Abstract
For structural integrity assessment, when dealing with problems such as uncertainty quantification, real-time structural health monitoring, and reliability analysis, fracture mechanics parameters, e.g. the energy release rate, are required to be evaluated for a large amount of crack configurations. Using finite element (FE) models directly for this task is generally time consuming. This work follows a machine learning-based method aiming at a fast estimation of the fracture mechanics parameters. Towards this end, the offline data are first created by performing the FE simulations for a set of crack configurations from which these parameters can be extracted and then a machine learning algorithm (e.g. artificial neural network (ANN)) is used to build a surrogate model with the obtained data. Using this surrogate model, the above mentioned tasks can be performed with a significantly reduced computational cost while assuring accurate results.
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Acknowledgements
The support of Thu Dau Mot University for this work within the “Modelling and Simulation in the Digital Age – MaSDA” research program is greatly appreciated.
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Hoang, TV., Tran, VX., Nguyen, V.D., Nguyen, Q.H., Bui, V.S. (2021). Assessment of Structural Integrity Using Machine Learning. In: Kumar, R., Quang, N.H., Kumar Solanki, V., Cardona, M., Pattnaik, P.K. (eds) Research in Intelligent and Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-7527-3_57
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DOI: https://doi.org/10.1007/978-981-15-7527-3_57
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