Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests - Loan T. Q. Ngo; Yu-Ren Wang; Yi-Ming Chen (Advances in Civil Engineering)
When inspecting the property of material, nondestructive testing methods are more preferable than destructive testing since they do not damage the test sample. Nondestructive testing methods, however, might not yield the same accurate results in examining the property of material when compared with destructive testing. To improve the result of nondestructive testing methods, this research applies artificial neural networks and adaptive neural fuzzy inference system in predicting the concrete strength estimation using nondestructive testing method, the ultrasonic pulse velocity test. In this research, data from a total of 312 cylinder concrete samples were collected. Ultrasonic pulse velocity test was applied to those 312 samples in the lab, following the ASTM procedure. Then, the testing results of 312 samples were used to develop and validate two artificial intelligence prediction models. The research results show that artificial intelligence prediction models are more accurate than statistical regression models in terms of the mean absolute percentage error.