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Review

Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring

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Geoinformatics Laboratory, Department of Computer Science and Engineering, Rajiv Gandhi Institute of Petroleum Technology, Amethi 229304, Uttar Pradesh, India
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Machine Learning and Automation Laboratory, Department of Petroleum Engineering and Geoengineering, Rajiv Gandhi Institute of Petroleum Technology, Amethi 229304, Uttar Pradesh, India
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Department of Civil Engineering, Indian Institute of Technology, Kanpur 208016, Uttar Pradesh, India
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Department of Electronics Engineering, Rajiv Gandhi Institute of Petroleum Technology, Amethi 229304, Uttar Pradesh, India
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Department of Chemical Engineering and Biochemical Engineering, Rajiv Gandhi Institute of Petroleum Technology, Jais 229304, Uttar Pradesh, India
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Department of Materials Science and Engineering, Ajou University, Suwon 16499, Korea
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Department of Materials Science and Engineering, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea
*
Authors to whom correspondence should be addressed.
Equal contribution.
Academic Editor: Bálint Medgyes
Metals 2021, 11(10), 1537; https://doi.org/10.3390/met11101537
Received: 29 August 2021 / Revised: 20 September 2021 / Accepted: 22 September 2021 / Published: 27 September 2021
(This article belongs to the Special Issue Reliability Aspects of Lead-Free Solder Alloys Used in Electronics)
Real-time health monitoring of civil infrastructures is performed to maintain their structural integrity, sustainability, and serviceability for a longer time. With smart electronics and packaging technology, large amounts of complex monitoring data are generated, requiring sophisticated artificial intelligence (AI) techniques for their processing. With the advancement of technology, more complex AI models have been applied, from simple models to sophisticated deep learning (DL) models, for structural health monitoring (SHM). In this article, a comprehensive review is performed, primarily on the applications of AI models for SHM to maintain the sustainability of diverse civil infrastructures. Three smart data capturing methods of SHM, namely, camera-based, smartphone-based, and unmanned aerial vehicle (UAV)-based methods, are also discussed, having made the utilization of intelligent paradigms easier. UAV is found to be the most promising smart data acquisition technology, whereas convolution neural networks are the most impressive DL model reported for SHM. Furthermore, current challenges and future perspectives of AI-based SHM systems are also described separately. Moreover, the Internet of Things (IoT) and smart city concepts are explained to elaborate on the contributions of intelligent SHM systems. The integration of SHM with IoT and cloud-based computing is leading us towards the evolution of future smart cities. View Full-Text
Keywords: electronics packaging; lead-free solders; structural health monitoring; civil infrastructure; damage detection; pipeline leakage detection electronics packaging; lead-free solders; structural health monitoring; civil infrastructure; damage detection; pipeline leakage detection
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MDPI and ACS Style

Sharma, V.B.; Tewari, S.; Biswas, S.; Lohani, B.; Dwivedi, U.D.; Dwivedi, D.; Sharma, A.; Jung, J.P. Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring. Metals 2021, 11, 1537. https://doi.org/10.3390/met11101537

AMA Style

Sharma VB, Tewari S, Biswas S, Lohani B, Dwivedi UD, Dwivedi D, Sharma A, Jung JP. Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring. Metals. 2021; 11(10):1537. https://doi.org/10.3390/met11101537

Chicago/Turabian Style

Sharma, Vinamra Bhushan, Saurabh Tewari, Susham Biswas, Bharat Lohani, Umakant Dhar Dwivedi, Deepak Dwivedi, Ashutosh Sharma, and Jae Pil Jung. 2021. "Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring" Metals 11, no. 10: 1537. https://doi.org/10.3390/met11101537

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