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A platform for research: civil engineering, architecture and urbanism
Acoustic emission source location using Lamb wave propagation simulation and artificial neural network for I-shaped steel girder
Highlights Acoustic emission (AE) for structural health monitoring (SHM) Artificial neural networks (ANN) based source localization methods. Trained neural networks sucussfully predicted the AE source location.
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.
Acoustic emission source location using Lamb wave propagation simulation and artificial neural network for I-shaped steel girder
Highlights Acoustic emission (AE) for structural health monitoring (SHM) Artificial neural networks (ANN) based source localization methods. Trained neural networks sucussfully predicted the AE source location.
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.
Acoustic emission source location using Lamb wave propagation simulation and artificial neural network for I-shaped steel girder
Cheng, Lu (author) / Xin, Haohui (author) / Groves, Roger M. (author) / Veljkovic, Milan (author)
2020-11-12
Article (Journal)
Electronic Resource
English
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