Skip to main navigation menu Skip to main content Skip to site footer

Leveraging Steel Structures to Measure Real-Time Environmental Hazardous Seismic Activity: A Comprehensive Review

Abstract

Steel structures are a potential network of distributed sensors to do real-time seismic surveillance and detect environmental hazards. This review discusses the state-of-the-art in existing ways to leverage steel buildings, bridges, and infrastructure as integrated seismic monitoring systems. The present study examines sensor technologies, real-time extensions, communication frameworks, and analytical structures and mechanisms that allow steel structures to serve as both structural resources and environmental apparatus. The combination of accelerometers, strain sensors, fiber-optic systems, and wireless networks turns steel structures into active seismic observatories that will record, characterize, and report real-time dangerous seismic activities. This method has major benefits compared to conventional seismic networks, such as an increased spatial resolution, structure-specific damage, and real-time post-event safety assessment. Some of the major problems are longevity of sensors, bandwidth, variability of the environment, and the necessity to have standardized benchmarking protocols. This review suggests a roadmap through which researchers, engineers, and policymakers can contribute to the development of the field at practical stages of a large-scale implementation. Future research includes multi-sensor fusion, edge computing, the incorporation of digital twins, and field validation research.

Keywords

Structural Health Monitoring, Steel Structures, Real-Time Seismic Monitoring, System Identification, Wireless Sensor Networks, Earthquake Early Warning (EEW)

PDF

References

  1. Fanijo E, Liu J, Mohammed T. Transportation Infrastructure Health Monitoring 2025.
  2. Hassani S, Mousavi M, Gandomi AH. Structural health monitoring in composite structures: A comprehensive review. Sensors 2022;22:1–45. https://doi.org/10.3390/s22010153.
  3. Celebi M. Developments in seismic monitoring for risk reduction. J Risk Res 2007;10:715–27.
  4. Huang Y, Loh C, Chou C, Chen W. Long?term building safety assessment from a series of earthquake excitations. Earthq Eng Struct Dyn 2024;53:1593–611.
  5. Makhoul N, Roohi M, van de Lindt JW, Sousa H, Santos LO, Argyroudis S, et al. Seismic resilience of interdependent built environment for integrating structural health monitoring and emerging technologies in decision-making. Struct Eng Int 2024;34:19–33.
  6. Abdulkarem M, Samsudin K, Rokhani FZ, A Rasid MF. Wireless sensor network for structural health monitoring: A contemporary review of technologies, challenges, and future direction. Struct Heal Monit 2020;19:693–735.
  7. Iyama J, Ou CC, Yamada S, Chiba K, Toyoshima M. Shaking table test of steel truss frame focusing on acceleration and strain response for post-earthquake buckling evaluation. Bull Earthq Eng 2023;21:2759–83.
  8. Yu X, Li X. Dynamic strain estimations of beam ends in steel moment-resisting frames using acceleration data. Int. Conf. Civ. Eng., Springer; 2022, p. 73–86.
  9. Edwards H, Neal K, Reilly J, Van Buren K, Hemez F. Making Structural Condition Diagnostics Robust to Environmental Variability. Dyn. Civ. Struct. Vol. 2 Proc. 34th IMAC, A Conf. Expo. Struct. Dyn. 2016, Springer; 2016, p. 117–30.
  10. Hann CE, Singh-Levett I, Deam BL, Mander JB, Chase JG. Real-time system identification of a nonlinear four-story steel frame structure—Application to structural health monitoring. IEEE Sens J 2009;9:1339–46.
  11. Huang S-K, Chi F-C. Development of Recursive Subspace Identification for Real?Time Structural Health Monitoring under Seismic Loading. Struct Control Heal Monit 2023;2023:1117042.
  12. Loayssa A. Structural health monitoring using distributed fibre?optic sensors. Opt Fibre Sensors Fundam Dev Optim Devices 2020:125–49.
  13. Lee J, Jung H, Kim MJ, Kim YH. Real-Time Structural Health Monitoring of Reinforced Concrete Under Seismic Loading Using Dynamic OFDR. Sensors 2025;25:5818.
  14. Zhang J, Huang Y, Zheng Y. A feasibility study on timber damage detection using piezoceramic-transducer-enabled active sensing. Sensors 2018;18:1563.
  15. Hassani S, Mousavi M, Gandomi AH. Structural health monitoring in composite structures: A comprehensive review. Sensors 2021;22:153.
  16. PAROL J, AL QAZWEENI J, KAMAL H, KALKAN E. Structural Health Monitoring and Real-Time System Identification Analysis of the World’s Tallest Sculptured Skyscraper. Struct Heal Monit 2023 2023.
  17. Malekzadeh M, Atia G, Catbas FN. Performance-based structural health monitoring through an innovative hybrid data interpretation framework. J Civ Struct Heal Monit 2015;5:287–305.
  18. Makarios T, Bakalis A, Efthymiou E. Seismic Damage Assessment of Existing Planar Steel X-or V-Braced Frames Using the Hybrid “M and P” Technique. Appl Sci 2024;14:8638.
  19. Deng H, Chen J. A Survey of Structural Health Monitoring Advances Based on Internet of Things (IoT) Sensors. Int J Adv Comput Sci Appl 2023;14:225–34. https://doi.org/10.14569/IJACSA.2023.0141025.
  20. Gattulli V, Franchi F, Graziosi F, Marotta A, Rinaldi C, Potenza F, et al. Design and evaluation of 5G-based architecture supporting data-driven digital twins updating and matching in seismic monitoring. Bull Earthq Eng 2022;20:4345–65.
  21. Özcebe AG, Tiganescu A, Ozer E, Negulescu C, Galiana-Merino JJ, Tubaldi E, et al. Raspberry shake-based rapid structural identification of existing buildings subject to earthquake ground motion: The case study of Bucharest. Sensors 2022;22:4787.
  22. Barella BP, Rezende SWF de, Tsuruta KM, Finzi Neto RM, De Moura Junior J dos RV. Calibration of a low-cost electromechanical impedance-based structural health monitoring device. Obs La Econ Latinoam 2023;21:3941–52. https://doi.org/10.55905/oelv21n6-046.
  23. Sajitha I, Sambandam RK, John SP. Advancing Building Damage Classification Accuracy through Machine Learning-based Model Design 2024.
  24. Lynch JP, Loh KJ. A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib Dig 2006;38:91–130.
  25. Bao Y, Li H. Machine learning paradigm for structural health monitoring. Struct Heal Monit 2021;20:1353–72.
  26. D’Alessandro A, Luzio D, D’Anna G. Urban MEMS based seismic network for post-earthquakes rapid disaster assessment. Adv Geosci 2014;40:1–9.
  27. Sadat Shokouhi SK, Vosoughifar HR. Optimal sensor placement in the lightweight steel framing structures using the novel TTFD approach subjected to near-fault earthquakes. J Civ Struct Heal Monit 2013;3:257–67.
  28. Petrone F, Perez R, Coates J, McCallen D. A Biaxial Discrete Diode Position Sensor for Rapid Postevent Structural Damage Assessment. J Struct Eng 2023;149:4022251.
  29. Lynch JP. Wireless Sensing Technologies for Pre-Earthquake Event Mitigation And Post-Earthquake Event Response. Proc, 9th US. Natl. and10th Can. Conf. Earthq. Eng. Toronto, Ontario, Canada, 2010.
  30. Lynch JP, Wang Y, Lu KC, Hou T-C, Loh CH. Post-seismic damage assessment of steel structures instrumented with self-interrogating wireless sensors. Proc. 8th Natl. Conf. Earthq. Eng., vol. 18, 2006.
  31. Lu K-C, Loh C-H, Yang Y-S, Lynch JP, Law KH. Real-time structural damage detection using wireless sensing and monitoring system. Smart Struct Syst 2008;4:759–77.
  32. Fu Y, Mechitov K, Hoang T, Kim JR, Lee DH, Spencer Jr BF. Development and full-scale validation of high-fidelity data acquisition on a next-generation wireless smart sensor platform. Adv Struct Eng 2019;22:3512–33.
  33. Fuhr PL, Huston DR. Corrosion detection in reinforced concrete roadways and bridges via embedded fiber optic sensors. Smart Mater Struct 1998;7:217.
  34. Luo L, Xu T, Wang J, Wang C-C, Xu D, Lee A, et al. Real-time well integrity monitoring in underground gas storage wells using distributed temperature and strain sensing: a field demonstration. Struct Heal Monit 2025:14759217251362392.
  35. Willberry JO, Papaelias M, Franklyn Fernando G. Structural health monitoring using fibre optic acoustic emission sensors. Sensors 2020;20:6369.
  36. Rangwala H, Vora T, Baz A. Integrating Machine Learning with Electromechanical Impedance for Non-Destructive Detection of Bolt Looseness in Steel Structures. Case Stud Constr Mater 2025:e05430.
  37. Thoriya A, Vora T, Nyanzi P. Pipeline corrosion assessment using electromechanical impedance technique. Mater Today Proc 2022;56:2334–41.
  38. Rangwala H, Vora T. Application of Smart Computing in Steel Structural Health Monitoring: Sensor Based Damage Detection for Smart Infrastructures. Int. Conf. Adv. Smart Comput. Inf. Secur., Springer; 2024, p. 195–211.
  39. Thoriya A, Vora T, Makwana V. Corrosion assessment in rebars of high-strength concrete using electromechanical impedance technique. Mater Today Proc 2022;57:2234–41.
  40. Kohler M, Heaton TH, Govindan R, Davis P, Estrin D. Using embedded wired and wireless seismic networks in the moment-resisting steel frame Factor building for damage identification 2006.
  41. Chang H-F, Shokrolah Shirazi M. Integration with 3D visualization and IoT-based sensors for real-time structural health monitoring. Sensors 2021;21:6988.
  42. Hou S, Wu G. A low-cost IoT-based wireless sensor system for bridge displacement monitoring. Smart Mater Struct 2019;28:85047.
  43. Gomez-Cabrera A, Escamilla-Ambrosio PJ. Review of machine-learning techniques applied to structural health monitoring systems for building and bridge structures. Appl Sci 2022;12:10754.
  44. Thoriya A, Vora T, Jadeja R, Ali YAA, Patel SK. Application of wavelet transform techniques for corrosion assessment of embedded rebars in RC elements using electromechanical impedance. Measurement 2024;226:114081.
  45. Flah M, Nunez I, Ben Chaabene W, Nehdi ML. Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review. Arch Comput Methods Eng 2021;28.
  46. Malekloo A, Ozer E, AlHamaydeh M, Girolami M. Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Struct Heal Monit 2022;21:1906–55.
  47. Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mech Syst Signal Process 2021;147:107077.
  48. Reuland Y, Martakis P, Chatzi E. A comparative study of damage-sensitive features for rapid data-driven seismic structural health monitoring. Appl Sci 2023;13:2708.
  49. Kurata M, Nakashima M, Suita K. Effect of column base behaviour on the seismic response of steel moment frames. J Earthq Eng 2005;9:415–38.
  50. Li X, Kurata M, Nakashima M. Evaluating damage extent of fractured beams in steel moment?resisting frames using dynamic strain responses. Earthq Eng Struct Dyn 2015;44:563–81.
  51. Sitapra NJ, Shukla KP, Vora TP, Thoriya AJ. Use of IDA to Estimate Variations in Drift Demands of Irregular RCC Buildings. Recent Adv. Mater. Mech. Struct. Sel. Proc. ICMMS 2022, Springer; 2022, p. 85–95.
  52. Okur FY, Altuni?ik AC, Kalkan Okur E. A Novel Approach for Anomaly Detection in Vibration?Based Structural Health Monitoring Using Autoencoders in Deep Learning. Struct Control Heal Monit 2025;2025:5602604.
  53. Del Priore E, Lampani L. Real-Time Damage Detection and Localization on Aerospace Structures Using Graph Neural Networks. J Sens Actuator Networks 2025;14:89. https://doi.org/10.3390/jsan14050089.
  54. Pirrò M, Pereira S, Gentile C, Magalhães F, Cunha Á. Damage detection under environmental and operational variability using the cointegration technique. Eng Struct 2025;328:119700.
  55. Xie Y, Feng D, Chen H, Liu Z, Mao W, Zhu J, et al. Damaged building detection from post-earthquake remote sensing imagery considering heterogeneity characteristics. IEEE Trans Geosci Remote Sens 2022;60:1–17.
  56. Meier M. How “good” are real?time ground motion predictions from earthquake early warning systems? J Geophys Res Solid Earth 2017;122:5561–77.
  57. Allen RM, Kong Q, Martin-Short R. The MyShake platform: a global vision for earthquake early warning. Pure Appl Geophys 2020;177:1699–712.
  58. Cheng Z, Peng C, Chen M. Real-time seismic intensity measurements prediction for earthquake early warning: A systematic literature review. Sensors 2023;23:5052.
  59. Thoriya A, Rangwala H, Kamati D, Vora T. Seismic sensitivity analysis of plan-irregular RC buildings using statistical and machine learning approaches. Asian J Civ Eng 2025. https://doi.org/10.1007/s42107-025-01471-z.
  60. Elsisi A, Zamrawi A, Emad S. A Comprehensive Review of Structural Health Monitoring for Steel Bridges: Technologies, Data Analytics, and Future Directions 2025.
  61. Vasconcelos ARC, de Matos RA, Silveira MV, Mesquita E. Applications of Smart and Self-Sensing Materials for Structural Health Monitoring in Civil Engineering: A Systematic Review. Buildings 2024;14:2345.
  62. Vinuja G, Devi NB. Leveraging 6G Networks for Disaster Monitoring and Management in Remote Sensing. Dev 6G Networks Technol 2024:115–43.
  63. Al-Adly AIF, Kripakaran P. Physics-informed neural networks for structural health monitoring: a case study for Kirchhoff–Love plates. Data-Centric Eng 2024;5:e6.
  64. Balaji B, Bhattacharya A, Fierro G, Gao J, Gluck J, Hong D, et al. Brick: Metadata schema for portable smart building applications. Appl Energy 2018;226:1273–92.
  65. Otieno E, Chipangio S, Modi R, Rangwala H. Optimization of an integrated hybrid commercial building with building information modelling. Innov Infrastruct Solut 2025;10:159. https://doi.org/10.1007/s41062-025-01986-z.
  66. Yuan F-G, Zargar SA, Chen Q, Wang S. Machine learning for structural health monitoring: challenges and opportunities. Sensors Smart Struct Technol Civil, Mech Aerosp Syst 2020 2020;11379:1137903.
  67. Barman SK, Mishra M, Maiti DK, Maity D. Vibration-based damage detection of structures employing Bayesian data fusion coupled with TLBO optimization algorithm. Struct Multidiscip Optim 2021;64:2243–66.
  68. Savage N. Quantum diamond sensors. Nature 2021;591:S37.
  69. Zhao X, Yuan S, Zhou H, Sun H, Qiu L. An evaluation on the multi-agent system based structural health monitoring for large scale structures. Expert Syst Appl 2009;36:4900–14.
  70. Shu J, Ding W, Zhang J, Lin F, Duan Y. Continual?learning?based framework for structural damage recognition. Struct Control Heal Monit 2022;29:e3093.
  71. Habib A, Habib M, Bashir B, Bachir H. Exploring the Sustainability Benefits of Digital Twin Technology in Achieving Resilient Smart Cities During Strong Earthquake Events. Arab J Sci Eng 2025:1–15.
  72. Shadabfar M, Mahsuli M, Zhang Y, Xue Y, Ayyub BM, Huang H, et al. Resilience-based design of infrastructure: Review of models, methodologies, and computational tools. ASCE-ASME J Risk Uncertain Eng Syst Part A Civ Eng 2022;8:3121004.
  73. M. A. . Omer, A. A. . Yazdeen, H. S. . Malallah, and L. M. . Abdulrahman, “A Survey on Cloud Security: Concepts, Types, Limitations, and Challenges”, JASTT, vol. 3, no. 02, pp. 101–111, Dec. 2022

Downloads

Download data is not yet available.

Similar Articles

21-30 of 79

You may also start an advanced similarity search for this article.