Abstract
In order to obtain the degree of damage in reinforced concrete (RC) beams exposed to fire, using the equivalent fire exposure time as the damage index, a new method of damage identification based on the support vector machine technology was proposed. Firstly, the feasibility analysis was conducted based on finite element models of simply supported beams. Thereafter, four RC simply supported beams were designed for fire test and vibration test, which were used to amend the finite element model and the SVM-based identification method. Fire tests were carried out on 4 beams for 60, 90, 120, and 150 min, respectively. During and after the fire tests, structural modal information were recorded. The first two order modal information, as SVM input paraments, was used to predict the equivalent fire exposure time based on SVM. The predicted results were very close to the actual fire exposure time. The residual bearing capacities of the beams after fire were calculated according to the predicted fire exposure time, which were close to experimental results. It indicated that the equivalent fire exposure time as the output parameter for damage identification was reliable. Finally, on the basis of damage identification method for simply supported beams, a new three-step positioning method was established for identifing the degree of damage in continuous beams. The method was applied to a thress-span continuous beam. The numercial situlation results revealed that the three-step positioning method was accurate.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bakhary, N., Hao, H., and Deeks, A. J. (2010). “Substructuring technique for damage detection using statistical multi-stage artificial neural network.” Advances in Structural Engineering, Vol. 13, No. 4, pp. 619–640, DOI: https://doi.org/10.1260/1369-4332.13.4.619.
Dos Santos, J. R., Branco, F. A., and De Brito, J. (2002). “Assessment of concrete structures subjected to fire-the FB Test.” Magazine of Concrete Research, Vol. 54, No. 3, pp. 203–216.
Duron, Z. H. (2005). Early warning capability for firefighter: Testing of collapse prediction techniques, National Institute of Standards and Technology, Gaithersburg, MD, USA.
Fan, W. and Qiao, P. Z. (2011). “Vibration-based damage identification methods: A review and comparative study.” Structural Health Monitoring, Vol. 10, No. 1, pp. 83–111, DOI: https://doi.org/10.1177/1475921710365419.
Guo, Z. H. and Shi, X. D. (2012). “Experiment and calculation of reinforced concrete at elevated temperatures.” Tsinghua University Press, Beijing, China, pp. 125–178.
He, X. H., Yan, W. M., Zhang, A. L., and Wang, Z. (2011). “Damage detection experiment on beam string structure using support vector machine.” Journal of Vibration, Measurement & Diagnosis, Vol. 31, No. 1, pp. 45–49.
Lie, T. T. (2003). “Structural fire protection.” ASCE, New Work, USA, pp. 263–274.
Liu, C. W., Huang, X. H., Miao, J. J., and Ba, G. Z. (2019). “Modification of finite element models based on support vector machines for reinforced concrete beam vibrational analyses at elevated temperatures.” Structure Control and Health Monitor, Vol. 26, No. 6, pp. 1–18, DOI: https://doi.org/10.1002/stc.2350.
Liu, C. W., Liu, C. F., Xu, W. L., Zhang, X. B., and Lan, Y. (2018). “A fractal-interpolation model for diagnosing spalling risk in concrete at elevated temperatures.” KSCE Journal of Civil Engineering, Vol. 22, No. 12, pp. 5154–5163, DOI: https://doi.org/10.1007/s12205-018-0697-z.
Liu, C. W., Miao, J. J., and Zhao, C. Y. (2016). “Finite element model fractional steps updating strategy for spatial lattice structures based on generalized regression neural network.” Shock and Vibration, pp. 1–10, DOI: https://doi.org/10.1155/2016/5845326.
Lv, J. L., Dong, Y. L., and Sun, J. D. (2012). “Effect of fire on self-excited vibration characteristic of composite beams of the overall structure.” Applied Mechanics and Materials, Vol. 105–107, pp. 760–764, DOI: https://doi.org/10.4028/www.scientific.net/AMM.105-107.760.
Mita, A. and Hagiwara, H. (2003). “Damage diagnosis of a building structure using support vector machine and modal frequency patterns.” Proc., Conf, on Smart Structures and Materials, San Diego, CA, Vol. 5057, No. 118–125, DOI: https://doi.org/10.1117/12.482705.
Ni, Y. Q., Zhou, X. T., Ko, J. M., and Wang, B. S. (2000). “Vibration based damage localization in ting kau bridge using probabilistic neutral network.” Advances in Structural Dynamics, J.M. Ko and Y.L. Xu (eds.), Elsevier Science Ltd, Oxford, UK, (II), pp. 1069–1076.1.
Ou, J. P. and Li, H. (2010). “Structural health monitoring in mainland China: Review and future trends.” Structural Health Monitoring, Vol. 9, No. 3, pp. 219–231, DOI: https://doi.org/10.1177/1475921710365269.
Whelan, M. J., Tempest, B. Q., and Scott, D. B. (2014). “Influence of fire damage on the modal parameters of a prestressed concrete double-tee joist roof.” Structural Control and Health Monitoring, Vol. 21, No. 11, pp. 1335–1346, DOI: https://doi.org/10.1002/stc.1651.
Wu, B. (2003). “Mechanical properties of reinforced concrete structures after fire.” Science Press, Beijing, China, pp. 453–462.
Xiao, L. and Qu, W. Z. (2012). “Nonlinear structural damage detection using support vector machines.” Proc., Conf, on Health Monitoring of Structural and Biological Systems, San Diego, CA, pp. 806–821, DOI: https://doi.org/10.1117/12.914688.
Zhu, D. L., Wang, Z. Q., Qiao, M., and Han, Y. L. (2011). “Elastic-Plastic analysis on continuous reinforced concrete beams at elevated temperatures caused by the redistribution of internal forces.” Journal of Harbin in Engineering University, Vol. 32, No. 2, pp. 165–172, DOI: https://doi.org/10.3969/j.issn.1006-7043.2011.02.006.
Acknowledgements
The authors are grateful to editors and anonymous referees for their very valuable comments and suggestions, which have significantly helped improve the quality of this paper. This research work was supported by the National Natural Science Foundation of China (Grant No. 51608289), the China Postdoctoral Science Foundation (Grant No. 2018 M632640), the Qingdao Postdoctoral Applied Research Project (Grant No. 2018103), the Tianjin Transportation Science and Technology Development Project (No. 2016A-02) and the First-Class Discipline Project Funded by the Education Department of Shandong Province. The financial support is highly appreciated.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, C., Liu, C., Liu, C. et al. Fire Damage Identification in RC Beams based on Support Vector Machines considering Vibration Test. KSCE J Civ Eng 23, 4407–4416 (2019). https://doi.org/10.1007/s12205-019-2353-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-019-2353-7