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Optimization of life-cycle cost of retrofitting school buildings under seismic risk using evolutionary support vector machine

    Min-Yuan Cheng Affiliation
    ; Hsi-Hsien Wei Affiliation
    ; Yu-Wei Wu Affiliation
    ; Hung-Ming Chen Affiliation
    ; Cai-Wei Wu Affiliation

Abstract

The assessment of the seismic performance of existing school buildings is especially important in seismic-disaster mitigation planning. Utilizing a support vector machine coupled with a fast messy genetic algorithm, this study developed two inference models, both using the same input variables: i.e., 18 building characteristics selected based on expert opinion. The first model was designed to judge whether a building needs to be retrofitted; and the second, to estimate the cost of retrofitting buildings to specific levels. The study proposes a life-cycle seismic risk framework that takes into account projections of the seismic risk a given building will confront over the course of its entire existence, and thus helps determine the economically optimal level of retrofitting. The results of a case study indicate that the higher upfront cost of retrofitting that is required to reach higher seismic performance levels could, depending on the level of predicted seismic risk, be offset by lower repair costs in the long run. It is hoped that this research will serve as a basis for further studies of the assessment of the life-cycle seismic risk of school buildings, with the wider aim of arriving at an economically optimal building-retrofit policy.

Keyword : life cycle cost, seismic risk, seismic retrofitting, support vector machine

How to Cite
Cheng, M.-Y., Wei, H.-H., Wu, Y.-W., Chen, H.-M., & Wu, C.-W. (2018). Optimization of life-cycle cost of retrofitting school buildings under seismic risk using evolutionary support vector machine. Technological and Economic Development of Economy, 24(2), 812-824. https://doi.org/10.3846/tede.2018.247
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Mar 20, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Alam, N., Alam, M. S., & Tesfamariam, S. (2012). Buildings’ seismic vulnerability assessment methods: a comparative study. Natural hazards, 62(2), 405-424. https://doi.org/10.1007/s11069-011-0082-4

ATC. (1996). Seismic evaluation and retrofit of concrete buildings. Applied Technology Council, Redwood City, CA.

Cardone, D. (2007). Nonlinear static methods vs. experimental shaking table test results. Journal of Earthquake Engineering, 11(6), 847-875. https://doi.org/10.1080/13632460601173938

Cardone, D., & Perrone, G. (2017). Damage and loss assessment of pre-70 RC frame buildings with FEMA P-58. Journal of Earthquake Engineering, 21(1), 23-61. https://doi.org/10.1080/13632469.2016.1149893

Cardone, D., Sullivan, T. J., Gesualdi, G., & Perrone, G. (2017). Simplified estimation of the expected annual loss of reinforced concrete buildings. Earthquake Engineering & Structural Dynamics, 46(12), 2009-2032. https://doi.org/10.1002/eqe.2893

Chai, J. F., & Teng, T. J. (2012). Seismic design force for buildings in Taiwan. Paper presented at the 15th World Conference on Earthquake Engineering, Lisbon, Portugal.

Chen, C. S., Cheng, M. Y., & Wu, Y. W. (2012a). Seismic assessment of school buildings in Taiwan using the evolutionary support vector machine inference system. Expert Systems with Applications, 39(4), 4102-4110. https://doi.org/10.1016/j.eswa.2011.09.078

Chen, H. M., Kao, W. K., & Tsai, H. C. (2012b). Genetic programming for predicting aseismic abilities of school buildings. Engineering Applications of Artificial Intelligence, 25(6), 1103-1113. https://doi.org/10.1016/j.engappai.2012.04.002

Cheng, M. Y., & Wu, Y. W. (2009). Evolutionary support vector machine inference system for construction management. Automation in Construction, 18(5), 597-604. https://doi.org/10.1016/j.autcon.2008.12.002

Cheng, M. Y., Wu, Y. W., & Syu, R. F. (2014). Seismic assessment of bridge diagnostic in Taiwan using the evolutionary support vector machine inference model. Applied Artificial Intelligence, 28(5), 449-469. https://doi.org/10.1080/08839514.2014.905818

Chiu, C. K.; & Wang, R. X. (2012). A risk-based estimating method for the lifetime reparability of an RC building damaged by earthquakes. Journal of Architecture, 80(0), 45-62.

Chung, L. L., Hwang, S. J., & Wu, L. Y. (2012). A preliminary assessment of seismic performance for school buildings in Taiwan. Structural Engineering, 27(1), 61-80.

Drucker, H., Burges, C., Kaufman, L., Smola, A., & Vapnik, V. N. (1996). Support vector regression machines. Advances in neural information processing systems. Cambridge, MA: The MIT Press.

Ghosh, S., Datta, D., & Katakdhond, A. A. (2011). Estimation of the Park–Ang damage index for planar multi-storey frames using equivalent single-degree systems. Engineering Structures, 33(9), 2509-2524. https://doi.org/10.1016/j.engstruct.2011.04.023

Goldberg, D. E., Deb, K., & Korb, B. (1991). Don’t worry, be messy. Paper presented at the ICGA.

Guéguen, P., Michel, C., & LeCorre, L. (2007). A simplified approach for vulnerability assessment in moderate-to-low seismic hazard regions: application to Grenoble (France). Bulletin of Earthquake Engineering, 5(3), 467-490. https://doi.org/10.1007/s10518-007-9036-3

Guettiche, A., Guéguen, P., & Mimoune, M. (2017). Seismic vulnerability assessment using association rule learning: application to the city of Constantine, Algeria. Natural hazards, 86(3), 1223-1245. https://doi.org/10.1007/s11069-016-2739-5

Gupta, R., Ye, Y., & Sako, C. M. (2013). Financial variables and the out-of-sample forecastability of the growth rate of Indian industrial production. Technological and Economic Development of Economy, 19 (suppl. 1), S83-S99. https://doi.org/10.3846/20294913.2013.879544

Huang, C. L., & Wang, C. J. (2006). A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications, 31(2), 231-240. https://doi.org/10.1016/j.eswa.2005.09.024

Kao, W. K., Chen, H. M., & Chou, J. S. (2011). Aseismic ability estimation of school building using predictive data mining models. Expert Systems with Applications, 38(8), 10252-10263. https://doi.org/10.1016/j.eswa.2011.02.059

Khandekar, A. V., Antuchevičienė, J., & Chakraborty, S. (2015). Small hydro-power plant project selection using fuzzy axiomatic design principles. Technological and Economic Development of Economy, 21(5), 756-772. https://doi.org/10.3846/20294913.2015.1056282

Koo, C., Hong, T., & Kim, S. (2015). An integrated multi-objective optimization model for solving the construction time-cost trade-off problem. Journal of Civil Engineering and Management, 21(3), 323-333. https://doi.org/10.3846/13923730.2013.802733

Nakhaeizadeh, G., & Taylor, C. (1998). Machine learning and statistics. Statistics and Computing, 8(1), 89.

Park, Y. J., & Ang, H. S. (1985). Mechanistic seismic damage model for reinforced concrete. Journal of Structural Engineering, 111(4), 722-739. https://doi.org/10.1061/(ASCE)0733-9445(1985)111:4(722)

Riedel, I., Guéguen, P., Dalla Mura, M., Pathier, E., Leduc, T., & Chanussot, J. (2015). Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods. Natural hazards, 76(2), 1111-1141. https://doi.org/10.1007/s11069-014-1538-0

Takahashi, N., Nakano, Y., & Shiohara, H. (2006). Reparability demand spectrum of R/C buildings due to the lifecycle seismic loss estimation. Paper presented at the First European Conference on Earthquake Engineering and Seismology, Geneva, Switzerland.

Tsehayae, A. A., & Fayek, A. R. (2016). Developing and optimizing context-specific fuzzy inference system-based construction labor productivity models. Journal of construction engineering and management, 142(7), 04016017. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001127