Abstract
Acoustic emission (AE) is often used for structural health monitoring (SHM) in the wide field of engineering structures and one of its most beneficial attributes is the ability to localize the damage/crack based on the AE events. The vast majority of ongoing work on AE monitoring focues on geometrically simple structures or a confined area, but the AE source location strategies are rather complicated for real engineering structures. In this paper, an effective method for source localization in realistic structures is presented based on the application of artificial neural networks (ANN), using finite element (FE) simulation results of Lamb waves as the modelling basis. Pencil lead break experiments and related FE simulations on a steel-concrete composite girder are conducted to evaluate the performance of the method. The identification of different wave modes is carried by comparing alternative onset time detection methods. Numerical results are found to be matching closely with the experimental results. To get a reliable ANN model, the validated FE model is used to create a comprehensive database with five different sensor arrangements. It is found that the proposed method is superior to the classical Time of Arrival (TOA) method with the same input data. The results indicate that using trained neural networks based on numerical data is a viable option for AE source location in the case of the I-shaped girder, increasing the likelihood of design and optimization of the AE technique in monitoring realistic structures.
Highlights
Many in-service structures suffer the problems of cumulative damages resulting from overloading and fatigue cracks with increased age [1]
Based on the results showed in this paper, time of arrival (TOA) method could not guarantee the prediction accuracy and artificial neural networks (ANN)-based method could identify the damage even with a less number of sensors for the I-shape steel girder investigated in this paper
The main objective of this study is to investigate an alternative and reliable localization method for global monitoring of life-size complex structures based on Artificial Neural Networks and Lamb Wave propagation simulation
Summary
Many in-service structures suffer the problems of cumulative damages resulting from overloading and fatigue cracks with increased age [1]. The use of the AE technique provides the potential for early damage detection and real-time monitoring of the structures [3,4]. Identifying the source location can allow an accurate global investigation of a structure and a prior understanding of the specific possible damaged/cracked area [5]. It can lead to a better insight into the nature of the source mechanism, as certain AE sources are only related to a particular load case and geometric characteristics [6]. The source mechanism under a certain load regime can be defined more accurately
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