Artificial neural networks-based ultrasonic pulse velocity prediction model for concrete structures

Accurately predicting the Ultrasonic Pulse Velocity (UPV) in concrete is important invarious fields, including construction, engineering and non-destructive testing. This research paper proposes a novel mathematical model based on Artificial Neural Networks (ANN) applied to accurately predict the UPV of concrete. The proposed model was formulated and validated through comprehensive experimental measurements, which were divided into three subsets for training, validating and testing the model. The training process involved adjusting the model’s parameters through iterative optimization techniques to minimize prediction errors. A separate group of measurements was used to validate the model’s precision and generality. The results demonstrate that the suggested equation matches the data from experiments well. Remarkably, the model achieved a high level of accuracy in predicting UPV values, with an error rate of less than 2% when compared to the measured experimental values. These results validate the effectiveness of the developed ANN model for accurate UPV predictions in concrete structures. The practical applications of the model extend to concrete characterization and non-destructive testing, enabling efficient quality control and assessment of structural integrity. Additionally, the model facilitates concrete characterization and mix design optimization, developing more durable and sustainable structures.