Skip to main content
Book cover

XV Multidisciplinary International Congress on Science and Technology

CIT 2021: Emerging Research in Intelligent Systems pp 56–70Cite as

MQTT Based Event Detection System for Structural Health Monitoring of Buildings

  • 86 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 405)

Abstract

Structural Health Monitoring (SHM) consists in a fundamental research field which aim to evaluate the current status of an infrastructure with the main purpose to identify damages and prevent catastrophic events. This paper presents an SHM solution that implements an automatic system based on the MQTT protocol and IoT devices for detecting seismic events. In particular, the architecture consists of a set of accelerometer sensors which communicate by means of a decentralized network topology (i.e., an Ad hoc Network configuration). Moreover, the system has the capacity to transmit the information about the events detected in real-time using cloud services. In order to verify the proper operation, the system was deployed on an actual building and the information acquired by the sensors was registered along four months. In this context, a relevant event detected was selected for analyzing the dynamic response of the building during a seism. Results show that the acceleration values increase as a function of the building height. Regarding the seismic event analyzed, the RMS values of acceleration identified on the basement were 0.26, 0.22, and 0.22 cm/s2 and in the case of the eighth floor were 1.18, 1.33, and 0.59 cm/s2 for the longitudinal, transverse, and vertical axes, respectively. Additionally, a first assessment regarding the structural health status of the building was performed through the OMA methodology (Operational Modal Analysis). Specifically, the FDD (Frequency Domain Decomposition) mechanism was used to determine the first four frequencies and its respective vibration modes.

Keywords

  • MQTT
  • IoT
  • Event detection
  • WSN
  • Structural Health
  • Operational Modal Analysis

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-96043-8_5
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   189.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-96043-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   249.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.

References

  1. Yun, D.Y., et al.: Field measurements for identification of modal parameters for high-rise buildings under construction or in use. Autom. Constr. 121, 103446 (2021). https://doi.org/10.1016/j.autcon.2020.103446

  2. Pachón, P., Castro, R., García-Macías, E., Compan, V., Puertas, E.: E. Torroja’s bridge: tailored experimental setup for SHM of a historical bridge with a reduced number of sensors. Eng. Struct. 162, 11–21 (2018). https://doi.org/10.1016/j.engstruct.2018.02.035

    CrossRef  Google Scholar 

  3. Pachón, P., et al.: Evaluation of optimal sensor placement algorithms for the Structural Health Monitoring of architectural heritage. Application to the Monastery of San Jerónimo de Buenavista (Seville, Spain). Eng. Struct. 202, 109843 (2020). https://doi.org/10.1016/j.engstruct.2019.109843

  4. Boscato, G., Dal Cin, A., Ientile, S., Russo, S.: Optimized procedures and strategies for the dynamic monitoring of historical structures. J. Civ. Struct. Health Monit. 6(2), 265–289 (2016). https://doi.org/10.1007/s13349-016-0164-9

    CrossRef  Google Scholar 

  5. Zhou, Y., Zhou, Y., Yi, W., Chen, T., Tan, D., Mi, S.: Operational modal analysis and rational finite-element model selection for ten high-rise buildings based on on-site ambient vibration measurements. J. Perform. Constr. Facil. 31(5), 04017043 (2017). https://doi.org/10.1061/(asce)cf.1943-5509.0001019

    CrossRef  Google Scholar 

  6. Lamonaca, F., Scuro, C., Sciammarella, P.F., Olivito, R.S., Grimaldi, D., Carnì, D.L.: A layered IoT-based architecture for a distributed structural health monitoring system. Acta IMEKO 8(2), 45–52 (2019). https://doi.org/10.21014/acta_imeko.v8i2.640

    CrossRef  Google Scholar 

  7. Wang, J., Fu, Y., Yang, X.: An integrated system for building structural health monitoring and early warning based on an Internet of things approach. Int. J. Distrib. Sens. Netw. 13(1) (2017). https://doi.org/10.1177/1550147716689101

  8. Abdelgawad, A., Yelamarthi, K.: Structural health monitoring: internet of things application. In: Midwest Symposium on Circuits System, vol. 0, pp. 16–19 (2016). https://doi.org/10.1109/MWSCAS.2016.7870118

  9. Alphonsa, A., Ravi, G.: Earthquake early warning system by IOT using Wireless sensor networks. In: Proceedings of the 2016 IEEE International Conference Wireless Communication Signal Processing Networking, WiSPNET 2016, pp. 1201–1205 (2016). https://doi.org/10.1109/WiSPNET.2016.7566327

  10. Jagannath, J., Furman, S., Jagannath, A., Ling, L., Burger, A., Drozd, A.: HELPER: Heterogeneous Efficient Low Power Radio for enabling ad hoc emergency public safety networks. Ad Hoc Netw. 89, 218–235 (2019). https://doi.org/10.1016/j.adhoc.2019.03.010

    CrossRef  Google Scholar 

  11. Sikora, A., Niewiadomska-Szynkiewicz, E., Krzysztoń, M.: Simulation of mobile wireless ad hoc networks for emergency situation awareness. In: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, vol. 5, pp. 1087–1095 (2015). https://doi.org/10.15439/2015F52

  12. Martinez, Z.O.N., Arias, O.M., Lopez, P.A., Ugarte, S.A.: Hybrid wireless ad hoc network design based on WIFI technology for facing seismic catastrophes. In: Canadian Conference on Electrical and Computer Engineering, vol. 2016 (2016). https://doi.org/10.1109/CCECE.2016.7726638

  13. MQTT.org: MQTT: The Standard for IoT Messaging (2021). https://mqtt.org/. Accessed 15 Mar 2021

  14. Yang, J., Wu, P., Chen, Y., Wei, W.: Development and testing of the earthquake early warning information push platform based on MQTT protocol. In: Science Conference Mechatronics Engineering and Computing Science, SCMC 2019, no. Scmc, pp. 399–405 (2019). https://doi.org/10.25236/scmc.2019.082

  15. Greco, L., Ritrovato, P., Tiropanis, T., Xhafa, F.: IoT and semantic web technologies for event detection in natural disasters. Concurr. Comput. 30(21), 1–9 (2018). https://doi.org/10.1002/cpe.4789

    CrossRef  Google Scholar 

  16. Zambrano, A.M., Perez, I., Palau, C., Esteve, M.: Technologies of internet of things applied to an earthquake early warning system. Futur. Gener. Comput. Syst. 75, 206–215 (2017). https://doi.org/10.1016/j.future.2016.10.009

    CrossRef  Google Scholar 

  17. Pierleoni, P., et al.: IoT solution based on MQTT protocol for real-time building monitoring. In: 2019 IEEE 23rd International Symposium on Consumer Technologies, ISCT 2019, pp. 57–62 (2019). https://doi.org/10.1109/ISCE.2019.8901024

  18. Trnkoczy, A.: Understanding and parameter setting of STA/LTA trigger algorithm. New Man. Seismol. Obs. Pract. 2, 1–20 (2012). https://doi.org/10.2312/GFZ.NMSOP-2

    CrossRef  Google Scholar 

  19. Li, X., Shang, X., Wang, Z., Dong, L., Weng, L.: Identifying P-phase arrivals with noise: an improved Kurtosis method based on DWT and STA/LTA. J. Appl. Geophys. 133, 50–61 (2016). https://doi.org/10.1016/j.jappgeo.2016.07.022

    CrossRef  Google Scholar 

  20. Zhang, J., Tang, Y., Li, H.: STA/LTA fractal dimension algorithm of detecting the P-wave arrival. Bull. Seismol. Soc. Am. 108(1), 230–237 (2018). https://doi.org/10.1785/0120170099

    CrossRef  Google Scholar 

  21. Sya’bani, Y.A., Novianty, A., Prasasti, A.L.: Implementation of automatic first arrival picking on P-wave seismic signal using logistic regression method. In: 2020 8th International Conference on Information and Communication Technology, ICoICT 2020, pp. 1–5 (2020). https://doi.org/10.1109/ICoICT49345.2020.9166345

  22. Indi, M.W.P., Novianty, A., Prasasti, A.L.: Automatic first arrival picking on P-wave seismic signal using support vector machine method. In: 2020 8th International Conference on Information and Communication Technology, ICoICT 2020, pp. 1–6 (2020). https://doi.org/10.1109/ICoICT49345.2020.9166267

  23. Dokht, R.M.H., Kao, H., Visser, R., Smith, B.: Seismic event and phase detection using time-frequency representation and convolutional neural networks. Seismol. Res. Lett. 90(2A), 481–490 (2019). https://doi.org/10.1785/0220180308

    CrossRef  Google Scholar 

  24. Wu, Y., Lin, Y., Zhou, Z., Bolton, D.C., Liu, J., Johnson, P.: DeepDetect: a cascaded region-based densely connected network for seismic event detection. IEEE Trans. Geosci. Remote Sens. 57(1), 62–75 (2019). https://doi.org/10.1109/TGRS.2018.2852302

    CrossRef  Google Scholar 

  25. Anchal, S., Mukhopadhyay, B., Kar, S.: UREDT: unsupervised learning based real-time footfall event detection technique in seismic signal. IEEE Sensors Lett. 2(1), 1–4 (2017). https://doi.org/10.1109/lsens.2017.2787611

    CrossRef  Google Scholar 

  26. Muñoz, M., Guevara, R., González, S., Jiménez, J.C.: Reliable data acquisition system for a low-cost accelerograph applied to structural health monitoring. J. Appl. Sci. Eng. Technol. Educ. 3(2), 181–194 (2020). https://doi.org/10.35877/454ri.asci159

    CrossRef  Google Scholar 

  27. A. N. Inc.: ALFA AWUS036NH (2021). https://www.alfa.com.tw/products/awus036nh?variant=36481029374024. Accessed 15 Mar 2021

  28. D.-L. Corporation: DWR-M921 D-Link (2021). https://la.dlink.com/la/4g-lte/dwr-m921/. Accessed 15 Mar 2021

  29. U. E. H. Program: Search Earthquake Catalog (2021). https://earthquake.usgs.gov/earthquakes/search/. Accessed 15 Mar 2021

  30. I. G. EPN: Informes de los Últimos Sismos (2021). https://www.igepn.edu.ec/ultimos-sismos. Accessed 15 Mar 2021

  31. R. S. U. Cuenca: Red Sísmica U Cuenca | LinkedIn (2021). https://www.linkedin.com/in/red-sísmica-u-cuenca-a223821a6/detail/recent-activity/. Accessed 15 Mar 2021

  32. U. E. H. Program: M 4.9 - 6 km SSW of Coronel Marcelino Maridueña, Ecuador (2021). https://earthquake.usgs.gov/earthquakes/eventpage/us7000d1zv/executive. Accessed 15 Mar 2021

  33. American Society of Civil Engineers: Minimum Design Loads and Associated Criteria for Buildings and Other Structures, no. 7–98 (2016)

    Google Scholar 

  34. Lacanna, G., Ripepe, M., Marchetti, E., Coli, M., Garzonio, C.A.: Dynamic response of the Baptistery of San Giovanni in Florence, Italy, based on ambient vibration test. J. Cult. Herit. 20, 632–640 (2016). https://doi.org/10.1016/j.culher.2016.02.007

    CrossRef  Google Scholar 

  35. Neu, E., Janser, F., Khatibi, A.A., Braun, C., Orifici, A.C.: Operational Modal Analysis of a wing excited by transonic flow. Aerosp. Sci. Technol. 49, 73–79 (2016). https://doi.org/10.1016/j.ast.2015.11.032

    CrossRef  Google Scholar 

  36. ACI Committee 318: Building Code Requirements for Structural Concrete (ACI 318-14) (2014)

    Google Scholar 

Download references

Acknowledgment

This work is part of the research project “IoT Technologies and Wireless Sensor Networks for Structural Health Monitoring of Essential Facilities of the City of Cuenca”. The authors gratefully acknowledge to the Research Management Department of the University of Cuenca-Ecuador (DIUC), the RSA Department (Red Sísmica del Austro), and The Electricity Company of the city of Cuenca-Ecuador (EERCS) for the support and resources provided during the development of this research work.

Author information

Authors and Affiliations

  1. Department of Electric, Electronic and Telecommunication Engineering, University of Cuenca, Av.12 de Abril, 010203, Cuenca, Ecuador

    Iván Palacios, José Placencia & Santiago González

  2. Red Sísmica del Austro, University of Cuenca, Av.12 de Abril, 010203, Cuenca, Ecuador

    Milton Muñoz & Juan Jiménez

  3. Department of Civil Engineering, University of Cuenca, Av.12 de Abril, 010203, Cuenca, Ecuador

    Víctor Samaniego & Juan Jiménez

Authors
  1. Iván Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Placencia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Milton Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Víctor Samaniego
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Santiago González
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Juan Jiménez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santiago González .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Dr. Miguel Botto-Tobar

  2. Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

    Henry Cruz

  3. University of Valencia, Valencia, Spain

    Angela Díaz Cadena

  4. International University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Benjamin Durakovic

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Palacios, I., Placencia, J., Muñoz, M., Samaniego, V., González, S., Jiménez, J. (2022). MQTT Based Event Detection System for Structural Health Monitoring of Buildings. In: Botto-Tobar, M., Cruz, H., Díaz Cadena, A., Durakovic, B. (eds) Emerging Research in Intelligent Systems. CIT 2021. Lecture Notes in Networks and Systems, vol 405. Springer, Cham. https://doi.org/10.1007/978-3-030-96043-8_5

Download citation