DOI : https://doi.org/10.18517/ijaseit.14.2.19291

Online Real-Time Monitoring System of A Structural Steel Railway Bridge Using Wireless Smart Sensors

Okghi A. Qowiy (1), Wimpie A. N. Aspar (2), Herry Susanto (3), Thiya Fiantika (4), Suwarjono (5), Aam Muharam (6), Fauzi D. Setiawan (7), Rahmat Burhanuddin (8)
(1) Research Center for Transportation Technology, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
(2) Research Center for Transportation Technology, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
(3) PT. Daun Biru Engineering Indonesia, Depok 16455, Indonesia
(4) Research Center for Transportation Technology, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
(5) PT. Daun Biru Engineering Indonesia, Depok 16455, Indonesia
(6) Research Center for Transportation Technology, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
(7) Research Center for Transportation Technology, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
(8) PT. Sekawan Senantiasa Sejahtera (3S) Engineering, Depok 16519, Indonesia
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How to cite (IJASEIT) :
Qowiy, Okghi A., et al. “Online Real-Time Monitoring System of A Structural Steel Railway Bridge Using Wireless Smart Sensors”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 2, Apr. 2024, pp. 381-92, doi:10.18517/ijaseit.14.2.19291.
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In the transportation network, railway bridges are crucial for the transfer of both passengers and commodities. Railway bridges require continuous monitoring to observe their performance. A structural health monitoring system is one method for assessing the viability of a railway bridge structure. The functioning of railroad bridge structures has been extensively observed using wireless technology. This research aims to implement smart wireless sensors for monitoring the structural health of the railway bridge online in real-time during operation. Many sensor kinds were installed on the railway bridge, including strain gauges, accelerometers, linear variable displacement transducers, and proximity sensors. Geometric modeling and numerical simulation were performed to find critical frame locations on the railway bridge where the instrumentation sensors would be placed. In this study, MONITA® is employed for data acquisition modules. The MONITA® system consists of a combination of hardware and software that functions to retrieve, send, store, and process data. This paper describes the result of the establishment of this method to comprehend the performance of the steel railway bridge structure in real-time via the human-machine interface display dashboard. As a result, the monitoring system can appropriately be used to assess a structural railway bridge in real-time. This study may be helpful to practicing engineers and researchers in future studies of steel railway bridge evaluation. This could be a useful reference for future studies in implementing such systems as the railway bridge early warning system technique in detecting bridge damage.

E. OBrien, M. A. Khan, D. P. McCrum, and A. Žnidarič, “Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection,” Applied Sciences, vol. 10, no. 2, 2020, doi: 10.3390/app10020663.

A. Malekjafarian, C.-W. Kim, E. J. OBrien, L. J. Prendergast, P. C. Fitzgerald, and S. Nakajima, “Experimental Demonstration of a Mode Shape-Based Scour-Monitoring Method for Multispan Bridges with Shallow Foundations,” Journal of Bridge Engineering, vol. 25, no. 8, p. 4020050, 2020, doi: 10.1061/(ASCE)BE.1943-5592.0001586.

M. A. Khan, D. P. McCrum, L. J. Prendergast, E. J. OBrien, P. C. Fitzgerald, and C.-W. Kim, “Laboratory investigation of a bridge scour monitoring method using decentralized modal analysis,” Struct Health Monit, vol. 20, no. 6, pp. 3327–3341, 2021, doi: 10.1177/1475921720985122.

B. Bachinilla, A. Evangelista, M. Siddhpura, A. N. Haddad, and B. B. F. da Costa, “High-Speed Railway Bridge and Pile Foundation: A Review,” Infrastructures (Basel), vol. 7, no. 11, 2022, doi: 10.3390/infrastructures7110154.

Q. He et al., “Intelligent construction for the transportation infrastructure: a review,” Intelligent Transportation Infrastructure, vol. 1, p. liac007, Sep. 2022, doi: 10.1093/iti/liac007.

V. Chakraborty and S. Dutta, “Indian railway infrastructure systems: global comparison, challenges and opportunities,” Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, vol. 175, no. 3, pp. 127–140, 2022, doi: 10.1680/jsmic.22.00014.

E. F. Souza, C. Bragança, A. Meixedo, D. Ribeiro, T. N. Bittencourt, and H. Carvalho, “Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts,” Applied Sciences, vol. 13, no. 12, 2023, doi: 10.3390/app13126940.

W. Barasa, T. Fiantika, D. A. Purnomo, and W. Aspar, “The Comparative Study of Railway Bridge Design Load Between PM 60/2012 and EN 1991: 2-2003,” Majalah Ilmiah Pengkajian Industri (Journal of Industrial Research and Innovation), vol. 14, no. 2, pp. 107–116, 2020, doi: https://doi.org/10.29122/mipi.v14i2.4196.

M. A. B. Nadi, Nurfaizia, M. N. Karunia, W. A. N. Aspar, W. Barasa, and A. Fudholi, “Characterization of Site Effect and Natural Frequency of Railway Bridges,” International Journal of Sustainable Development and Planning, vol. 17, no. 1, pp. 243–249, Feb. 2022, doi: 10.18280/ijsdp.170124.

Y. Ren, E. J. OBrien, D. Cantero, and J. Keenahan, “Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains,” Applied Sciences, vol. 12, no. 10, 2022, doi: 10.3390/app12104972.

W. Barasa, W. A. N. Aspar, D. A. Purnomo, M. A. B. Nadi, T. Fiantika, and S. P. Primadiyanti, “Assessment of an existing steel railway bridge structure,” AIP Conf Proc, vol. 2689, no. 1, p. 040019, Jul. 2023, doi: 10.1063/5.0116333.

O. S. Sonbul and M. Rashid, “Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review,” Sensors, vol. 23, no. 9, 2023, doi: 10.3390/s23094230.

Z. Deng, M. Huang, N. Wan, and J. Zhang, “The Current Development of Structural Health Monitoring for Bridges: A Review,” Buildings, vol. 13, no. 6, 2023, doi: 10.3390/buildings13061360.

X. Zhang and W. Zhou, “Structural Vibration Data Anomaly Detection Based on Multiple Feature Information Using CNN-LSTM Model,” Struct Control Health Monit, vol. 2023, p. 3906180, 2023, doi: 10.1155/2023/3906180.

A. Meixedo, D. Ribeiro, J. Santos, R. Calçada, and M. Todd, “Progressive numerical model validation of a bowstring-arch railway bridge based on a structural health monitoring system,” J Civ Struct Health Monit, vol. 11, no. 2, pp. 421–449, 2021, doi: 10.1007/s13349-020-00461-w.

K. Maes, L. Van Meerbeeck, E. P. B. Reynders, and G. Lombaert, “Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51,” Mech Syst Signal Process, vol. 165, p. 108380, 2022, doi: https://doi.org/10.1016/j.ymssp.2021.108380.

A. Pourtarki, H. B. Ghavifekr, and H. Afshin, “Study on the dynamic behaviour of Bafgh-Bandar Abbas lane railway bridge for structural health monitoring purpose,” Australian Journal of Structural Engineering, vol. 24, no. 3, pp. 243–253, 2023, doi: 10.1080/13287982.2022.2149975.

X. Li, Y. Xiao, H. Guo, and J. Zhang, “A BIM Based Approach for Structural Health Monitoring of Bridges,” KSCE Journal of Civil Engineering, vol. 26, no. 1, pp. 155–165, 2022, doi: 10.1007/s12205-021-2040-3.

M. R. Azim and M. Gül, “Damage detection framework for truss railway bridges utilizing statistical analysis of operational strain response,” Struct Control Health Monit, vol. 27, no. 8, p. e2573, 2020, doi: https://doi.org/10.1002/stc.2573.

A. Meixedo, J. Santos, D. Ribeiro, R. Calçada, and M. Todd, “Damage detection in railway bridges using traffic-induced dynamic responses,” Eng Struct, vol. 238, p. 112189, 2021, doi: https://doi.org/10.1016/j.engstruct.2021.112189.

M. R. Azim, “A Data-Driven Damage Assessment Tool for Truss-Type Railroad Bridges Using Train Induced Strain Time-History Response,” Australian Journal of Structural Engineering, vol. 22, no. 2, pp. 147–162, 2021, doi: 10.1080/13287982.2021.1908710.

M. R. Azim and M. Gül, “Damage Detection of Steel-Truss Railway Bridges Using Operational Vibration Data,” Journal of Structural Engineering, vol. 146, no. 3, p. 4020008, 2020, doi: 10.1061/(ASCE)ST.1943-541X.0002547.

Z.-W. Li, X.-Z. Liu, and S.-X. Chen, “A reliability assessment approach for slab track structure based on vehicle-track dynamics and surrogate model,” Proc Inst Mech Eng O J Risk Reliab, vol. 236, no. 1, pp. 79–89, 2022, doi: 10.1177/1748006X211028405.

M. J. Khosraviani, O. Bahar, and S. H. Ghasemi, “Global and Local Damage Detection in Continuous Bridge Decks Using Instantaneous Amplitude Energy and Cross-Correlation Function Methods,” KSCE Journal of Civil Engineering, vol. 25, no. 2, pp. 603–620, 2021, doi: 10.1007/s12205-020-0622-0.

S. Karimi and O. Mirza, “Damage identification in bridge structures: review of available methods and case studies,” Australian Journal of Structural Engineering, vol. 24, no. 2, pp. 89–119, 2023, doi: 10.1080/13287982.2022.2120239.

Z. Li, Y. Lan, and W. Lin, “Investigation of Frequency-Domain Dimension Reduction for A2M-Based Bridge Damage Detection Using Accelerations of Moving Vehicles,” Materials, vol. 16, no. 5, 2023, doi: 10.3390/ma16051872.

M. Zhao, A. Sadhu, and M. Capretz, “Multiclass anomaly detection in imbalanced structural health monitoring data using convolutional neural network,” Journal of Infrastructure Preservation and Resilience, vol. 3, no. 1, p. 10, 2022, doi: 10.1186/s43065-022-00055-4.

C. Qu, H. Zhang, R. Zhang, S. Zou, L. Huang, and H. Li, “Multiclass Anomaly Detection of Bridge Monitoring Data with Data Migration between Different Bridges for Balancing Data,” Applied Sciences, vol. 13, no. 13, 2023, doi: 10.3390/app13137635.

M. A. Mousa et al., “Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review,” Infrastructures (Basel), vol. 6, no. 12, 2021, doi: 10.3390/infrastructures6120176.

Y.-W. Wang, Y.-Q. Ni, and S.-M. Wang, “Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review,” Intelligent Transportation Infrastructure, vol. 1, p. liac009, Sep. 2022, doi: 10.1093/iti/liac009.

Z. He, W. Li, H. Salehi, H. Zhang, H. Zhou, and P. Jiao, “Integrated structural health monitoring in bridge engineering,” Autom Constr, vol. 136, p. 104168, 2022, doi: https://doi.org/10.1016/j.autcon.2022.104168.

J. Xin, Y. Jiang, J. Zhou, L. Peng, S. Liu, and Q. Tang, “Bridge deformation prediction based on SHM data using improved VMD and conditional KDE,” Eng Struct, vol. 261, p. 114285, 2022, doi: https://doi.org/10.1016/j.engstruct.2022.114285.

A. Malekjafarian, R. Corbally, and W. Gong, “A review of mobile sensing of bridges using moving vehicles: Progress to date, challenges and future trends,” Structures, vol. 44, pp. 1466–1489, 2022, doi: https://doi.org/10.1016/j.istruc.2022.08.075.

P. Russo and M. Schaerf, “Anomaly detection in railway bridges using imaging techniques,” Sci Rep, vol. 13, no. 1, p. 3916, 2023, doi: 10.1038/s41598-023-30683-z.

D. Ribeiro, R. Calçada, M. Brehm, and V. Zabel, “Train–Track–Bridge Dynamic Interaction on a Bowstring-Arch Railway Bridge: Advanced Modeling and Experimental Validation,” Sensors, vol. 23, no. 1, 2023, doi: 10.3390/s23010171.

M. Abdulkarem, K. Samsudin, F. Z. Rokhani, and M. F. A. Rasid, “Wireless sensor network for structural health monitoring: A contemporary review of technologies, challenges, and future direction,” Struct Health Monit, vol. 19, no. 3, pp. 693–735, 2020, doi: 10.1177/1475921719854528.

S. Mustapha, Y. Lu, C.-T. Ng, and P. Malinowski, “Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review,” Vibration, vol. 4, no. 3, pp. 551–585, 2021, doi: 10.3390/vibration4030033.

A. Sofi, J. Jane Regita, B. Rane, and H. H. Lau, “Structural health monitoring using wireless smart sensor network – An overview,” Mech Syst Signal Process, vol. 163, p. 108113, 2022, doi: https://doi.org/10.1016/j.ymssp.2021.108113.

N. Navabian, S. Beskhyroun, and J. Matulich, “Development of wireless smart sensor network for vibration-based structural health monitoring of civil structures,” Structure and Infrastructure Engineering, vol. 18, no. 3, pp. 345–361, 2022, doi: 10.1080/15732479.2020.1850801.

F. Zanelli, F. Castelli-Dezza, D. Tarsitano, M. Mauri, M. L. Bacci, and G. Diana, “Design and Field Validation of a Low Power Wireless Sensor Node for Structural Health Monitoring,” Sensors, vol. 21, no. 4, 2021, doi: 10.3390/s21041050.

M. Karatas and B. S. Onggo, “Optimising the barrier coverage of a wireless sensor network with hub-and-spoke topology using mathematical and simulation models,” Comput Oper Res, vol. 106, pp. 36–48, 2019, doi: https://doi.org/10.1016/j.cor.2019.02.007.

F. Zanelli, N. Debattisti, M. Mauri, A. Argentino, and M. Belloli, “Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification,” Applied Sciences, vol. 13, no. 6, 2023, doi: 10.3390/app13063620.

Y. Fu, Y. Zhu, T. Hoang, K. Mechitov, and B. F. Spencer, “xImpact: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts,” Sensors, vol. 22, no. 15, 2022, doi: 10.3390/s22155701.

Y. Fu, T. Hoang, K. Mechitov, J. R. Kim, D. Zhang, and B. F. Spencer, “xShake: Intelligent wireless system for cost-effective real-time seismic monitoring of civil infrastructure,” Smart Struct Syst, vol. 28, no. 4, pp. 483–497, Oct. 2021, doi: 10.12989/sss.2021.28.4.483.

S. S. Saidin, A. Jamadin, S. Abdul Kudus, N. Mohd Amin, and M. A. Anuar, “An Overview: The Application of Vibration-Based Techniques in Bridge Structural Health Monitoring,” Int J Concr Struct Mater, vol. 16, no. 1, p. 69, 2022, doi: 10.1186/s40069-022-00557-1.

D. M. Siringoringo, Y. Fujino, M. Suzuki, and V. Mehta, “Implementation of Wireless Sensor Network for Continuous Seismic Monitoring of Isolated Cable-Stayed Bridge,” Journal of Bridge Engineering, vol. 27, no. 4, p. 4022011, 2022, doi: 10.1061/(ASCE)BE.1943-5592.0001813.

Z. Ni, S. Cai, and C. Ni, “Application of Wireless Sensor Network Based on Improved Genetic Algorithm in Bridge Health Monitoring,” Sensors and Materials, vol. 35, no. 5, pp. 1659–1670, 2023, doi: 10.18494/SAM4309.

D. A. Purnomo, W. A. N. Aspar, W. Barasa, S. M. Harjono, P. Sukamdo, and T. Fiantika, “Initial Implementation of Structural Health Monitoring System of a Railway Bridge,” IOP Conf Ser Mater Sci Eng, vol. 1200, no. 1, p. 012019, Nov. 2021, doi: 10.1088/1757-899x/1200/1/012019.

T. Fiantika, W. A. N. Aspar, D. A. Purnomo, W. Barasa, S. M. Harjono, and S. P. Primadiyanti, “A review of structural health monitoring systems and application for railway bridges,” AIP Conf Proc, vol. 2646, no. 1, p. 50006, Sep. 2023, doi: 10.1063/5.0115843.

D. M. Siringoringo and Y. Fujino, “Wavelet-Based Analysis for Detection of Isolation Bearing Malfunction in a Continuous Multi-Span Girder Bridge,” Journal of Earthquake Engineering, vol. 26, no. 10, pp. 5237–5267, 2022, doi: 10.1080/13632469.2020.1868363.

C. Yang, “An adaptive sensor placement algorithm for structural health monitoring based on multi-objective iterative optimization using weight factor updating,” Mech Syst Signal Process, vol. 151, p. 107363, 2021, doi: https://doi.org/10.1016/j.ymssp.2020.107363.

M. Nasir, A. R. Javed, M. A. Tariq, M. Asim, and T. Baker, “Feature engineering and deep learning-based intrusion detection framework for securing edge IoT,” J Supercomput, vol. 78, no. 6, pp. 8852–8866, 2022, doi: 10.1007/s11227-021-04250-0.

J. Yang et al., “A Review on Damage Monitoring and Identification Methods for Arch Bridges,” Buildings, vol. 13, no. 8, 2023, doi: 10.3390/buildings13081975.

E. Yakıcı and M. Karatas, “Solving a multi-objective heterogeneous sensor network location problem with genetic algorithm,” Computer Networks, vol. 192, p. 108041, 2021, doi: https://doi.org/10.1016/j.comnet.2021.108041.

S. Samudra, M. Barbosh, and A. Sadhu, “Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring,” Sensors, vol. 23, no. 7, 2023, doi: 10.3390/s23073365.

STAAD.Pro V8i (SELECTseries 4) Technical Reference Manual, Bently Software Inc., Exton, PA, USA, 2012.

Transportation Minister of the Republic of Indonesia, Regulation no. 60 of 2012: Railroad Technical Requirements, PM 60 Tahun 2012, Minister of Transportation, Jakarta, Indonesia, 2012. [Online]. Available: https://djka.dephub.go.id/regulasi?lang=en

Indonesia National Standard: Loads for bridges, SNI 1725:2016, National Standardization of Indonesia, Jakarta, Indonesia, 2016. [Online]. Available: https://akses-sni.bsn.go.id/sni

Indonesia National Standard: Specifications for Structural Steel Buildings, SNI 1729:2020, National Standardization of Indonesia, Jakarta, Indonesia, 2020. [Online]. Available: https://akses-sni.bsn.go.id/sni

Indonesia National Standard: Bridge design against earthquake loads, SNI 2833:2016, National Standardization of Indonesia, Jakarta, Indonesia, 2016. [Online]. Available: https://akses-sni.bsn.go.id/sni

J. Ding, T. Xu, C. Jin, Z. Wang, J. Zhao, and T. Liu, “Impact of Equalization-Enhanced Phase Noise on Digital Nonlinearity Compensation in High-Capacity Optical Communication Systems,” Sensors, vol. 20, no. 15, 2020, doi: 10.3390/s20154149.

A. Kajmakovic, K. Diwold, K. Römer, J. Pestana, and N. Kajtazovic, “Degradation Detection in a Redundant Sensor Architecture,” Sensors, vol. 22, no. 12, 2022, doi: 10.3390/s22124649.

R. R. Nigmatullin, E. A. Bataleva, K. S. Nepeina, and V. E. Matiukov, “Quality control of the initial magnetotelluric data: Analysis of calibration curves using a fitting function represented by the ratio of 4th-order polynomials,” Measurement, vol. 216, p. 112914, 2023, doi: https://doi.org/10.1016/j.measurement.2023.112914.

S. Adla, N. K. Rai, S. H. Karumanchi, S. Tripathi, M. Disse, and S. Pande, “Laboratory Calibration and Performance Evaluation of Low-Cost Capacitive and Very Low-Cost Resistive Soil Moisture Sensors,” Sensors, vol. 20, no. 2, 2020, doi: 10.3390/s20020363.

W. A. N. Aspar, M. H. Pradono, W. Barasa, L. S. B. Wibowo, S. P. Primadiyanti, D. A. Purnomo, and E. I. N. S. P. J. D. S. Pasadena, “Direct analysis of a steel railway bridge via monitoring system of instrumented structure,” Int J Adv Sci Eng Inf Technol., submitted to IJASEIT for publication.

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