Reliable Data Acquisition System for a Low-Cost Accelerograph Applied to Structural Health Monitoring

  • Milton Muñoz Red Sísmica del Austro, University of Cuenca, Av. 12 de Abril, Cuenca,010203, Ecuador (EC)
  • Remigio Guevara Red Sísmica del Austro, University of Cuenca, Av. 12 de Abril, Cuenca,010203, Ecuador (EC)
  • Santiago González Department of Electric, Electronic and Telecommunication Engineering, University of Cuenca, Av. 12 de Abril, Cuenca,010203, Ecuador (EC)
  • Juan Carlos Jiménez Red Sísmica del Austro, University of Cuenca, Av. 12 de Abril, Cuenca,010203, Ecuador (EC)
Keywords: Data acquisition system, Internet of Things, real time processing, single board computer, structural health monitoring

Viewed = 152 time(s)

Abstract

This paper presents and evaluates a continuous recording system designed for a low-cost seismic station. The architecture has three main blocks. An accelerometer sensor based on MEMS technology (Microelectromechanical Systems), an SBC platform (Single Board Computer) with embedded Linux and a microcontroller device. In particular, the microcontroller represents the central component which operates as an intermediate agent to manage the communication between the accelerometer and the SBC block. This strategy allows the system for data acquisition in real time. On the other hand, the SBC platform is used for storing and processing data as well as in order to configure the remote communication with the station. This proposal is intended as a robust solution for structural health monitoring (i.e. in order to characterize the response of an infrastructure before, during and after a seismic event). The paper details the communication scheme between the system components, which has been minutely designed to ensure the samples are collected without information loss. Furthermore, for the experimental evaluation the station was located in the facilities on a relevant infrastructure, specifically a hydroelectric dam. The system operation was compared and verified with respect to a certified accelerograph station. Results prove that the continuous recording system operates successfully and allows for detecting seismic events according to requirements of structural health applications (i.e. detects events with a frequency of vibration less than 100 Hz). Specifically, through the system implemented it was possible to characterize the effect of a seismic event of 4 MD reported by the regional seismology network and with epicenter located about 30 Km of the hydroelectric dam. Particularly, the vibration frequencies detected on the infrastructure are in the range of 13 Hz and 29 Hz. Regarding the station performance, results from experiments reveals an average CPU load of 51%, consequently the processes configured on the SBC platform do not involve an overload. Finally, the average energy consumption of the station is close to 2.4 W, therefore autonomy provided by the backup system is aroud of 10 hours.



Downloads

Download data is not yet available.

References

[1] G. Lacanna, M. Ripepe, M. Coli, R. Genco, and E. Marchetti, “Full structural dynamic response from ambient vibration of Giotto’s bell tower in Firenze (Italy), using modal analysis and seismic interferometry,” NDT E Int., vol. 102, no. November 2018, pp. 9–15, 2019.
[2] A. Bayraktar, T. Türker, and A. C. Altunişik, “Experimental frequencies and damping ratios for historical masonry arch bridges,” Constr. Build. Mater., vol. 75, pp. 234–241, 2015.
[3] J. He, Y. L. Xu, S. Zhan, and Q. Huang, “Structural control and health monitoring of building structures with unknown ground excitations: Experimental investigation,” J. Sound Vib., vol. 390, pp. 23–38, 2017.
[4] S. Hou, C. Zeng, H. Zhang, and J. Ou, “Monitoring interstory drift in buildings under seismic loading using MEMS inclinometers,” Constr. Build. Mater., vol. 185, pp. 453–467, 2018.
[5] C. Doglioni, “A classification of induced seismicity,” Geosci. Front., vol. 9, no. 6, pp. 1903–1909, 2018.
[6] A. Deb, M. Gazi, J. Ghosh, S. Chowdhury, and C. Barman, “Monitoring of soil radon by SSNTD in Eastern India in search of possible earthquake precursor,” J. Environ. Radioact., vol. 184–185, no. January, pp. 63–70, 2018.
[7] A. D. K. Tareen, M. S. A. Nadeem, K. J. Kearfott, K. Abbas, M. A. Khawaja, and M. Rafique, “Descriptive analysis and earthquake prediction using boxplot interpretation of soil radon time series data,” Appl. Radiat. Isot., vol. 154, no. July, 2019.
[8] A. Carpinteri and O. Borla, “Fracto-emissions as seismic precursors,” Eng. Fract. Mech., vol. 177, pp. 239–250, 2017.
[9] A. Carpinteri and O. Borla, “Acoustic, electromagnetic, and neutron emissions as seismic precursors: The lunar periodicity of low-magnitude seismic swarms,” Eng. Fract. Mech., vol. 210, no. April 2018, pp. 29–41, 2019.
[10] K. I. Oyama et al., “Precursor effect of March 11, 2011 off the coast of Tohoku earthquake on high and low latitude ionospheres and its possible disturbing mechanism,” Adv. Sp. Res., vol. 63, no. 8, pp. 2623–2637, 2019.
[11] A. Mahmoudian and M. J. Kalaee, “Study of ULF-VLF wave propagation in the near-Earth environment for earthquake prediction,” Adv. Sp. Res., vol. 63, no. 12, pp. 4015–4024, 2019.
[12] C. Sotomayor-Beltran, “Ionospheric anomalies preceding the low-latitude earthquake that occurred on April 16, 2016 in Ecuador,” J. Atmos. Solar-Terrestrial Phys., vol. 182, no. October 2018, pp. 61–66, 2019.
[13] M. Khalili, S. K. Alavi Panah, and S. S. Abdollahi Eskandar, “Using Robust Satellite Technique (RST) to determine thermal anomalies before a strong earthquake: A case study of the Saravan earthquake (April 16th, 2013, M W  = 7.8, Iran),” J. Asian Earth Sci., vol. 173, no. January, pp. 70–78, 2019.
[14] L. Pierotti, F. Gherardi, G. Facca, L. Piccardi, and G. Moratti, “Detecting CO2 anomalies in a spring on Mt. Amiata volcano (Italy),” Phys. Chem. Earth, vol. 98, pp. 161–172, 2017.
[15] R. A. Grant and T. Halliday, “Predicting the unpredictable; evidence of pre-seismic anticipatory behaviour in the common toad,” J. Zool., vol. 281, no. 4, pp. 263–271, 2010.
[16] G. Langfelder and A. Tocchio, Microelectromechanical systems integrating motion and displacement sensors. Elsevier Ltd, 2018.
[17] S. Das and P. Saha, “A review of some advanced sensors used for health diagnosis of civil engineering structures,” Meas. J. Int. Meas. Confed., vol. 129, no. January, pp. 68–90, 2018.
[18] P. Pachón, R. Castro, E. García-Macías, V. Compan, and E. Puertas, “E. Torroja’s bridge: Tailored experimental setup for SHM of a historical bridge with a reduced number of sensors,” Eng. Struct., vol. 162, no. September 2017, pp. 11–21, 2018.
[19] U. Isikdag, “Internet of Things: Single-Board Computers,” in Enhanced Building Information Models: Using IoT Services and Integration Patterns, Springer International Publishing, 2015, pp. 43–53.
[20] D. Molloy, “Interfacing to the Raspberry Pi Buses,” in Exploring Raspberry Pi Interfacing to the Real World with Embedded Linux, Wiley, 2016, pp. 219–274.
[21] F. Reghenzani, G. Massari, and W. Fornaciari, “The real-time linux kernel: A survey on Preempt_RT,” ACM Comput. Surv., vol. 52, no. 1, 2019.
[22] S. González, J. C. Jiménez, R. Guevara, and I. Palacios, “IoT-based Microseismic Monitoring System for the Evaluation of Structural Health in Smart Cities,” in Congreso Iberoamericamo de Ciudades Inteligentes (ICSC-CITIES), Soria, Valladolid-Spain, 2018, pp. 191–203.
[23] V. L. Zimmer and N. Sitar, “Detection and location of rock falls using seismic and infrasound sensors,” Eng. Geol., vol. 193, pp. 49–60, 2015.
[24] C. Wang, W. Liu, and J. Li, “Artificial earthquake test of buried water distribution network,” Soil Dyn. Earthq. Eng., vol. 79, pp. 171–185, 2015.
[25] H. Miao, W. Liu, C. Wang, and J. Li, “Artificial earthquake test of gas supply networks,” Soil Dyn. Earthq. Eng., vol. 90, no. February, pp. 510–520, 2016.
[26] Z. Herrasti, I. Val, I. Gabilondo, J. Berganzo, A. Arriola, and F. Martínez, “Wireless sensor nodes for generic signal conditioning: Application to Structural Health Monitoring of wind turbines,” Sensors Actuators, A Phys., vol. 247, pp. 604–613, 2016.
[27] G. Kilic and M. S. Unluturk, “Testing of wind turbine towers using wireless sensor network and accelerometer,” Renew. Energy, vol. 75, pp. 318–325, 2015.
[28] K. Dai, K. Gao, and Z. Huang, Environmental and Structural Safety Issues Related to Wind Energy. Elsevier Inc., 2017.
[29] E. Ghafoori, A. Hosseini, R. Al-Mahaidi, X. L. Zhao, and M. Motavalli, “Prestressed CFRP-strengthening and long-term wireless monitoring of an old roadway metallic bridge,” Eng. Struct., vol. 176, no. June, pp. 585–605, 2018.
[30] H. Malik and W. Zatar, “Software Agents to Support Structural Health Monitoring (SHM)-Informed Intelligent Transportation System (ITS) for Bridge Condition Assessment,” Procedia Comput. Sci., vol. 130, pp. 675–682, 2018.
[31] E. Lenticchia, R. Ceravolo, and C. Chiorino, “Damage scenario-driven strategies for the seismic monitoring of XX century spatial structures with application to Pier Luigi Nervi’s Turin Exhibition Centre,” Eng. Struct., vol. 137, pp. 256–267, 2017.
[32] D. Gargaro, C. Rainieri, and G. Fabbrocino, “Structural and seismic monitoring of the ‘cardarelli’ Hospital in Campobasso,” Procedia Eng., vol. 199, pp. 936–941, 2017.
[33] A. M. Zambrano, I. Perez, C. Palau, and M. Esteve, “Technologies of Internet of Things applied to an Earthquake Early Warning System,” Futur. Gener. Comput. Syst., vol. 75, no. 2017, pp. 206–215, 2017.
[34] M. Klapez, C. A. Grazia, S. Zennaro, M. Cozzani, and M. Casoni, “First Experiences with Earthcloud, a Low-Cost, Cloud-Based IoT Seismic Alert System,” Int. Conf. Wirel. Mob. Comput. Netw. Commun., vol. 2018-Octob, pp. 262–269, 2018.
[35] N. Carreras, D. Moure, S. Gomáriz, D. Mihai, A. Mànuel, and R. Ortiz, “Design of a smart and wireless seismometer for volcanology monitoring,” Meas. J. Int. Meas. Confed., vol. 97, pp. 174–185, 2017.
[36] A. Alphonsa and G. Ravi, “Earthquake early warning system by IOT using Wireless sensor networks,” Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, pp. 1201–1205, 2016.
[37] A. D’alessandro et al., “Monitoring Earthquake through MEMS Sensors (MEMS project) in the town of Acireale (Italy),” 5th IEEE Int. Symp. Inert. Sensors Syst. Inert. 2018 - Proc., pp. 1–4, 2018.
[38] S. Valenti et al., “A low cost wireless sensor node for building monitoring,” EESMS 2018 - Environ. Energy, Struct. Monit. Syst. Proc., pp. 1–6, 2018.
[39] S. Sindhuja and J. S. J. Kevildon, “MEMS-based wireless sensors network system for post-seismic tremor harm evaluation and building monitoring,” IEEE Int. Conf. Circuit, Power Comput. Technol. ICCPCT 2015, pp. 1–4, 2015.
[40] V. Vujović and M. Maksimović, “Raspberry Pi as a Sensor Web node for home automation,” Comput. Electr. Eng., vol. 44, pp. 153–171, 2015.
[41] M. Sajjad, M. Nasir, F. U. M. Ullah, K. Muhammad, A. K. Sangaiah, and S. W. Baik, “Raspberry Pi assisted facial expression recognition framework for smart security in law-enforcement services,” Inf. Sci. (Ny)., vol. 479, pp. 416–431, 2019.
[42] S. E. Oltean, “Mobile Robot Platform with Arduino Uno and Raspberry Pi for Autonomous Navigation,” Procedia Manuf., vol. 32, pp. 572–577, 2019.
[43] R. Delgado, B. J. You, and B. W. Choi, “Real-time control architecture based on Xenomai using ROS packages for a service robot,” J. Syst. Softw., vol. 151, pp. 8–19, 2019.
[44] G. Johansson, “Real-Time Linux Testbench on Raspberry Pi 3 using Xenomai,” KTH ROYAL INSTITUTE OF TECHNOLOGY, 2018.
[45] Xenomai.org, “Xenomai.” [Online]. Available: https://xenomai.org/documentation/xenomai-3/pdf/. [Accessed: 02-Dec-2019].
[46] Microchip, “Data sheet dsPIC33EP256MC,” Microchip, Available: https://www.microchip.com/design-centers/16-bit/products/dspic33e, [Accessed: 02-Dec-2019].
[47] RaspberryPi.org, “Raspberry Pi 3 Model B+,” RaspberryPi.org, Available: https://static.raspberrypi.org/files/product-briefs/Raspberry-Pi-Model-Bplus-Product-Brief.pdf, [Accessed: 02-Dec-2019].
[48] GlobalTop Technology, “FGPMMOPA6H GPS Standalone Module Data Sheet,” GlobalTop Technology, Available: https://cdn-shop.adafruit.com/datasheets/GlobalTop-FGPMMOPA6H-Datasheet-V0A.pdf, [Accessed: 02-Dec-2019].
[49] D. Semiconductor, “Extremely Accurate SPI Bus RTC with Integrated Crystal and SRA.” Dallas Semiconductor, Available: https://www.sparkfun.com/datasheets/BreakoutBoards/DS3234.pdf, [Accessed: 02-Dec-2019].
[50] Analog Devices, “Low Noise, Low Drift, Low Power, 3-Axis MEMS Accelerometer ADXL355.” Analog Devices, Available: https://www.analog.com/media/en/technical-documentation/data-sheets/adxl354_355.pdf, [Accessed: 02-Dec-2019].
[51] Adafruit Industries, “Technical specifications INA219 Current sensor.” Adafruit Industries, Available: https://cdn-learn.adafruit.com/downloads/pdf/adafruit-ina219-current-sensor-breakout.pdf, [Accessed: 10-Jan-2020].
[52] ElecAustro, “Represa Chanlud.” [Online]. Available: https://www.elecaustro.gob.ec/centrales-y-represas/represa-chanlud/. [Accessed: 13-Apr-2020].
[53] Kinemetrics, “Technical specifications ETNA accelerograph.” Available: http://cnrrs.utcb.ro/cnrrs_en/divizii/divizia2/doc/etna.pdf, [Accessed: 17-Jan-2020].
[54] Kinemetrics, “User Guide Etna Digital Recorder, Document 302230.” https://docplayer.net/63635428-User-guide-etna-digital-recorder.html, [Accessed: 17-Jan-2020].
[55] Red Sísmica del Austro, “Reportes Red Sismica del Austro- Universidad de Cuenca -Ecuador.” [Online]. Available: linkedin.com/in/red-sísmica-u-cuenca-a223821a6.
[56] X. Liang and K. M. Mosalam, “Ground motion selection and modification evaluation for highway bridges subjected to Bi-directional horizontal excitation,” Soil Dyn. Earthq. Eng., vol. 130, no. December 2019, p. 105994, 2020.
[57] S. M. Sah, J. J. Thomsen, and D. Tcherniak, “Transverse vibrations induced by longitudinal excitation in beams with geometrical and loading imperfections,” J. Sound Vib., vol. 444, pp. 152–160, 2019.
[58] J. Jiménez-Pacheco, R. González-Drigo, L. G. Pujades Beneit, A. H. Barbat, and J. Calderón-Brito, “Traditional High-rise Unreinforced Masonry Buildings: Modeling and Influence of Floor System Stiffening on Their Overall Seismic Response,” Int. J. Archit. Herit., vol. 00, no. 00, pp. 1–38, 2020.
Published
2021-11-25
Section
Articles
How to Cite
Muñoz, M., Guevara, R., González, S., & Jiménez, J. C. (2021). Reliable Data Acquisition System for a Low-Cost Accelerograph Applied to Structural Health Monitoring. Journal of Applied Science, Engineering, Technology, and Education, 3(2), 181-194. https://doi.org/10.35877/454RI.asci159