Structural Health Monitoring Using Image Processing and Advanced Technologies for the Identification of Deterioration of Building Structure: A Review
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| Publicado en: | Structural Durability & Health Monitoring vol. 19, no. 6 (2025), p. 1547-1563 |
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| Otros Autores: | , |
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Tech Science Press
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3280656822 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1930-2983 | ||
| 022 | |a 1930-2991 | ||
| 024 | 7 | |a 10.32604/sdhm.2025.069239 |2 doi | |
| 035 | |a 3280656822 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Bodke, Kavita |u Department of Computer Engineering, Veermata Jijabai Technological Institute, Mumbai, 400019, India | |
| 245 | 1 | |a Structural Health Monitoring Using Image Processing and Advanced Technologies for the Identification of Deterioration of Building Structure: A Review | |
| 260 | |b Tech Science Press |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Structural Health Monitoring (SHM) systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity. There is a need for more efficient techniques to detect defects, as traditional methods are often prone to human error, and this issue is also addressed through image processing (IP). In addition to IP, automated, accurate, and real- time detection of structural defects, such as cracks, corrosion, and material degradation that conventional inspection techniques may miss, is made possible by Artificial Intelligence (AI) technologies like Machine Learning (ML) and Deep Learning (DL). This review examines the integration of computer vision and AI techniques in Structural Health Monitoring (SHM), investigating their effectiveness in detecting various forms of structural deterioration. Also, it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage, ultimately enhancing safety, durability, and maintenance practices in the field. Key findings reveal that AI-powered approaches, especially those utilizing IP and DL models like CNNs, significantly improve detection efficiency and accuracy, with reported accuracies in various SHM tasks. However, significant research gaps remain, including challenges with the consistency, quality, and environmental resilience of image data, a notable lack of standardized models and datasets for training across diverse structures, and concerns regarding computational costs, model interpretability, and seamless integration with existing systems. Future work should focus on developing more robust models through data augmentation, transfer learning, and hybrid approaches, standardizing protocols, and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable, scalable, and affordable SHM systems. | |
| 653 | |a Structural health monitoring | ||
| 653 | |a Accuracy | ||
| 653 | |a Data augmentation | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Structural integrity | ||
| 653 | |a Damage assessment | ||
| 653 | |a Computer vision | ||
| 653 | |a Image quality | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Structural damage | ||
| 653 | |a Image processing | ||
| 653 | |a Defects | ||
| 653 | |a Datasets | ||
| 653 | |a Collaboration | ||
| 653 | |a Buildings | ||
| 653 | |a Interdisciplinary aspects | ||
| 653 | |a Automation | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Efficiency | ||
| 653 | |a Preventive maintenance | ||
| 653 | |a Corrosion | ||
| 653 | |a Infrastructure | ||
| 653 | |a Computer engineering | ||
| 653 | |a Algorithms | ||
| 653 | |a Human error | ||
| 700 | 1 | |a Bhirud, Sunil |u Department of Computer Engineering, Veermata Jijabai Technological Institute, Mumbai, 400019, India | |
| 700 | 1 | |a Keshav Kashinath Sangle |u Department of Structural Engineering, Veermata Jijabai Technological Institute, Mumbai, 400019, India | |
| 773 | 0 | |t Structural Durability & Health Monitoring |g vol. 19, no. 6 (2025), p. 1547-1563 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3280656822/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3280656822/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |