Authors :
Sonali Dhudse
Volume/Issue :
Volume 11 - 2026, Issue 4 - April
Google Scholar :
https://tinyurl.com/38d8eads
Scribd :
https://tinyurl.com/3xemu4we
DOI :
https://doi.org/10.38124/ijisrt/26apr2484
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Non-destructive testing (NDT) techniques have become indispensable in structural engineering for evaluating
material integrity, detecting defects, and ensuring the safety and durability of infrastructure without causing damage. This
review paper presents a comprehensive overview of advanced NDT methods and their applications in modern structural
assessment. Conventional techniques such as ultrasonic pulse velocity, rebound hammer testing, and radiography are
discussed alongside emerging technologies including ground-penetrating radar, infrared thermography, acoustic emission
monitoring, and laser-based methods. The integration of digital tools, such as artificial intelligence and machine learning,
with NDT systems is also examined, highlighting their role in enhancing defect detection accuracy, data interpretation, and
predictive maintenance strategies. Furthermore, the concept of real-time structural health monitoring through sensor
networks and IoT-enabled systems is explored, emphasizing its significance in smart infrastructure development. The review
critically analyzes the advantages, limitations, and practical challenges associated with each technique, including issues
related to cost, accessibility, data reliability, and environmental influence. Comparative insights are provided to guide the
selection of appropriate NDT methods for different structural materials and conditions. The study also identifies current
research gaps and future directions, particularly in the areas of automation, hybrid testing approaches, and digital twin
integration. Overall, this paper aims to serve as a valuable reference for researchers and practitioners seeking to adopt
advanced, efficient, and reliable NDT techniques for sustainable structural engineering practices.
Keywords :
Non-Destructive Testing; Structural Health Monitoring; Ground-Penetrating Radar; Infrared Thermography; Artificial Intelligence.
References :
- Azanaw A. (2024). Application of digital twin in structural health monitoring of civil structures: A systematic review. Structures, 58, 105–120.
- Hallaji M., Seppänen A., & Pour-Ghaz M. (2014). Electrical impedance tomography-based sensing for concrete structures: A review. Cement and Concrete Research, 58, 52–62.
- Gholizadeh S. (2016). A review of non-destructive testing methods of composite materials. Procedia Structural Integrity, 1, 50–57.
- Meola C., & Carlomagno G. M. (2004). Recent advances in the use of infrared thermography. Measurement Science and Technology, 15(9), R27–R58.
- Bungey J. H., Millard S. G., & Grantham M. G. (2006). Testing of Concrete in Structures. Taylor & Francis.
- Malhotra V. M., & Carino N. J. (2004). Handbook on Non-Destructive Testing of Concrete. CRC Press.
- Balageas D., Fritzen C. P., & Güemes A. (2006). Structural Health Monitoring. ISTE Ltd.
- Diamanti N., & Redman J. D. (2012). Field observations and numerical models of GPR wave propagation in concrete. NDT & E International, 45(1), 52–59.
- Aggelis D. G. (2011). Classification of cracking mode in concrete by acoustic emission parameters. Mechanics Research Communications, 38(3), 153–157.
- Su Z., & Ye L. (2009). Identification of damage using Lamb waves. Smart Materials and Structures, 18(10).
- Sohn H., Farrar C. R., Hemez F. M., et al. (2004). A review of structural health monitoring literature: 1996–2001. Los Alamos National Laboratory Report.
- Worden K., & Dulieu-Barton J. M. (2004). An overview of intelligent fault detection in systems and structures. Structural Health Monitoring, 3(1), 85–98.
- Grosse C. U., & Ohtsu M. (2008). Acoustic Emission Testing. Springer.
- Kang L., Wang L., & Chen H. (2020). Machine learning for structural health monitoring: A review. Sensors, 20(6), 1681.
- Farrar C. R., & Worden K. (2012). Structural Health Monitoring: A Machine Learning Perspective. Wiley.
- Tajne, G. B. et al., A Review on Manufacturing Process and Techniques of Hume Concrete, International Journal Publication and Review (IJRPR), vol. 3, no. 11, 2022. https://doi.org/10.55248/gengpi.2022.3.11.51
- Zade, S. B. et al., Refurbishing of Civil Engineering Laboratories, International Journal of Research Publication and Review (IJRPR), vol. 5, no. 12, 2024. https://doi.org/10.55248/gengpi.5.1224.3411
- Marve, S. R., et al. (2024). Parking management system at SSCET campus. International Journal of Research Publication and Review. https://doi.org/10.55248/gengpi.2022.3.8.33
- Marve, S. R., et al. (2025). Integrated data-driven optimization and microstructural modeling of nano-silica enhanced cement–fly ash–lime wall panels for prefabricated construction. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-025-01440-6
- Marve, S. R., & Shende, S. R. (2018). Public transportation system in Chandrapur city. International Journal of Scientific Research in Science, Engineering and Technology. https://doi.org/10.32628/18410IJSRSET
- Giri, et al. (2023). A review on analysis and design of multistorey hospital building (G+4). International Journal of Research Publication and Review. https://doi.org/10.55248/gengpi.2023.4.34359
- Shende, S. R., et al. (2024). Comparative assessment of different types of slabs by using software. International Journal of Research Publication and Review. https://doi.org/10.55248/gengpi.5.0524.1450
- Barde, et al. (2022). A review of parking management system at SSCET campus. International Journal of Research Publication and Review. https://doi.org/10.55248/gengpi.2022.3.7.15
- Bhashakhetre, et al. (2017). Plastic waste prevention system analysis & application. International Journal of Innovative Research in Science, Engineering and Technology.
- Bhoyar, A. R., et al. (2024). Green initiatives in SSCET campus. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.5.1224.3513
- Mandade, P. S., et al. (2024). Assessing and reducing the carbon footprint of SSCET. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.5.1224.3415
- Harne, R. S., et al. (2024). Implementation of rainwater harvesting (RWH) on a college campus. International Journal of Research Publication and Reviews, 5(12). https://doi.org/10.55248/gengpi.5.1224.3424
- Nimsarkar, B. D., et al. (2024). Evaluating the impact of Bio-CNG and electric buses on Chandrapur city’s public transport system. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.5.1224.3556
- Marve, S. R., et al. (2025). Performance attributes of pervious concrete for pavement design.
- Marve, S. R., et al. (2023). Optimizing public transportation: A software design approach for enhanced quality.
- Marve, S. R., et al. (2023). Enhancing public transportation network systems in Nagpur using machine learning.
- Shende, S. R. et. Al. (2018). A Review on Design of Public Transportation System in Chandrapur City. Journal for Research, 4(1), 41-47.
Non-destructive testing (NDT) techniques have become indispensable in structural engineering for evaluating
material integrity, detecting defects, and ensuring the safety and durability of infrastructure without causing damage. This
review paper presents a comprehensive overview of advanced NDT methods and their applications in modern structural
assessment. Conventional techniques such as ultrasonic pulse velocity, rebound hammer testing, and radiography are
discussed alongside emerging technologies including ground-penetrating radar, infrared thermography, acoustic emission
monitoring, and laser-based methods. The integration of digital tools, such as artificial intelligence and machine learning,
with NDT systems is also examined, highlighting their role in enhancing defect detection accuracy, data interpretation, and
predictive maintenance strategies. Furthermore, the concept of real-time structural health monitoring through sensor
networks and IoT-enabled systems is explored, emphasizing its significance in smart infrastructure development. The review
critically analyzes the advantages, limitations, and practical challenges associated with each technique, including issues
related to cost, accessibility, data reliability, and environmental influence. Comparative insights are provided to guide the
selection of appropriate NDT methods for different structural materials and conditions. The study also identifies current
research gaps and future directions, particularly in the areas of automation, hybrid testing approaches, and digital twin
integration. Overall, this paper aims to serve as a valuable reference for researchers and practitioners seeking to adopt
advanced, efficient, and reliable NDT techniques for sustainable structural engineering practices.
Keywords :
Non-Destructive Testing; Structural Health Monitoring; Ground-Penetrating Radar; Infrared Thermography; Artificial Intelligence.