Axial force prediction based on signals of the elastic wave propagation and artificial neural networks

Abstract

The identification of internal forces is not only important to preserve the structure integrity but also to understand how their certain elements and connections work. Two examples of laboratory test are discussed in this paper. The first is related to an aluminium rod mounted in a stand where compression load was applied. Due to the relaxation phenomenon force prediction becomes even more important in case of compressed bolts. Thus, the second example is related to a bolted flange connection during static tensile test. Four out of six bolts were equipped with washer load cells. Alternatively, selected bolts were equipped with piezoelectric transducers (actuator and sensor) in order to measure signals of elastic waves. It was noted that the load increasing causes changes in the measured signals. Principal components analysis was used for dimensionality reduction of measured signals. The aim of this study is to investigate the use of elastic waves and artificial neural networks for the purpose of the force of identification. Examples of preliminary results have shown that axial forces may be estimated with relatively good accuracy.