Recent advancements in ai-enabled smart electronics packaging for structural health monitoring

Vinamra Bhushan Sharma, Saurabh Tewari, Susham Biswas, Bharat Lohani, Umakant Dhar Dwivedi, Deepak Dwivedi, Ashutosh Sharma, Jae Pil Jung

Research output: Contribution to journalReview articlepeer-review

17 Scopus citations

Abstract

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.

Original languageEnglish
Article number1537
JournalMetals
Volume11
Issue number10
DOIs
StatePublished - Oct 2021

Keywords

  • Civil infrastructure
  • Damage detection
  • Electronics packaging
  • Lead-free solders
  • Pipeline leakage detection
  • Structural health monitoring

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