Evaluation Of Compressive Strength Of Concrete Using Ndt And Artificial Intelligence Methods
DOI:
https://doi.org/10.53555/jaz.v45i2.3813Keywords:
adaptive neural fuzzy inference system, artificial intelligence, support vector machine, artificial neural network, concrete strength, non–destructive testing, rebound hammer test, ultrasonic pulse velocityAbstract
Non-destructive testing (NDT) techniques are frequently applied in the field to evaluate the compressive strength of concrete in the construction sector. NDT techniques are comparatively inexpensive and do not harm the current structure. The ultrasonic pulse velocity (UPV) test and the rebound hammer (RH) test are two common NDT techniques. The concrete compressive strength estimates are not particularly precise when compared to the outcomes of the destructive tests, which is one of the main disadvantages of the RH and UPV tests. The researchers used artificial intelligence prediction models to examine the correlations between the input values—the outcomes of the two NDT tests—and the output values—concrete strength—in order to enhance the estimation of concrete strength. In cooperation with a material testing facility and the Professional Civil Engineer Association, in-situ NDT data from 98 samples were gathered. Both conventional statistical and artificial intelligence (AI) prediction models were developed and validated using in-situ NDT data. The analysis's findings demonstrated that, in comparison to statistical regression models, artificial intelligence prediction models yield more accurate estimations. When AI methods (ANNs, SVM, and ANFIS) are used to predict concrete compressive strength in RH and UPV tests, the study findings demonstrate a considerable improvement.
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Copyright (c) 2024 Dr K Ramadevi, Sonal Banchhor, P Sudheer Kumar, Riyaz Syed, B Naga Kiran, Amruta Jagadish Killol

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