Applying Artificial Intelligence to Improve On-Site Non-Destructive Concrete Compressive Strength Tests
| Autor: | Yu-Ren Wang, Tu Quynh Loan Ngo, Dai-Lun Chiang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
General Chemical Engineering law.invention Inorganic Chemistry law Nondestructive testing support vector machine General Materials Science Hammer concrete strength non–destructive testing Adaptive neuro fuzzy inference system Crystallography Artificial neural network business.industry adaptive neural fuzzy inference system Regression analysis Statistical model Structural engineering rebound hammer test artificial intelligence Condensed Matter Physics ultrasonic pulse velocity Compressive strength QD901-999 business artificial neural network Predictive modelling |
| Zdroj: | Crystals, Vol 11, Iss 1157, p 1157 (2021) Crystals Volume 11 Issue 10 |
| ISSN: | 2073-4352 |
| DOI: | 10.3390/cryst11101157 |
| Popis: | In the construction industry, non–destructive testing (NDT) methods are often used in the field to inspect the compressive strength of concrete. NDT methods do not cause damage to the existing structure and are relatively economical. Two popular NDT methods are the rebound hammer (RH) test and the ultrasonic pulse velocity (UPV) test. One major drawback of the RH test and UPV test is that the concrete compressive strength estimations are not very accurate when comparing them to the results obtained from the destructive tests. To improve concrete strength estimation, the researchers applied artificial intelligence prediction models to explore the relationships between the input values (results from the two NDT tests) and the output values (concrete strength). In-situ NDT data from a total of 98 samples were collected in collaboration with a material testing laboratory and the Professional Civil Engineer Association. In-situ NDT data were used to develop and validate the prediction models (both traditional statistical models and AI models). The analysis results showed that AI prediction models provide more accurate estimations when compared to statistical regression models. The research results show significant improvement when AI techniques (ANNs, SVM and ANFIS) are applied to estimate concrete compressive strength in RH and UPV tests. |
| Databáze: | OpenAIRE |
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