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.

Application of Elastic Waves and Neural Networks for the Prediction of Forces in Bolts of Flange Connections Subjected to Static Tension Tests

“…On the basis of the trained ANN, axial force prediction was carried out for bolts that were not equipped with force sensors. The values obtained were consistent with those of other bolts, which was not possible in previous studies [11,17]. In this work, the dimensionality reduction of measured wave signals (neural network encoder) gave meaningful improvement to the results of the identification of the force in the bolts.…”

“…However, the issue of cyclic loading is very important, and further research in this area is needed. Examples of experimental studies carried out so far and the preliminary results of the axial forces' identification can be found in the literature [11,17].…”

“…In previous works [11,17], it was assumed that data from all bolts (2, 4, 5) were to be used for the purpose of ANN training, assuming a constant distribution of patterns for testing and validation. The new approach proposed herein involves the learning, testing, and validation patterns being separated into individual bolts from a given connection.…”

Detection of Anomaly in a Pretensioned Bolted Beam-to-Column Connection Node Using Digital Image Correlation and Neural Networks

“…Though many types of ANNs are available, simple feedforward networks are still very popular for the structure condition monitoring of buildings. An exemplary application of ANNs in the considered field can be found in many areas, e.g., in damage detection based on time signals registered during dynamic measurements [51], in an assessment of building damage and safety after an earthquake [52], and in the identification of internal forces in pretensioned bolts [4].…”

“…The identification of forces in bolts may be performed using different approaches, among which there are elastic wave propagation phenomenon-based methods as well as ultrasonic and vibration-based methods. Some attempts to determine the value of forces in bolts in a complex flange connection using elastic wave propagation, via Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), are presented in [4]. Guided waves were applied by Yang et al, see [5], to the issue of the detection of changes in the lap joint.…”