Structural Health Monitoring Using Machine Learning Techniques

Title

Structural Health Monitoring Using Machine Learning Techniques

Creator

Monisha P.; Arunraja A.; Judeson Antony Kovilpillai J.

Description

Environmental factors, particularly vibrations and temperature can damage the structural health of the building. To avoid heavy damage to the building and to maintain the building's structural health this paper suggests monitoring of building using machine learning algorithms. Machine learning algorithms are used to predict temperature and vibration damages in buildings. Temperature and vibration values are obtained through the grove vibration sensor and NTC thermistor attached to Raspberry Pi 3B plus. In the Raspberry pi, Machine learning algorithms are executed. The activation functions used are Relu, Sigmoid, and Tanh. The experimental results reveal that the Sigmoid activation function gives the best results in terms of metrics with accuracy 94.25, Precision 0.951, Recall 0.912, and F1 score 0.388. The sigmoid function is used in machine learning algorithms for predicting temperature and vibrations. Predicted temperature and vibrations damages are sent to the server and viewed through the user mobile. K- Nearest Neighbor algorithm produced best results with an accuracy rate of 85.50, Precision of 0.922, Sensitivity of 0.830, Specificity of 0.840 and F1 score of 0.873. 2023 IEEE.

Source

Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, pp. 275-282.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Activation function; Artificial Neural Networks; Decision Tree; K-Nearest Neighbor; Logistic Regression; Machine learning algorithms; Random Forest

Coverage

Monisha P., Sri Ramakrishna Engineering College, Department of Electronics and Communication Engineering, Coimbatore, India; Arunraja A., Christ University (Deemed to Be University), School of Engineering and Technology, Department of Electronics and Communication Engineering, Bengaluru, India; Judeson Antony Kovilpillai J., Sri Ramakrishna Engineering College, Department of Electronics and Communication Engineering, Coimbatore, India

Rights

Restricted Access

Relation

ISBN: 979-835032284-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICPCSN58827.2023.00052

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