Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 5, 25 May 2023


Open Access | Article

Artificial Intelligence and Machine Learning Applications in to Structural Health Monitoring

Qingfeng Tian * 1
1 Architecture, Cornell University, Ithaca, NY, 14850, USA

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 5, 348-354
Published 25 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Qingfeng Tian. Artificial Intelligence and Machine Learning Applications in to Structural Health Monitoring. TNS (2023) Vol. 5: 348-354. DOI: 10.54254/2753-8818/5/20230581.

Abstract

Numerous civil infrastructures, such as bridges, dams, and skyscrapers are getting to be vulnerable to losing their planned capacities as they break down from utilization. In spite of the fact that many people have been propelled to examine these structures by the concern on a regular basis, there is still diagnosis in need for onsite inspections on closing bridge frameworks or building structures for diagnosis, due to lack of personnel. In that case, many researches team have come up with structural health monitoring (SHM) techniques. This paper will investigate a few strategies for SHM techniques. The scope is primarily on machine learning strategies, such as vision-based strategy for SHM techniques. Based on other correlated articles, vision-based strategy is fine to be used for structural health monitoring and detection. Models that are well defined will be applied to different kinds of issue. There's much to think about and research here. To summarize, machine learning techniques for SHM have been shown to be more successful than traditional strategies. The primary paper employments territorially upgraded multiphase division procedure, which is prepared with SVM, which has been shown to be superior to previous methods. Overall, ML methods are gradually becoming crucial perspectives on SHM challenges.

Keywords

Structural health monitoring, Vision-based, Machine learning, CNN classifier

References

1. Teidj, S., Khamlichi, A., Driouach, A., 2016. Identification of beam cracks by solution of an inverse problem. Procedia Technology 22, 86–93.

2. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 2278–2324.

3. O’Byrne, M., Ghosh, B., Schoefs, F., Pakrashi, V., 2014. Regionally enhanced multiphase segmentation technique for damaged surfaces. Computer-Aided Civil and Infrastructure Engineering 29, 644–658.

4. Ahmadlou, M., Adeli, H., 2010. Enhanced probabilistic neural network with local decision circles: A robust classifier. Integrated Computer-Aided Engineering 17, 197–210.

5. Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25, 1097–1105.

6. Soukup, D., Huber-M¨ork, R., 2014. Convolutional neural networks for steel surface defect detection from photometric stereo images, in: International Symposium on Visual Computing, Springer. pp. 668–677.

7. Cha, Y.J., Choi, W., Bu¨yu¨k¨oztu¨rk, O., 2017. Deep learning-based crack damage detection using convo- lutional neural networks. Computer-Aided Civil and Infrastructure Engineering 32, 361–378.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)
ISBN (Print)
978-1-915371-53-9
ISBN (Online)
978-1-915371-54-6
Published Date
25 May 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/5/20230581
Copyright
25 May 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated