USING A BACK-PROPAGATION ALGORITHM TO CREATE A NEURAL NETWORK FOR INTERPRETING ULTRASONIC READINGS OF CONCRETE A. Lorenzi 1 , L. C. P. Silva Filho 1 , and J. L. Campagnolo 1 1 Universidade Federal do Rio Grande do Sul, Laboratório de Ensaios e Modelos Estruturais (LEME), Porto Alegre, RS, Brasil Abstract: 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 though 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 age and ultrasonic readings and a certain output (in this case, compressive strength, because this is the most used parameter to determine concrete quality). The net was trained using a back propagation algorithm to minimize the mean squared error. The results obtained indicate that, with four layers of perceptrons, the estimation power of the neural network is better than using traditional modeling techniques, such as regression analysis. Introduction: There are several techniques for modeling the process of converting data into information that try to emulate the human ability to reason. The use of Artificial Neural Networks (ANN) is a new alternative, capable of solving complex problems using an “artificial reasoning system” constructed with basis on the human brain. These computational tools were inspired by the analysis of the neural structure of intelligent organisms and use knowledge acquired through the analysis of previous experiences to develop correlations between known initial conditions and results. The basic idea is to reproduce the vast array of relationships that are established between individual brain neurons, using different synaptic pathways to determine the output to a certain stimulus. The neurons are the basic building blocks of the human brain, one of the most efficient “processing machines” known to date. Nonetheless, the chemically-based biological neurons are much slower than silicon logic gates. The brain makes up for the slow rate of operation because [3]:  There are a huge number of nerve cells and interconnections between them inside the brain. Besides, the functions performed by a biological neuron seem to be much broader than those of a logic gate.  The brain is very energy efficient. It consumes only about 10 -16 joules per operation per second, comparing with 10 -6 joules per operation per second for a digital computer. The brain are a highly complex, non-linear, parallel information processing system, which performs tasks like pattern recognition and visual processing many times faster than the fastest digital computers. However, the human brain might be influenced by emotional or technical bias, and it is not very efficient when dealing with problems of ranking involving a large number of items. Furthermore, it takes a long time to train a human brain to perform to the best of its abilities. There are situations when using an artificial, less efficient, but more organized tool might be useful. In this way, ANNs might alleviate experts and contribute to produce quick and unbiased assessments that might help decision-makers to deal with uncertain and complex matters. Using Artificial Neural Networks: Due to their nature, ANNs are very useful for analyzing complex problems where the relationships between input and output data are not very well known, such as pattern and speech recognition, machine vision, robotics, signal processing and optimization. They are also useful in fields where there is a high degree of uncertainty, such as market analysis, analysis and control of industrial processes and medical diagnosis. In the case of civil engineering, the ANNs have already begun to be used in problems of structural diagnosis or work programming. The present work describes the preliminary results of a research effort aimed at