Effects of design parameters and sea level rise on lifecycle performance of rubble-mound breakwaters: A computational-statistical framework

This study presents a simulation-based framework to evaluate the long-term performance of rubble-mound breakwaters under hydrodynamic forcing and sea level rise (SLR). The framework integrates synthetic storm generation (StormSim), process-based damage modeling (CSHORE), and suite of statistical methods, including Cox hazard models, Principal Component Analysis (PCA), and machine learning classification, to identify performance-critical parameters and assess structural resilience under non-stationary forcing conditions. The methodology is demonstrated through application to 32 breakwater configurations at a site within the North Atlantic Coast Comprehensive Study region, with 1000 synthetic lifecycles spanning 50 years. Key findings for the tested wave climate include: steeper slopes exhibited 20-fold higher initial failure risk than milder slopes, though this differential decreased over time; armor stone size and porosity strongly influenced time-to-repair; spectral wave height and water level dominated damage prediction (odds ratios, OR= 15.6 and 4.6); and SLR incorporation accelerated damage progression, particularly after Year 30. The framework successfully quantified time-dependent reliability, extracted spatial damage patterns via PCA , and ranked forcing parameters by importance. While quantitative results are specific to North Atlantic conditions and selected breakwater configurations, the methodology is transferable given appropriate storm climatology, SLR projections, and structural parameters. The results demonstrate the importance of integrating sea level rise into performance-based coastal infrastructure design through physics-based modeling coupled with comprehensive statistical assessment.