Structural Health Monitoring using Varied Machine Learning Algorithms
DOI:
https://doi.org/10.14419/ijet.v7i3.12.16503Keywords:
SHM, Machine Learning Algorithms, WEKA.Abstract
Structural Health Monitoring is essential in today’s world where large amount of money and labour are involved in building a structure. There arises a need to periodically check whether the built structure is strong and flawless, also how long it will be strong and if not how much it is damaged. These information are needed so that the precautions can be made accordingly. Otherwise, it may result in disastrous accidents which may take away even human lives. There are various methods to evaluate a structure. In this paper, we apply various classification algorithms like J48, Naive Bayes and many other classifiers available, to the dataset to check on the accuracy of the prediction determined by all of these classification algorithms and ar-rive at the conclusion of the best possible classifier to say whether a structure is damaged or not.
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Received date: July 29, 2018
Accepted date: July 29, 2018
Published date: July 20, 2018