Abstract
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.
Highlights
Rebound Hammer (RH) and Ultrasonic Pulse Velocity (UPV) tests are two popular non–destructive testing (NDT) methods that can be used to estimate the compressive strength of concrete
To obtain the actual concrete compressive strength, the core samples that were taken at each location were brought back to the laboratory for destructive tests
The model prediction accuracy was measured by the root mean square error (RMSE), the mean absolute percentage error (MAPE), the mean absolute error (MAE), the mean forecast error (MFE), and the error to signal ratio (ESR), as illustrated in the following equations: s
Summary
Non–destructive testing (NDT) methods are used to examine the compressive strength of concrete because they are important alternatives to destructive tests, and in the meantime, are relatively easy to conduct and are economical. If destructive methods are used in the lab, the resulting compressive strength test results would not accurately represent the quality of the in-situ cast concrete. This is because the strength and quality of the in-situ concrete might be affected by many factors such as transportation, placement, tamping and curing. Testing core samples from an existing structure is a better way to examine the concrete quality.
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