prediction based on experimental data. A laboratory experimental program, covering the main variables that affect the test results, was conducted. Then, neural network (ANN) and neuro-fuzzy (ANFIS) models were developed based on four targets compressive strength (40, 60, 70, and 80 MPa), three testing ages (7, 28, and 90 days), three protocols of curing, two different specimens shapes (cylinder and cube), and three different specimens sizes. The model output, the predicted compressive strength, revealed a good agreement with the experimental test results for both ANN and ANFIS approaches. The correlation coefficients for ANN and ANFIS models are 0.976 and 0.989, respectively. The best results are obtained by ANFIS because it provides a slightly lower root mean squared error and a higher correlation coefficient.
Artificial Intelligence-based Compressive Strength Prediction of Medium to High Strength Concrete
https://orcid.org/http://orcid.org/0000-0002-6740-4523
Predicting the compressive strength of concrete before casting is a requisite criterion. This research develops an artificial intelligence-based model for compressive strength prediction based on experimental data. A laboratory experimental program, covering the main variables that affect the test results, was conducted. Then, neural network (ANN) and neuro-fuzzy (ANFIS) models were developed based on four targets compressive strength (40, 60, 70, and 80 MPa), three testing ages (7, 28, and 90 days), three protocols of curing, two different specimens shapes (cylinder and cube), and three different specimens sizes. The model output, the predicted compressive strength, revealed a good agreement with the experimental test results for both ANN and ANFIS approaches. The correlation coefficients for ANN and ANFIS models are 0.976 and 0.989, respectively. The best results are obtained by ANFIS because it provides a slightly lower root mean squared error and a higher correlation coefficient.
Artificial Intelligence-based Compressive Strength Prediction of Medium to High Strength Concrete
https://orcid.org/http://orcid.org/0000-0002-6740-4523
Predicting the compressive strength of concrete before casting is a requisite criterion. This research develops an artificial intelligence-based model for compressive strength prediction based on experimental data. A laboratory experimental program, covering the main variables that affect the test results, was conducted. Then, neural network (ANN) and neuro-fuzzy (ANFIS) models were developed based on four targets compressive strength (40, 60, 70, and 80 MPa), three testing ages (7, 28, and 90 days), three protocols of curing, two different specimens shapes (cylinder and cube), and three different specimens sizes. The model output, the predicted compressive strength, revealed a good agreement with the experimental test results for both ANN and ANFIS approaches. The correlation coefficients for ANN and ANFIS models are 0.976 and 0.989, respectively. The best results are obtained by ANFIS because it provides a slightly lower root mean squared error and a higher correlation coefficient.
Artificial Intelligence-based Compressive Strength Prediction of Medium to High Strength Concrete
Iran J Sci Technol Trans Civ Eng
Al-Haidari, Hawraa Saeed Jawad (Autor:in) / Al-Haydari, Israa Saeed (Autor:in)
01.04.2022
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Determination of Compressive Strength of Concrete via Artificial Intelligence
| BASE | 2020