Comparative study of classification algorithms for damage classification in smart composite laminates

Asif Khan; Chang-Kyung Ryoo; Heung Soo Kim

doi:10.1117/12.2257296

17 April 2017 Comparative study of classification algorithms for damage classification in smart composite laminates

Asif Khan, Chang-Kyung Ryoo, Heung Soo Kim

Proceedings Volume 10167, Nanosensors, Biosensors, Info-Tech Sensors and 3D Systems 2017; 101671Q (2017) https://doi.org/10.1117/12.2257296
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2017, Portland, Oregon, United States

Abstract

This paper presents a comparative study of different classification algorithms for the classification of various types of inter-ply delaminations in smart composite laminates. Improved layerwise theory is used to model delamination at different interfaces along the thickness and longitudinal directions of the smart composite laminate. The input-output data obtained through surface bonded piezoelectric sensor and actuator is analyzed by the system identification algorithm to get the system parameters. The identified parameters for the healthy and delaminated structure are supplied as input data to the classification algorithms. The classification algorithms considered in this study are ZeroR, Classification via regression, Naïve Bayes, Multilayer Perceptron, Sequential Minimal Optimization, Multiclass-Classifier, and Decision tree (J48). The open source software of Waikato Environment for Knowledge Analysis (WEKA) is used to evaluate the classification performance of the classifiers mentioned above via 75-25 holdout and leave-one-sample-out cross-validation regarding classification accuracy, precision, recall, kappa statistic and ROC Area.

Citation Download Citation

Asif Khan, Chang-Kyung Ryoo, and Heung Soo Kim "Comparative study of classification algorithms for damage classification in smart composite laminates", Proc. SPIE 10167, Nanosensors, Biosensors, Info-Tech Sensors and 3D Systems 2017, 101671Q (17 April 2017); https://doi.org/10.1117/12.2257296

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