Vehicle-Assisted Techniques for Health Monitoring of Bridges

doi:10.3390/s20123460

Review

. 2020 Jun 19;20(12):3460.

doi: 10.3390/s20123460.

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Hoofar Shokravi¹, Hooman Shokravi², Norhisham Bakhary^{1

3}, Mahshid Heidarrezaei⁴, Seyed Saeid Rahimian Koloor⁵, Michal Petrů⁵

Affiliations

¹ School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia.
² Department of Civil Engineering, Islamic Azad University, Tabriz 5157944533, Iran.
³ Institute of Noise and Vibration, Universiti Teknologi Malaysia, City Campus, Jalan Semarak, Kuala Lumpur 54100, Malaysia.
⁴ Department of Bioprocess Engineering, School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia.
⁵ Institute for Nanomaterials, Advanced Technologies and Innovation (CXI), Technical University of Liberec (TUL), Studentska 2, 461 17 Liberec, Czech Republic.

PMID: 32575359
PMCID: PMC7349906
DOI: 10.3390/s20123460

Review

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Hoofar Shokravi et al. Sensors (Basel). 2020.

. 2020 Jun 19;20(12):3460.

doi: 10.3390/s20123460.

Authors

Hoofar Shokravi¹, Hooman Shokravi², Norhisham Bakhary^{1

3}, Mahshid Heidarrezaei⁴, Seyed Saeid Rahimian Koloor⁵, Michal Petrů⁵

Affiliations

¹ School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia.
² Department of Civil Engineering, Islamic Azad University, Tabriz 5157944533, Iran.
³ Institute of Noise and Vibration, Universiti Teknologi Malaysia, City Campus, Jalan Semarak, Kuala Lumpur 54100, Malaysia.
⁴ Department of Bioprocess Engineering, School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia.
⁵ Institute for Nanomaterials, Advanced Technologies and Innovation (CXI), Technical University of Liberec (TUL), Studentska 2, 461 17 Liberec, Czech Republic.

PMID: 32575359
PMCID: PMC7349906
DOI: 10.3390/s20123460

Abstract

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle's speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.

Keywords: bridge weigh in motion (BWIM); drive-by damage detection; indirect structural health monitoring; structural health monitoring (SHM); vehicle bridge interaction (VBI); weigh in motion (WIM).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

**Figure 1**
Schematic of an indirect health monitoring system for bridge structures.

**Figure 2**
Schematic of numerical vehicles model including (a) moving mass and (b) moving sprung.

See this image and copyright information in PMC

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References

1. Sun S., Sun L., Chen L. Damage detection based on structural responses induced by traffic load: Methodology and application. Int. J. Struct. Stab. Dyn. 2016;16:1640026. doi: 10.1142/S0219455416400265. - DOI
1. Chae M.J., Yoo H.S., Kim J.Y., Cho M.Y. Development of a wireless sensor network system for suspension bridge health monitoring. Autom. Constr. 2012;21:237–252. doi: 10.1016/j.autcon.2011.06.008. - DOI
1. Mufti A.A., Bakht B., Tadros G., Horosko A.T., Sparks G. Sensing Issues in Civil Structural Health Monitoring. Springer; Dordrecht, The Netherlands: 2005. Are Civil Structural Engineers “Risk Averse”? Can Civionics Help? pp. 3–12.
1. Farrar C.R., Worden K. An introduction to structural health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2007;365:303–315. doi: 10.1098/rsta.2006.1928. - DOI - PubMed
1. Shokravi H., Shokravi H., Bakhary N., Koloor S.S.R., Petru M. A Comparative Study of the Data-driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study. Appl. Sci. 2020;10:3132. doi: 10.3390/app10093132. - DOI

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[1] Sun S., Sun L., Chen L. Damage detection based on structural responses induced by traffic load: Methodology and application. Int. J. Struct. Stab. Dyn. 2016;16:1640026. doi: 10.1142/S0219455416400265. - DOI

[2] Sun S., Sun L., Chen L. Damage detection based on structural responses induced by traffic load: Methodology and application. Int. J. Struct. Stab. Dyn. 2016;16:1640026. doi: 10.1142/S0219455416400265. - DOI

[3] Chae M.J., Yoo H.S., Kim J.Y., Cho M.Y. Development of a wireless sensor network system for suspension bridge health monitoring. Autom. Constr. 2012;21:237–252. doi: 10.1016/j.autcon.2011.06.008. - DOI

[4] Chae M.J., Yoo H.S., Kim J.Y., Cho M.Y. Development of a wireless sensor network system for suspension bridge health monitoring. Autom. Constr. 2012;21:237–252. doi: 10.1016/j.autcon.2011.06.008. - DOI

[5] Mufti A.A., Bakht B., Tadros G., Horosko A.T., Sparks G. Sensing Issues in Civil Structural Health Monitoring. Springer; Dordrecht, The Netherlands: 2005. Are Civil Structural Engineers “Risk Averse”? Can Civionics Help? pp. 3–12.

[6] Mufti A.A., Bakht B., Tadros G., Horosko A.T., Sparks G. Sensing Issues in Civil Structural Health Monitoring. Springer; Dordrecht, The Netherlands: 2005. Are Civil Structural Engineers “Risk Averse”? Can Civionics Help? pp. 3–12.

[7] Farrar C.R., Worden K. An introduction to structural health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2007;365:303–315. doi: 10.1098/rsta.2006.1928. - DOI - PubMed

[8] Farrar C.R., Worden K. An introduction to structural health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2007;365:303–315. doi: 10.1098/rsta.2006.1928. - DOI - PubMed

[9] Shokravi H., Shokravi H., Bakhary N., Koloor S.S.R., Petru M. A Comparative Study of the Data-driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study. Appl. Sci. 2020;10:3132. doi: 10.3390/app10093132. - DOI

[10] Shokravi H., Shokravi H., Bakhary N., Koloor S.S.R., Petru M. A Comparative Study of the Data-driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study. Appl. Sci. 2020;10:3132. doi: 10.3390/app10093132. - DOI

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Vehicle-Assisted Techniques for Health Monitoring of Bridges

Affiliations

Vehicle-Assisted Techniques for Health Monitoring of Bridges

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Affiliations

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Conflict of interest statement

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