Abstract
Improving prediction accuracy has been a major challenge for damage diagnosis system in the field of structural health monitoring. To tackle this issue, several machine learning algorithms have been used. This study presents effectiveness in improving prediction accuracy of meta-learning model over a range of individual machine learning algorithms in damage diagnosis. The learning algorithm chosen in this paper is support vector machine, random forest, vote method, gradient boosting regression and stacked regression as meta-model. The learning algorithms are employed for debonding quantification in metallic stiffened plate. The algorithms trained and tested on numerically simulated first mode shape vibration data. To check robustness of algorithms, artificial noise is added in numerically simulated data. The result showed that the prediction accuracy of the meta-model as stacked regression is better than the individual model.
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Kumar, A., Guha, A., Banerjee, S. (2021). Improving Prediction Accuracy for Debonding Quantification in Stiffened Plate by Meta-Learning Model. In: Tiwari, S., Suryani, E., Ng, A.K., Mishra, K.K., Singh, N. (eds) Proceedings of International Conference on Big Data, Machine Learning and their Applications. Lecture Notes in Networks and Systems, vol 150. Springer, Singapore. https://doi.org/10.1007/978-981-15-8377-3_5
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DOI: https://doi.org/10.1007/978-981-15-8377-3_5
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