Integrating Peak Ground Acceleration as a Damage Factor in Risk-Based Premium Rate Assessment using K-medoids Bayesian networks

Devni Prima  Sari; Dedi  Rosadi; Adhitya Ronnie  Effendi; Danardono; Media  Rosha

doi:10.6180/jase.202506_28(6).0018

Integrating Peak Ground Acceleration as a Damage Factor in Risk-Based Premium Rate Assessment using K-medoids Bayesian networks

Devni Prima Sari^1,2This email address is being protected from spambots. You need JavaScript enabled to view it., Dedi Rosadi³, Adhitya Ronnie Effendi³, Danardono³, and Media Rosha¹

¹Department of Mathematics, Universitas Negeri Padang, Indonesia

²Data Analytics, Mathematical Modelling, and Forecasting Research Group, Universitas Negeri Padang, Indonesia

³Department of Mathematics, Universitas Gadjah Mada, Indonesia

Received: January 19, 2024
Accepted: June 7, 2024
Publication Date: September 25, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202506_28(6).0018

In the insurance market, determining fair and acceptable premium rates requires an accurate evaluation of risk. In the context of earthquake damage, the Peak Ground Acceleration (PGA) level is essential for assessing the intensity of ground shaking and its effect on structures. However, the present approaches for adding the PGA level as a damage factor in risk-based premium rate calculation are inaccurate and inefficient. This study proposes integrating the PGA level as a damage factor using Bayesian networks to overcome this issue. Using the probabilistic nature of Bayesian networks, the suggested solution provides a more complete and accurate method for determining premium rates. The premise is that the integrated Bayesian network model will produce more accurate calculations of premium rates than previous techniques. This work is significant because it has the potential to improve the fairness and openness of premium rate determination, resulting in enhanced risk assessment methods in the insurance business. By taking into account the unique impact of the PGA level on building damage, insurers can better align premium rates with the real risk profile of insured items, which is advantageous for both insurers and policyholders. According to the research findings, the premium rate increases as the level of risk in a location rises. Incorporating PGA and the extent of damage, the output of the BN model can also be used to estimate the premium rate per subdistrict. This analysis clearly demonstrates that the premium rates varied by subdistrict.

Keywords: Earthquake; Building Damage; Bayesian Network; K-medoids

[1] K. M. Hamdia, M. Arafa, and M. Alqedra, (2018) “Structural damage assessment criteria for reinforced concrete buildings by using a Fuzzy Analytic Hierarchy process" Underground Space 3(3): 243–249.
[2] J. M. Adam, J. D. Moreno, M. Bonilla, and T. M. Pellicer, (2016) “Classification of damage to the structures of buildings in towns in coastal areas" Engineering Failure Analysis 70: 212–221.
[3] F. Rodrigues, R. Matos, M. D. Prizio, and A. Costa, (2018) “Conservation level of residential buildings: Methodology evolution" Construction and Building Materials 172: 781–786. DOI: 10.1016/J.CONBUILDMAT.2018.03.129.
[4] L. Li, J. Chen, and W. Wang, (2023) “Evaluation of the Residual Seismic Capacity of Post-Earthquake Damaged RC Columns Based on the Damage Distribution Model" Buildings 2023, Vol. 13, Page 595 13: 595. DOI: 10.3390/BUILDINGS13030595.
[5] A. Rosti, C. D. Gaudio, M. Rota, P. Ricci, M. D. Ludovico, A. Penna, and G. M. Verderame, (2021) “Empirical fragility curves for Italian residential RC buildings" Bulletin of Earthquake Engineering 19: 3165– 3183. DOI: 10.1007/S10518-020-00971-4/TABLES/3.
[6] F. Zhao and C. Zhang, (2020) “Building Damage Evaluation from Satellite Imagery using Deep Learning" Proceedings - 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science, IRI 2020: 82–89. DOI: 10.1109/IRI49571.2020.00020.
[7] B. Galloway, J. Hare, D. Brunsdon, P. Wood, B. Lizundia, and M. Stannard, (2014) “Lessons from the post-earthquake evaluation of damaged buildings in Christchurch" Earthquake Spectra 30: 451–474. DOI: 10.1193/022813EQS057M.
[8] R. Jünemann, J. C. de la Llera, M. A. Hube, L. A. Cifuentes, and E. Kausel, (2015) “A statistical analysis of reinforced concrete wall buildings damaged during the 2010, Chile earthquake" Engineering Structures 82: 168–185. DOI: 10.1016/J.ENGSTRUCT.2014.10.014.
[9] G. Deierlein, A. Reinhorn, and M. Willford. NEHRP Seismic Design Technical Brief No. 4 - Nonlinear Structural Analysis for Seismic Design: A Guide for Practicing Engineers. 2010.
[10] G. Augusti, M. Ciampoli, and P. Giovenale, (2001) “Seismic vulnerability of monumental buildings" Structural Safety 23: 253–274. DOI: 10.1016/S0167-4730(01) 00018-2.
[11] S. Mousavi, A. Bagchi, and V. K. Kodur, (2008) “Review of post-earthquake fire hazard to building structures" https://doi.org/10.1139/L08-029 35: 689–698. DOI: 10.1139/L08-029.
[12] S. Lagomarsino, (2006) “On the vulnerability assessment of monumental buildings" Bulletin of Earthquake Engineering 4: 445–463. DOI: 10.1007/S10518-006-9025-Y/METRICS.
[13] O. Mavrouli and J. Corominas, (2010) “Vulnerability of simple reinforced concrete buildings to damage by rockfalls" Landslides 7: 169–180. DOI: 10.1007/S10346-010-0200-5/FIGURES/14.
[14] F. V. Karantoni and G. Bouckovalas, (1997) “Description and analysis of building damage due to Pyrgos, Greece earthquake" Soil Dynamics and Earthquake Engineering 16: 141–150. DOI: 10.1016/S0267-7261(96)00035-8.
[15] Y. Fan, Q. Wen, W. Wang, P. Wang, L. Li, and P. Zhang, (2017) “Quantifying Disaster Physical Damage Using Remote Sensing Data—A Technical Work Flow and Case Study of the 2014 Ludian Earthquake in China" International Journal of Disaster Risk Science 8: 471–488. DOI: 10.1007/S13753-017-0143-8/FIGURES/8.
[16] L. Barrington, S. Ghosh, M. Greene, S. Har-Noy, J. Berger, S. Gill, A. Y.-M. Lin, and C. Huyck, (2011) “Crowdsourcing earthquake damage assessment using remote sensing imagery" Annals of Geophysics 54: DOI: 10.4401/ag-5324.
[17] H. Qi and M. S. Altinakar, (2011) “Simulation-based decision support system for flood damage assessment under uncertainty using remote sensing and census block information" Natural Hazards 59: 1125–1143. DOI: 10.1007/S11069-011-9822-8/METRICS.
[18] F. Yamazaki and M. Matsuoka, (2007) “Remote sensing technologies in post-disaster damage assessment" Journal of Earthquake and Tsunami 01: 193–210. DOI: 10.1142/S1793431107000122.
[19] J. Urrutia, L. Bautista, and E. Baccay. “Mathematical models for estimating earthquake casualties and damage cost through regression analysis using matrices”. In: Journal of Physics: Conference Series. 495. 1. IOP Publishing. 2014, 012024.
[20] R. T. Musulin, (1997) “Issues in the Regulatory Acceptance of Computer Modeling for Property Insurance Ratemaking" Journal of Insurance Regulation: 342–359.
[21] L. F. Li, J. F. Wang, and H. Leung, (2010) “Using spatial analysis and bayesian network to model the vulnerability and make insurance pricing of catastrophic risk" International Journal of Geographical Information Science 24: 1759–1784. DOI: 10.1080/13658816.2010.510473.
[22] L. F. Li, J. F. Wang, H. Leung, and S. Zhao, (2012) “A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk" Risk Analysis 32: 1072–1092. DOI: 10.1111/j.1539-6924.2012.01790.x.
[23] L. F. Li, J. F. Wang, and C. Wang, (2005) “Typhoon insurance pricing with spatial decision support tools" International Journal of Geographical Information Science 19: 363–384. DOI: 10.1080/13658810412331317742.
[24] H. Pratiwi. “Earthquake Insurance Model Using SpatioTemporal Point Process Approach". (phdthesis). Universitas Gadjah Mada, 2015.
[25] T. Koski and J. Noble. Bayesian Networks: An Introduction. John Wiley Sons, Ltd, 2009.
[26] W. J. Petak and A. A. Atkisson. Natural hazard risk assessment and public policy : anticipating the unexpected. Springer-Verlag, 1982, 489.
[27] M. S. Yucemen, (2005) “Probabilistic assessment of earthquake insurance rates for Turkey" Natural Hazards 35: 291–313. DOI: 10.1007/S11069-004-6485-8/METRICS.
[28] G. S. Ibrahim. Pengetahuan Seismologi. Badan Meteorologi dan Geofisika, 2005.
[29] Y. Y. Bayraktarli and M. H. Faber, (2011) “Bayesian probabilistic network approach for managing earthquake risks of cities" Georisk 5: 2–24. DOI: 10.1080/17499511003679907.
[30] Y. Y. Bayraktarli, J.-P. Ulfkjaer, U. Yazgan, and M. H. Faber, (2005) “On the application of Bayesian probabilistic networks for earthquake risk management" 9th international conference on structural safety and reliability, Italy, Rome:
[31] Y. Y. Bayraktarli, U. Yazgan, A. Dazio, and M. H. Faber. “Capabilities of the Bayesian probabilistic networks approach for earthquake risk management”. In: First European Conference on Earthquake Engineering and Seismology. 2006, 3–8.
[32] D. P. Sari, D. Rosadi, A. R. Effendie, and Danardono, (2019) “K-means and bayesian networks to determine building damage levels" Telkomnika (Telecommunication Computing Electronics and Control) 17: 719–727. DOI: 10.12928/TELKOMNIKA.V17I2.11756.
[33] D. P. Sari, D. Rosadi, A. R. Effendie, and D. Danardono, (2021) “Discretization methods for bayesian networks in the case of the earthquake" Bulletin of Electrical Engineering and Informatics 10: 299–307. DOI: 10.11591/EEI.V10I1.2007.