Bridge Damage Detection in Presence of Varying Temperature Using Two-Step Neural Network Approach
Publication: Journal of Bridge Engineering
Volume 26, Issue 6
Abstract
The dynamic properties of bridges can be affected not only through damage but also from ambient uncertainty. False-positive or negative alarms may be raised if environmental effects are not considered in the detection algorithm. This article presents a two-step data-driven approach that can incorporate temperature effects in vibration-based damage detection and localization, provided the temperature is also measured. To detect the occurrence of damage, prediction errors of an autoassociative neural network (AANN) framework are first employed as a temperature-invariant novelty index (NI). Further, for damage localization, NIs associated with each of the damage cases, are classified using a radial basis function neural network (RBFNN). The proposed algorithm is numerically tested on a multispan bridge structure involving measurement uncertainties. The performance of the RBFNN classifier is further assessed using different classifier performance metrics. The possibility of positive and negative false alarms raised by the proposed algorithm and its sensitivity to measurement noise contamination is also investigated. It is observed that the proposed data-based approach can efficiently isolate a fault, even in the presence of temperature variation.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This study was funded by the Aeronautics Research & Development Board (DRDO), New Delhi, India (Grant No. ARDB/01/1051907/M/I).
References
Abdel-Jaber, H., and B. Glisic. 2019. “Monitoring of long-term prestress losses in prestressed concrete structures using fiber optic sensors.” Struct. Health Monit. 18 (1): 254–269. https://doi.org/10.1177/1475921717751870.
Alampalli, S. 2000. “Effects of testing, analysis, damage, and environment on modal parameters.” Mech. Syst. Sig. Process. 14 (1): 63–74. https://doi.org/10.1006/mssp.1999.1271.
Barai, S., and P. Pandey. 1995. “Vibration signature analysis using artificial neural networks.” J. Comput. Civ. Eng. 9 (4): 259–265. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(259).
Cao, X., Y. Sugiyama, and Y. Mitsui. 1998. “Application of artificial neural networks to load identification.” Comput. Struct. 69 (1): 63–78. https://doi.org/10.1016/S0045-7949(98)00085-6.
Cardini, A., and J. T. DeWolf. 2009. “Long-term structural health monitoring of a multi-girder steel composite bridge using strain data.” Struct. Health Monit. 8 (1): 47–58. https://doi.org/10.1177/1475921708094789.
Chakraborty, S., and J. T. DeWolf. 2006. “Development and implementation of a continuous strain monitoring system on a multi-girder composite steel bridge.” J. Bridge Eng. 11 (6): 753–762. https://doi.org/10.1061/(ASCE)1084-0702(2006)11:6(753).
Farrar, C. R., and D. A. Jauregui. 1998. “Comparative study of damage identification algorithms applied to a bridge: I. Experiment.” Smart Mater. Struct. 7 (5): 704. https://doi.org/10.1088/0964-1726/7/5/013.
Fath, A. H., F. Madanifar, and M. Abbasi. 2020. “Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems.” Petroleum 6 (1): 80–91. https://doi.org/10.1016/j.petlm.2018.12.002.
Figueiredo, E., G. Park, C. R. Farrar, K. Worden, and J. Figueiras. 2011. “Machine learning algorithms for damage detection under operational and environmental variability.” Struct. Health Monit. 10 (6): 559–572. https://doi.org/10.1177/1475921710388971.
Gavin, H. 2011. “Geometric stiffness effects in 2D and 3D frames.” In CE 131L. Matrix Structural Analysis. Durham, NC: Duke University.
Glisic, B., J. Chen, and D. Hubbell. 2011. “Streicker bridge: A comparison between bragg-grating long-gauge strain and temperature sensors and brillouin scattering-based distributed strain and temperature sensors.” In Sensors and smart structures technologies for civil, mechanical, and aerospace systems 2011, Vol. 7981. San Diego, CA: International Society for Optics and Photonics.
Gu, J., M. Gul, and X. Wu. 2017. “Damage detection under varying temperature using artificial neural networks.” Struct. Control Health Monit. 24 (11): e1998. https://doi.org/10.1002/stc.1998.
Jayawardena, A., D. Fernando, and M. Zhou. 1997. Comparison of multilayer perceptron and radial basis function networks as tools for flood forecasting, 173–182. Series of Proceedings and Reports-International Association of Hydrological Sciences. IAHS Publications No. 239. Wallingford, UK.
Jin, C., S. Jang, X. Sun, J. Li, and R. Christenson. 2016. “Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network.” J. Civ. Struct. Health Monit. 6 (3): 545–560. https://doi.org/10.1007/s13349-016-0173-8.
Khan, I. A., and D. R. Parhi. 2015. “Fault detection of composite beam by using the modal parameters and rbfnn technique.” J. Mech. Sci. Technol. 29 (4): 1637–1648. https://doi.org/10.1007/s12206-015-0335-3.
Kostić, B., and M. Gül. 2017. “Vibration-based damage detection of bridges under varying temperature effects using time-series analysis and artificial neural networks.” J. Bridge Eng. 22 (10): 04017065. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001085.
Kromanis, R., P. Kripakaran, and B. Harvey. 2016. “Long-term structural health monitoring of the Cleddau Bridge: Evaluation of quasi-static temperature effects on bearing movements.” Struct. Infrastruct. Eng. 12 (10): 1342–1355. https://doi.org/10.1080/15732479.2015.1117113.
Kullaa, J. 2009. “Eliminating environmental or operational influences in structural health monitoring using the missing data analysis.” J. Intell. Mater. Syst. Struct. 20 (11): 1381–1390. https://doi.org/10.1177/1045389X08096050.
Machavaram, R., and K. Shankar. 2013. “Joint damage identification using improved radial basis function (IRBF) networks in frequency and time domain.” Appl. Soft Comput. 13 (7): 3366–3379. https://doi.org/10.1016/j.asoc.2013.02.004.
Mehrjoo, M., N. Khaji, H. Moharrami, and A. Bahreininejad. 2008. “Damage detection of truss bridge joints using artificial neural networks.” Expert Syst. Appl. 35 (3): 1122–1131. https://doi.org/10.1016/j.eswa.2007.08.008.
Moaveni, B., and I. Behmanesh. 2012. “Effects of changing ambient temperature on finite element model updating of the dowling hall footbridge.” Eng. Struct. 43: 58–68. https://doi.org/10.1016/j.engstruct.2012.05.009.
Okafor, A. C., and A. Dutta. 2001. “Optimal ultrasonic pulse repetition rate for damage detection in plates using neural networks.” NDT & E Int. 34 (7): 469–481. https://doi.org/10.1016/S0963-8695(00)00078-5.
Ozdagli, A. I., and X. Koutsoukos. 2019. “Machine learning based novelty detection using modal analysis.” Comput.-Aided Civ. Infrastruct. Eng. 34 (12): 1119–1140. https://doi.org/10.1111/mice.v34.12.
Reddy, J., and C. Chin. 1998. “Thermomechanical analysis of functionally graded cylinders and plates.” J. Therm. Stresses 21 (6): 593–626. https://doi.org/10.1080/01495739808956165.
Reddy, R. R. K., and R. Ganguli. 2003. “Structural damage detection in a helicopter rotor blade using radial basis function neural networks.” Smart Mater. Struct. 12 (2): 232. https://doi.org/10.1088/0964-1726/12/2/311.
Salawu, O. 1997. “Detection of structural damage through changes in frequency: A review.” Eng. Struct. 19 (9): 718–723. https://doi.org/10.1016/S0141-0296(96)00149-6.
Santos, A., E. Figueiredo, M. Silva, C. Sales, and J. Costa. 2016. “Machine learning algorithms for damage detection: Kernel-based approaches.” J. Sound Vib. 363: 584–599. https://doi.org/10.1016/j.jsv.2015.11.008.
Sohn, H., C. R. Farrar, F. M. Hemez, D. D. Shunk, D. W. Stinemates, B. R. Nadler, and J. J. Czarnecki. 2004. A review of structural health monitoring literature: 1996–2001. Los Alamos National Laboratory Rep. LA-13976-MS. LANL.
Sokolova, M., N. Japkowicz, and S. Szpakowicz. 2006. “Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation.” In Proc., Australasian Joint Conf. on Artificial Intelligence, 1015–1021. Berlin: Springer.
Tharwat, A. 2020. “Classification assessment methods.” Appl. Comput. Inf. 2018: 1–13. https://doi.org/10.1016/j.aci.2018.08.003.
Weinstein, J. C., M. Sanayei, and B. R. Brenner. 2018. “Bridge damage identification using artificial neural networks.” J. Bridge Eng. 23 (11): 04018084. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001302.
Worden, K. 1997. “Structural fault detection using a novelty measure.” J. Sound Vib. 201 (1): 85–101. https://doi.org/10.1006/jsvi.1996.0747.
Xie, T., H. Yu, and B. Wilamowski. 2011. “Comparison between traditional neural networks and radial basis function networks.” In Proc., 2011 IEEE Int. Symp. on Industrial Electronics, 1194–1199. Piscataway, NJ: IEEE.
Xu, Y., B. Chen, C. Ng, K. Wong, and W. Chan. 2010. “Monitoring temperature effect on a long suspension bridge.” Struct. Control Health Monit. 17 (6): 632–653.
Yan, A.-M., G. Kerschen, P. De Boe, and J.-C. Golinval. 2005. “Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis.” Mech. Syst. Sig. Process. 19 (4): 847–864. https://doi.org/10.1016/j.ymssp.2004.12.002.
Yarnold, M., and F. Moon. 2015. “Temperature-based structural health monitoring baseline for long-span bridges.” Eng. Struct. 86: 157–167. https://doi.org/10.1016/j.engstruct.2014.12.042.
Zang, C., M. Friswell, and M. Imregun. 2007. “Structural health monitoring and damage assessment using frequency response correlation criteria.” J. Eng. Mech. 133 (9): 981–993. https://doi.org/10.1061/(ASCE)0733-9399(2007)133:9(981).
Zhou, H., Y. Ni, and J. Ko. 2011. “Eliminating temperature effect in vibration-based structural damage detection.” J. Eng. Mech. 137 (12): 785–796. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000273.
Zhou, Y., and L. Sun. 2019. “Insights into temperature effects on structural deformation of a cable-stayed bridge based on structural health monitoring.” Struct. Health Monit. 18 (3): 778–791. https://doi.org/10.1177/1475921718773954.
Zhu, Y., Y.-Q. Ni, H. Jin, D. Inaudi, and I. Laory. 2019. “A temperature-driven MPCA method for structural anomaly detection.” Eng. Struct. 190: 447–458. https://doi.org/10.1016/j.engstruct.2019.04.004.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 26 • Issue 6 • June 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 16, 2020
Accepted: Dec 16, 2020
Published online: Mar 26, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 26, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Siamak Ghadimi, Gholamreza Zamani-Ahari, Seyed Sina Kourehli, Crack detection in arbitrary beam cross-sections using a new mass-spring system, Structures, 10.1016/j.istruc.2022.12.067, 48, (133-146), (2023).
- Elisabetta Farneti, Nicola Cavalagli, Mario Costantini, Francesco Trillo, Federico Minati, Ilaria Venanzi, Filippo Ubertini, A method for structural monitoring of multispan bridges using satellite InSAR data with uncertainty quantification and its pre-collapse application to the Albiano-Magra Bridge in Italy, Structural Health Monitoring, 10.1177/14759217221083609, 22, 1, (353-371), (2022).
- Raffaele Zinno, Sina Shaffiee Haghshenas, Giuseppe Guido, Alessandro VItale, Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art, IEEE Access, 10.1109/ACCESS.2022.3199443, 10, (88058-88078), (2022).
- Eshwar Kuncham, Subhamoy Sen, Pankaj Kumar, Himanshu Pathak, An online model-based fatigue life prediction approach using extended Kalman filter, Theoretical and Applied Fracture Mechanics, 10.1016/j.tafmec.2021.103143, 117, (103143), (2022).
- Huu-Tai Thai, Machine learning for structural engineering: A state-of-the-art review, Structures, 10.1016/j.istruc.2022.02.003, 38, (448-491), (2022).
- Smriti Sharma, Subhamoy Sen, Real-time structural damage assessment using LSTM networks: regression and classification approaches, Neural Computing and Applications, 10.1007/s00521-022-07773-6, 35, 1, (557-572), (2022).