SENSOR FUSION FOR STRUCTURAL HEALTH MONITORING USING ACCELEROMETERS, STRAIN GAUGES AND VISION DATA
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Assistant Professor, Department of Civil Engineering
Jagannath University, Jaipur, Rajasthan, India
Jagannath University, Jaipur, Rajasthan, India
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Abstract
Structural health monitoring (SHM) increasingly relies on heterogeneous sensing because no single measurement modality simultaneously provides high-rate dynamics, low-frequency deformation, and spatially dense response information. This paper develops an IEEE-style multimodal framework that fuses accelerometer, strain-gauge, and vision-derived displacement data for damage-sensitive state estimation and condition assessment of bridge and frame structures. The proposed workflow combines common-clock synchronization, modality-specific pre-processing, strain-to-displacement calibration, multirate Kalman filtering, frequency-selective complementary fusion, confidence weighting, and physics-based consistency checks. The study is evidence-backed rather than claiming a new field campaign: the practical validation section synthesizes published laboratory and field experiments, including strain-vision fusion using physics-informed neural networks, strain-acceleration bridge displacement estimation, and asynchronous vision-acceleration fusion verified on a steel box-girder pedestrian bridge. A controlled reproducible benchmark is additionally used to demonstrate algorithm behavior under noise, asynchronous sampling, and sensor dropout; these simulated results are explicitly separated from published field data. Across the literature, fused estimates consistently reduce drift and fill frequency-band gaps that limit single-modality systems. Published validation includes displacement RMSE below 3 mm for asynchronous vision-acceleration fusion and NRMSE below 4% for an adaptive vision-acceleration framework, while recent strain-vision studies demonstrate physically consistent reconstruction of structural response. The combined evidence supports multimodal fusion as a practical route toward robust, redundant, and interpretable SHM, while highlighting remaining challenges in calibration, environmental compensation, uncertainty quantification, bandwidth mismatch, line-of-sight loss, and long-term sensor reliability.
Index Terms—structural health monitoring, sensor fusion, accelerometer, strain gauge, computer vision, displacement estimation, Kalman filter, bridge monitoring, data synchronization, multimodal sensing.
Index Terms—structural health monitoring, sensor fusion, accelerometer, strain gauge, computer vision, displacement estimation, Kalman filter, bridge monitoring, data synchronization, multimodal sensing.
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Double-Blind Peer Review
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CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 10
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2
Article Type:
Research Article
Publication Date:
Oct 12 2026
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KUMAR, S. (2026). Sensor Fusion for Structural Health Monitoring Using Accelerometers, Strain Gauges and Vision Data. International Journal of Science, Strategic Management and Technology, 02(10), 1-9. https://doi.org/10.55041/ijsmt.v2i10.017
KUMAR, SUNIL. "Sensor Fusion for Structural Health Monitoring Using Accelerometers, Strain Gauges and Vision Data." International Journal of Science, Strategic Management and Technology, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i10.017.
KUMAR, SUNIL. "Sensor Fusion for Structural Health Monitoring Using Accelerometers, Strain Gauges and Vision Data." International Journal of Science, Strategic Management and Technology 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i10.017.
References
[1] C. R. Farrar and K. Worden, Structural Health Monitoring: A Machine Learning Perspective. Chichester, U.K.: Wiley, 2013.
[2] A. W. Smyth and M. Wu, “Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 706–723, 2007.
[3] J. W. Park, S. H. Sim, and H. J. Jung, “Displacement estimation using multimetric data fusion,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 6, pp. 1675–1682, 2013.
[4] J. W. Park, K. C. Lee, S. H. Sim, H. J. Jung, and B. F. Spencer Jr., “Traffic safety evaluation for railway bridges using expanded multisensor data fusion,” Computer-Aided Civil and Infrastructure Engineering, vol. 31, no. 10, pp. 749–760, 2016.
[5] Z. Ma, J. Chung, P. Liu, and H. Sohn, “Bridge displacement estimation by fusing accelerometer and strain gauge measurements,” Structural Control and Health Monitoring, vol. 28, no. 6, Art. no. e2733, 2021, doi: 10.1002/stc.2733.
[6] Z. Ma, J. Choi, and H. Sohn, “Real-time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements,” Computer-Aided Civil and Infrastructure Engineering, vol. 37, no. 6, pp. 688–703, 2022, doi: 10.1111/mice.12767.
[7] Y. Chen et al., “Accurate structural displacement monitoring by data fusion of a consumer-grade camera and accelerometers,” Engineering Structures, vol. 262, Art. no. 114303, 2022, doi: 10.1016/j.engstruct.2022.114303.
[8] K. Aminfar, M. Ghyabi, Y. Shen, D. Lattanzi, G. Ciaramella, M. Gherlone, and C. Surace, “Data fusion in structural health monitoring: Integrating computer vision and strain gauges for enhanced inspection,” in Bridge Maintenance, Safety, Management, Digitalization and Sustainability, 2024, pp. 709–716, doi: 10.1201/9781003483755-82.
[9] D. Feng, M. Q. Feng, E. Ozer, and Y. Fukuda, “A vision-based sensor for noncontact structural displacement measurement,” Sensors, vol. 15, no. 7, pp. 16557–16575, 2015.
[10] D. Feng, Y. Fukuda, M. Feng, and M. Mizuta, “Nontarget vision sensor for remote measurement of bridge dynamic response,” Journal of Bridge Engineering, vol. 20, no. 12, 2015, doi: 10.1061/(ASCE)BE.1943-5592.0000747.
[2] A. W. Smyth and M. Wu, “Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 706–723, 2007.
[3] J. W. Park, S. H. Sim, and H. J. Jung, “Displacement estimation using multimetric data fusion,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 6, pp. 1675–1682, 2013.
[4] J. W. Park, K. C. Lee, S. H. Sim, H. J. Jung, and B. F. Spencer Jr., “Traffic safety evaluation for railway bridges using expanded multisensor data fusion,” Computer-Aided Civil and Infrastructure Engineering, vol. 31, no. 10, pp. 749–760, 2016.
[5] Z. Ma, J. Chung, P. Liu, and H. Sohn, “Bridge displacement estimation by fusing accelerometer and strain gauge measurements,” Structural Control and Health Monitoring, vol. 28, no. 6, Art. no. e2733, 2021, doi: 10.1002/stc.2733.
[6] Z. Ma, J. Choi, and H. Sohn, “Real-time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements,” Computer-Aided Civil and Infrastructure Engineering, vol. 37, no. 6, pp. 688–703, 2022, doi: 10.1111/mice.12767.
[7] Y. Chen et al., “Accurate structural displacement monitoring by data fusion of a consumer-grade camera and accelerometers,” Engineering Structures, vol. 262, Art. no. 114303, 2022, doi: 10.1016/j.engstruct.2022.114303.
[8] K. Aminfar, M. Ghyabi, Y. Shen, D. Lattanzi, G. Ciaramella, M. Gherlone, and C. Surace, “Data fusion in structural health monitoring: Integrating computer vision and strain gauges for enhanced inspection,” in Bridge Maintenance, Safety, Management, Digitalization and Sustainability, 2024, pp. 709–716, doi: 10.1201/9781003483755-82.
[9] D. Feng, M. Q. Feng, E. Ozer, and Y. Fukuda, “A vision-based sensor for noncontact structural displacement measurement,” Sensors, vol. 15, no. 7, pp. 16557–16575, 2015.
[10] D. Feng, Y. Fukuda, M. Feng, and M. Mizuta, “Nontarget vision sensor for remote measurement of bridge dynamic response,” Journal of Bridge Engineering, vol. 20, no. 12, 2015, doi: 10.1061/(ASCE)BE.1943-5592.0000747.
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