Abstract

This study investigates the system stability of breakwater foundations subjected to earthquakes from a probabilistic point of view. A fully probabilistic approach, i.e., a combination of the Monte Carlo simulation and Bishop’s simplified method, has been developed to evaluate the system failure probability of foundation damage, one of the prevailing failures encountered during earthquakes. Twelve sections of perforated caisson breakwaters located around Korea were chosen as case studies. First, the reliability analysis was performed for all the breakwaters at existing conditions; then, the calibration process involving the estimation of load and resistance factors was conducted for 12 breakwaters at three levels of the target reliability index. As the performance function, used in the stability analysis of breakwater foundations, is defined based on an implicit shape with a high-dimensional space of variables, the calibration process of load and resistance factors becomes cumbersome and complicated. Therefore, this study has proposed a sensitivity analysis to be implemented prior to the calibration process to elicit the effects of variables on the stability of each breakwater, which, thereafter, effectively directs the calibration process. The results of this study indicate that the failures in the foundation of breakwaters frequently occur in different modes. Therefore, the failure probability should be estimated considering all possible failure modes of the foundation. The sensitivity results elucidate that the soil strength parameters are the dominant variables, contributing to the stability of foundations, whereas the seismic coefficient presents the negative effect, causing the insecurity of breakwaters. In particular, the deadweights, though directly contributing to the seismic forces, show a small effect on the stability of foundations. The calibration shows that the load factors slightly vary with an increase in the target reliability index and set 1.10 for three safety levels. In contrast, the resistance factor exhibits an inverse relationship with the specified reliability index. Especially when the load factor equals 1.10, the resistance factors are 0.90, 0.85, and 0.80, corresponding to the reliability index of 2.0, 2.5, and 3.0, respectively. Eventually, it is proved that the sensitivity analysis prior to the calibration process makes the procedure more efficient. Accordingly, the iteration of simulation execution is diminished, and the convergence is quickly accomplished.

Highlights

  • Reliability-based design concepts and their applications to load and resistance factor design (LRFD) have proven to be efficient for cost-saving and material use [1,2]

  • The level II method, or the approximation method that includes the mean-value first-order second-moment (MVFOSM) method and first-order reliability method (FORM), requires the distributions of the load and resistance instead of all variables, the safety is defined in terms of reliability indexes (RIs)

  • Based on the regression line, the associated RI corresponding to the conventional design approach (CDA) safety factor of the existing BRW can be approximated

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Summary

Introduction

Reliability-based design concepts and their applications to load and resistance factor design (LRFD) have proven to be efficient for cost-saving and material use [1,2]. There are three levels of reliability-based design. The level III method, the fully probabilistic approach e.g., Monte Carlo simulation (MCS), derives the failure probability based on the Sustainability 2021, 13, 1730. Sustainability 2021, 13, 1730 requirement of all known probability distributions of all the input variables. The level II method, or the approximation method that includes the mean-value first-order second-moment (MVFOSM) method and first-order reliability method (FORM), requires the distributions of the load and resistance instead of all variables, the safety is defined in terms of reliability indexes (RIs). In the level I method, or a semi-probabilistic approach, safety is evaluated based on separate load and resistance factors (LRFs) or partial safety factors (PSFs), which are forth determined using the two aforementioned methods [2]

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