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As widely known, concrete is an essential material in civil engineering. However, its properties can vary considerably, depending on the nature and proportions of its constituents, the construction methods applied to create it, and the loading and environmental conditions to which it will be subjected over time. Therefore, the development of control methods to determine the condition and ascertain the quality of concrete is critical. Ultrasonic methods can play an important role in this area, since they allow us to monitor the density and homogeneity of concrete, providing information about the strength development and the existence of internal flaws and defects. To ensure that the information provided is reliable, expert knowledge is needed for interpreting the ultrasonic data. Computational tools that support the interpretation of ultrasonic (and other NDT) data might facilitate the job, reducing bias and helping specialists to analyze, in a consistent way, the great amount of data generated by the test. In this context, the use of Artificial Neural Networks (ANN) techniques is seen as a viable and adequate strategy to develop such tools. This study is focused on the evaluation of the feasibility of developing a specialist ANN tool. This kind of technique allows the emulation of the processes of specialists when dealing with uncertainty. Using a neural model, it is Possible to establish a non-linear correlation between known input data, such as ultrasonic readings and a certain output in this case, compressive strength, because this is the most used parameter to determine concrete quality.

Application of Artificial Neural Networks in Prediction of Compressive Strength of Concrete by Using Ultrasonic Pulse Velocities