Probabilistic Installation Windows for Suction Caissons under Coupled Wave-Current Seabed Loading
DOI:
https://doi.org/10.66238/ijrss.v6i2.128Keywords:
Suction Caisson, Probabilistic Methods, Wave-Current Interaction, Metocean ModelingAbstract
The reliable installation of offshore foundations is a critical bottleneck in the rapid deployment of marine renewable energy infrastructure. Suction caissons present a highly efficient foundation solution, yet their installation is notoriously sensitive to complex metocean conditions, particularly the coupled effects of wave and current kinematics on seabed stability. This paper presents a comprehensive analytical framework for determining probabilistic installation windows for suction caissons subjected to coupled wave-current seabed loading. By integrating advanced hydrodynamic boundary layer modeling with stochastic metocean time-series analysis, this research transitions away from deterministic operational limits toward a robust probabilistic assessment model. The methodology quantifies the transient seabed bearing capacity reductions and scouring potentials during the critical touchdown and initial penetration phases. A Markov Chain Monte Carlo simulation approach is utilized to generate synthetic weather states, enabling the evaluation of continuous operational windows against dynamically calculated structural and geotechnical thresholds. The application of this framework to a representative offshore wind farm site demonstrates that traditional decoupled assessment methods significantly overestimate safe installation windows, leading to unquantified operational risks. The findings illustrate that accounting for non-linear wave-current interactions reduces the identified safe operational windows by a measurable margin, varying according to seasonal storm frequency. This research provides offshore engineers with a vital predictive tool for optimizing vessel deployment schedules, mitigating project delays, and enhancing the overall safety of marine construction operations.
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