Document Type : Technical Paper


1 Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.

2 Department of Civil Engineering, K.N.TOOSI University of Technology, Tehran, Iran.

3 Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia



A structural health monitoring system contains two components, i.e. a data collection approach comprising a network of sensors for recording the structural responses as well as an extraction methodology in order to achieve beneficial information on the structural health condition. In this regard, data mining which is one of the emerging computer-based technologies, can be employed for extraction of valuable information from obtained sensor databases. On the other hand, data inverse analysis scheme as a problem-based procedure has been developing rapidly. Therefore, the aforesaid scheme and data mining should be combined in order to satisfy increasing demand of data analysis, especially in complex systems such as bridges. Consequently, this study develops a damage detection methodology based on these strategies. To this end, an inverse analysis approach using data mining is applied for a composite bridge. To aid the aim, the support vector machine (SVM) algorithm is utilized to generate the patterns by means of vibration characteristics dataset. To compare the robustness and accuracy of the predicted outputs, four kernel functions, including linear, polynomial, sigmoid, and radial basis function (RBF) are applied to build the patterns. The results point out the feasibility of the proposed method for detecting damage in composite slab-on-girder bridges.


[1] H.A. Alcala’ Garrido, M.E. Rivero-Angeles, and E.A. Anaya, “Primary User Emulation in Cognitive Radio-Enabled WSNs for Structural Health Monitoring: Modeling and Attack Detection,” J. Sensors, Vol. 2019, pp. 1–14, 2019.
[2] H.H. Ghayeb, H.A. Razak, and N.H.R. Sulong, “Development and testing of hybrid precast concrete beam-to-column connections under cyclic loading,” Constr. Build. Mater., Vol. 151, pp. 258–278, 2017.
[3] K. Ghaedi, Z. Ibrahim, A. Javanmardi, and R. Rupakhety, “Experimental Study of a New Bar Damper Device for Vibration Control of Structures Subjected to Earthquake Loads,” J. Earthq. Eng., pp. 1–19, 2018.
[4] M. Gordan, Z. Ismail, H.A. Razak, and Z. Ibrahim, “Vibration-based Structural Damage Identification using Data Mining,” in 24th International Congress on Sound and Vibration (ICSV24) London, 2017.
[5] M. Gordan and K. Ghaedi, “Experimental Study on the Effectiveness of Tuned Mass Damper on a steel frame under Harmonic Load,” in 4th International Congress on Civil Engineering , Architecture and Urban Development, Shahid Beheshti University, Tehran, 2016.
[6] Z.X. Tan, D. P.Thambiratnam, T.H.T. Chan, M. Gordan, and H. Abdul Razak, “Damage detection in steel-concrete composite bridge using vibration characteristics and artificial neural network,” Struct. Infrastruct. Eng., pp. 1–15, 2019.
[7] K. Ghaedi, Z. Ibrahim, and H. Adeli, “Invited Review: Recent developments in vibration control of building and bridge structures,” J. Vibroengineering, Vol. 19, No. 5, p. 3564 3580, 2017.
[8] M. Gordan, H.A. Razak, Z. Ismail, and K. Ghaedi, “Data mining-based damage identification using imperialist competitive algorithm and artificial neural network,” Lat. Am. J. Solids Struct., Vol. 15, No. 8, pp. 1–14, 2018.
[9] M. Gordan, Z.B. Ismail, H.A. Razak, and K. Ghaedi, “Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring,” J. Civ. Eng. Constr., Vol. 9, No. 1, pp. 14–23, 2019.
[10] A. Azevedo and M.F. Santos, “KDD, SEMMA AND CRISP-DM:A Parallel Overview,” in IADIS European Conference Data Mining, 2008, pp. 182–185.
[11] M. Gordan, H.A. Razak, Z. Ismail, K. Ghaedi, Z.X. Tan, and H.H. Ghayeb, “A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining,” Appl. Soft Comput. J., Vol. 88, p. 106013, 2020.
[12] F. Moslehi, A.R. Haeri, and A.R. Moini, “Analyzing and investigating the use of electronic payment tools in Iran using data mining techniques,” J. AI Data Min., 2018.
[13] M. Mohammadi and M. Sarmad, “Outlier Detection for Support Vector Machine using Minimum Covariance,” J. AI Data Min., Vol. 7, No. 2, pp. 299–309, 2019.
[14] A. Mosavi, “Data mining for decision making in engineering optimal design,” J. AI Data Min., Vol. 2, No. 1, pp. 7–14, 2014.
[15] M. Gordan, Z. Ismail, Z. Ibrahim, and H. Hashim, “Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison,” in Recent Trends in Artificial Neural Networks, London: IntechOpen Limited, 2019.
[16] M. Saltan, S. Terzi, and E. Ug, “Back-calculation of pavement layer moduli and Poisson’s ratio using data mining,” Expert Syst. Appl., Vol. 38, No. 3, pp. 2600–2608, 2011.
[17] T. Pang-Ning, M. Steinbach, and V. Kumar, Introduction to data mining. Boston: Pearson Addison-Wesley, 2006.
[18] M. Gordan, H.A. Razak, Z. Ismail, and K. Ghaedi,“Recent developments in damage identification of structures using data mining,” Lat. Am. J. Solids Struct., Vol. 14, No. 13, pp. 2373–2401, 2017.
[19] M. Gordan et al., “Data mining-based damage identification of a slab-on-girder bridge using inverse analysis,” Measurement, Vol. 151, p. 107175, 2020.
[20] B. DeVille and P. Neville, Decision Trees for Analytics using SAS® Enterprise MinerTM. 2013.
[21] G. Linoff and M.J.A. Berry, Data mining techniques: for marketing, sales, and customer relationship management. Wiley Pub, Indianapolis, Ind., 2011.
[22] J. Tinoco, A. Gomes Correia, and P. Cortez, “Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns,” Comput. Geotech., Vol. 55, pp. 132–140, Jan. 2014.
[23] G. Gui, H. Pan, Z. Lin, Y. Li, and Z. Yuan, “Data-Driven Support Vector Machine with Optimization Techniques for Structural Health Monitoring and Damage Detection,” KSCE J. Civ. Eng., Vol. 21, No. 2, pp. 523–534, 2017.
[24] S. Radhika, Y. Tamura, and M. Matsui, “Journal of Wind Engineering Cyclone damage detection on building structures from pre- and post- satellite images using wavelet based pattern recognition,” Jnl. Wind Eng. Ind. Aerodyn., Vol. 136, pp. 23–33, 2015.
[25] S.K. Arsava, J. W. Chong, and Y. Kim, “A novel health monitoring scheme for smart structures,” J. Vib. Control, pp. 1–19, May 2014.
[26] B. Chen, Z. Wu, J. Liang, and Y. Dou, “Time-Varying Identification Model for Crack Monitoring Data from Concrete Dams based on Support Vector Regression and the Bayesian Framework,” Math. Probl. Eng., 2017.