Call For Paper - Upcoming Conferences

Research Article | Open Access | Download PDF
Volume 13 | Issue 7 | Year 2026 | Article Id. IJCE-V13I7P101 | DOI : https://doi.org/10.14445/23488352/IJCE-V13I7P101

Tomographic Terahertz (THz) Wave Based Image Reconstruction for Subsurface Crack Identification in Civil Infrastructure Buildings


Surajit Mohanty, Subhendu Kumar Pani, Suvendu Tripathy, Achyutananda Rout, Rajeev Agarwal, Sunita Dalei

Received Revised Accepted Published
19 Mar 2026 01 Jun 2026 19 Jun 2026 29 Jul 2026

Citation :

Surajit Mohanty, Subhendu Kumar Pani, Suvendu Tripathy, Achyutananda Rout, Rajeev Agarwal, Sunita Dalei, "Tomographic Terahertz (THz) Wave Based Image Reconstruction for Subsurface Crack Identification in Civil Infrastructure Buildings," International Journal of Civil Engineering, vol. 13, no. 7, pp. 1-14, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I7P101

Abstract

Detection of cracks in civil infrastructure buildings at an early age is very much important to give these structures structural safety, serviceability and long life. Traditional Non-Destructive Testing (NDT) methods, which include visual inspection, ultrasonic testing and infrared thermography, are usually limited to detect concealed or near-surface defects especially on heterogeneous building materials. This paper introduces a Terahertz (THz) wave-based imaging technology to be able to detect and identify subsurface cracks in civil infrastructure buildings non-invasively. Thanks to the capacity of poring non-metallic materials, including concrete, masonry, and composites, THz imaging allows detecting internal discontinuities of high resolution without being in physical contact or preparing the surface. The suggested framework combines THz time-domain imaging and improved signal processing and image reconstruction algorithms to improve the visibility of cracks and depth discrimination. The test studies done on model building materials show that the THz system has the potential to identify the existence of both micro- and macro-level under-surface cracks which cannot be detected by other conventional methods. The findings suggest that THz wave based imaging has a high potential as an intelligent, non-destructive assessment instrument of early damage and structural health monitoring of civil infrastructure buildings.

Keywords

Terahertz imaging, Civil infrastructure buildings, Structural Health Monitoring, Subsurface crack detection, Terahertz time-domain spectroscopy, Non-Destructive Testing.

References

  1. Dong-Hoon Kwak et al., “Sub-Terahertz Imaging-Based Real-Time Non-Destructive Inspection System for Estimating Water Activity and Foreign Matter Depth in Seaweed,” Sensors, vol. 24, no. 23, pp. 1-19, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  2. Yiming Deng, and Xin Liu, “Electromagnetic Imaging Methods for Nondestructive Evaluation Applications,” Sensors, vol. 11, no. 12, pp. 11774-11808, 2011.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  3. Xiuwei Yang et al., “Defect Detection of Composite Material Terahertz Image Based on Faster Region-Convolutional Neural Networks,” Materials, vol. 16, no. 1, pp. 1-14, 2023.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  4. Ramesh Kumpati, Wojciech Skarka, and Sunith Kumar Ontipuli, “Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing,” Sensors, vol. 21, no. 18, pp. 1-32, 2021.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  5. Kirsti Krügener et al., “Terahertz Inspection of Buildings and Architectural Art,” Applied Sciences, vol. 10, no. 15, pp. 1-17, 2020.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  6. Marco Civera, and Cecilia Surace, “Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years,” Sensors, vol. 22, no. 4, pp. 1-52, 2022.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  7. Zhang Yue et al., “Single-Shot Direct Transmission Terahertz Imaging Based on Intense Broadband Terahertz Radiation,” Sensors, vol. 24, no. 13, pp. 1-12, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  8. Rocco Alaggio et al., “Two-years Static and Dynamic Monitoring of the Santa Maria di Collemaggio Basilica,” Construction and Building Materials, vol. 268, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  9. Angelo Aloisio et al., “The Recorded Seismic Response of the Santa Maria Di Collemaggio Basilica to Low-intensity Earthquakes,” International Journal of Architectural Heritage, vol. 15, no. 1, pp. 229-247, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  10. Guest-Editors: Francesco Clementi et al., “Structural Health Monitoring of Architectural Heritage: From the Past to the Future Advances,” International Journal of Architectural Heritage, 15, no. 1, pp. 1-4, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  11. Di Benedetto et al., “Concrete Half-Joints: Corrosion Damage Analysis with Numerical Simulation,” The International Federation for Structural Concrete: 2nd FIB Italy YMG Symposium on Concrete and Concrete Structures, Italy, pp. 297-304, 2021.
    [Google Scholar] [Publisher Link]
  12. Marco Martino Rosso et al., “Corrosion Effects on the Capacity and Ductility of Concrete Half-Joint Bridges,” Construction and Building Materials, vol. 360, pp. 1-44, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  13. Marco M. Rosso et al., Review on Deep Learning in Structural Health Monitoring, Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability, 1st ed., CRC Press, pp. 1-7, 2022.
    [Google Scholar] [Publisher Link]
  14. F. Parisi et al., “Automated Location of Steel Truss Bridge Damage using Machine Learning and Raw Strain Sensor Data,” Automation in Construction, vol. 138, 2022.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  15. Sandeep Sony et al., “Vibration-Based Multiclass Damage Detection and Localization using Long Short-term Memory Networks,” Structures, vol. 35, pp. 436-451, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  16. M. Flah et al., “Localization and Classification of Structural Damage using Deep Learning Single-Channel Signal-Based Measurement,” Automation in Construction, vol. 139, 2022.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  17. Yang Yu et al., “Vision-Based Concrete Crack Detection using a Hybrid Framework Considering Noise Effect,” Journal of Building Engineering, vol. 61, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  18. Yang Yu et al., “Crack Detection of Concrete Structures using Deep Convolutional Neural Networks Optimized by Enhanced Chicken Swarm Algorithm,” Structural Health Monitoring, vol. 21, no. 5, pp. 2244-2263, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  19. Congcong Guo et al., “High-throughput Estimation of Plant Height and Above-Ground Biomass of Cotton using Digital Image Analysis and Canopeo,” Technologies in Agronomy, vol. 2, pp. 1-10, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  20. Marco M. Rosso et al., “Train-Track-Bridge Interaction Analytical Model with Non-proportional Damping: Sensitivity Analysis and Experimental Validation,” Proceedings of the European Workshop on Structural Health Monitoring, pp. 223-232, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  21. Marco Martino Rosso et al., “Convolutional Networks and Transformers for Intelligent Road Tunnel Investigations,” Computers and Structures, vol. 275, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. Annamaria Mesaros et al., “Sound Event Detection: A Tutorial,” IEEE Signal Processing Magazine, vol. 38, no. 5, pp. 67-83, 2021.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  23. Bibhu Prasad Ganthia et al., Tribological Behavior of Coconut Shell-Fly Ash-Epoxy Hybrid Composites: An Investigation, Natural Polymers, 1st ed., Apple Academic Press, pp. 1-25, 2022.
    [Google Scholar] [Publisher Link]