Bivariate Archimedean Copulas to Solve Complex Dependency in Marine  Engineering Problems

Adhitya Ryan Ramadhani; Waskito Pranowo

doi:10.12962/j25800914.v7i2.19334

Bivariate Archimedean Copulas to Solve Complex Dependency in Marine Engineering Problems

Adhitya Ryan Ramadhani, Waskito Pranowo

Abstract

Climate change has made offshore installations suffer severe consequences from extreme marine hazards. These offshore installations were designed to withstand extreme natural hazards. Marine natural hazards commonly occur interdependently. Thus, a multivariate model to capture this complex dependence structure is necessary. Practically, modelling marine natural hazards is usually assumed to be mutually independent or correlated by a multivariate Gaussian distribution. However, this biased assumption is not relevant to capture the real dependence structure between marine parameters. Copula functions are used to model the complex dependence structure between marine parameters. A simplified case study is selected to illustrate the modelling between wave height and wind speed. Results are compared with the traditional joint probability approach to demonstrate the advantage of copula functions. The use of copula functions provides a better result to model any complex dependence structure between correlated variables.

Keywords

Dependence modelling; Copula functions; Offshore structures; Probabilistic method; Marine engineering

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References

Cruz AM, Krausmann E (2008) Damage to offshore oil and gas facilities following hurricanes Katrina and Rita: An overview. J Loss Prev Process Ind 21:620–626

Wilson JF (2002) Dynamic Offshore Structure. John Wiley & Sons, New Jersey

McKeand AM, Gorguluarslan RM, Choi S-K (2021) Stochastic analysis and validation under aleatory and epistemic uncertainties. Reliab Eng Syst Saf 205:107258

Reilly AC, Baroud H, Flage R, Gerst MD (2021) Sources of uncertainty in interdependent infrastructure and their implications. Reliab Eng Syst Saf 213:107756

Chen C, Yang M, Reniers G (2021) A dynamic stochastic methodology for quantifying HAZMAT storage resilience. Reliab Eng Syst Saf 215:107909

Chen Y, Li S, Kang R (2021) Epistemic uncertainty quantification via uncertainty theory in the reliability evaluation of a system with failure Trigger effect. Reliab Eng Syst Saf 215:107896

Lucas C, Guedes Soares C (2015) Bivariate distributions of significant wave height and mean wave period of combined sea states. Ocean Engineering 106:341–353

Muraleedharan G, Lucas C, Martins D, Guedes Soares C, Kurup PG (2015) On the distribution of significant wave height and associated peak periods. Coastal Engineering 103:42–51

Sinsabvarodom C, Chai W, Leira BJ, Høyland K V., Naess A (2020) Uncertainty assessments of structural loading due to first year ice based on the ISO standard by using Monte-Carlo simulation. Ocean Engineering 198:106935

Xiao Q (2014) Evaluating correlation coefficient for Nataf transformation. Probabilistic Engineering Mechanics 37:1–6

Zhi B, Wang X, Xu F (2022) Managing inventory financing in a volatile market: A novel data-driven copula model. Transp Res E Logist Transp Rev 165:102854

Fang Y, Madsen L (2013) Modified Gaussian pseudo-copula: Applications in insurance and finance. Insur Math Econ 53:292–301

Boako G, Tiwari AK, Ibrahim M, Ji Q (2019) Analyzing dynamic dependence between gold and stock returns: Evidence using stochastic and full range tail dependence copula models. Financ Res Lett. https://doi.org/10.1016/j.frl.2018.12.008

Hashemi SJ, Ahmed S, Khan FI (2015) Correlation and Dependency in Multivariate Process Risk Assessment. IFAC-PapersOnLine 48:1339–1344

Kabir S, Taleb-Berrouane M, Papadopoulos Y (2019) Dynamic reliability assessment of flare systems by combining fault tree analysis and Bayesian networks. Energy Sources, Part A: Recovery, Utilization and Environmental Effects. https://doi.org/10.1080/15567036.2019.1670287

Taleb-berrrouane M (2019) Dynamic Corrosion Risk Assessment in the Oil and Gas Production andProcessing Facility (Doctoral dissertation, Memorial University of Newfoundland). Memorial University of Newfoundland

Jiang C, Xiong L, Yan L, Dong J, Xu C-Y (2019) Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice. Hydrol Earth Syst Sci 23:1683–1704

Salleh N, Yusof F, Yusop Z (2016) Bivariate copulas functions for flood frequency analysis. p 060007

De Michele C, Salvadori G, Passoni G, Vezzoli R (2007) A multivariate model of sea storms using copulas. Coastal Engineering 54:734–751

Ramadhani A, Khan F, Colbourne B, Ahmed S, Taleb-Berrouane M (2021) Environmental load estimation for offshore structures considering parametric dependencies. Safety in Extreme Environments. https://doi.org/10.1007/s42797-021-00028-y

Ramadhani A, Khan F, Colbourne B, Ahmed S, Taleb-Berrouane M (2022) Resilience assessment of offshore structures subjected to ice load considering complex dependencies. Reliab Eng Syst Saf 222:108421

Pranowo W, Ramadhani A (2022) Geostatistics for Risk Level Mapping: Synthetic Data Example. Proceedings of the 1st International Conference on Contemporary Risk Studies, ICONIC-RS 2022, 31 March-1 April 2022, South Jakarta, DKI Jakarta, Indonesia. https://doi.org/10.4108/eai.31-3-2022.2320971

Pranowo W, Ramadhani AR (2023) A Comparison of Python-Based Copula Parameter Estimation for Archimedean-Based Asymmetric Copulas. SN Comput Sci 4:207

Nelsen RB (2006) An introduction to copulas, 2nd ed. Springer

Zhang Y, Kim C-W, Beer M, Dai H, Soares CG (2018) Modeling multivariate ocean data using asymmetric copulas. Coastal Engineering 135:91–11

Genest C, Rémillard B, Beaudoin D (2009)Goodness-of-fit tests for copulas: A review and a power study. Insur Math Econ 44:199–213

Hashemi SJ, Ahmed S, Khan FI (2015) Correlationand Dependency in Multivariate Process Risk Assessment. IFAC-PapersOnLine 48:1339–1344

Lü T-J, Tang X-S, Li D-Q, Qi X-H (2020) Modeling multivariate distribution of multiple soil parameters using vine copula model. Comput Geotech 118:103340

Li D, Tang X (2014) Modeling and simulation of bivariate distribution of shear strength parameters using copulas. In: Risk and Reliability inGeotechnical Engineering. CRC Press, London, pp 77–128

ERDDAP (2022) Mouth of Placentia Bay Buoy. https://www.smartatlantic.ca/erddap/tabledap/SMA_MouthofPlacentiaBayBuoy.html. Accessed 28 Aug 2021

Zhang Y, Gomes AT, Beer M, Neumann I, Nackenhorst U, Kim C-W (2019) Modeling asymmetric dependences among multivariate soil data for the geotechnical analysis – The asymmetric copula approach. Soils and Foundations 59:1960–1979

Amini A, Abdollahi A, Hariri-Ardebili MA, Lall U (2021) Copula-based reliability and sensitivity analysis of aging dams: Adaptive Kriging and polynomial chaos Kriging methods. Appl Soft Comput 109:107524

DOI: http://dx.doi.org/10.12962/j25800914.v7i2.19334

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Pusat Publikasi Ilmiah LPPM Instutut Teknologi Sepuluh Nopember
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