This study proposes an efficient full probabilistic design method using an advanced Monte Carlo simulation (MCS) algorithm, so-called generalized Subset Simulation (GSS), for which a concept of design response vector is proposed. The proposed design response vector contains responses of all possible designs and is used to, simultaneously, explore failure domains of different designs. With the design response vector, the proposed approach determines feasible design domains of different geotechnical limit states by a single GSS run. The GSS run can be terminated as the target reliability level (or other user-defined reliability levels) is reached to avoid completely exploring the whole design space. This provides significant computational savings, particularly as the design space is relatively large and the target reliability level is relatively high. The proposed approach is illustrated using a drilled shaft design example. Results show that it provides an efficient and robust vehicle to determine the feasible design domains for full probabilistic design compared with direct MCS and original Subset Simulation-based approaches. Moreover, based on failure samples generated by GSS, the design point is, approximately, determined and can be used to back-calculate case-dependent resistance (or deformation) factors, which links MCS-based full probabilistic design to semi-probabilistic design.

本研究提出了一种使用高级蒙特卡罗模拟 (MCS) 算法的高效全概率设计方法，即所谓的广义子集模拟 (GSS)，为此提出了设计响应向量的概念。提议的设计响应向量包含所有可能设计的响应，并用于同时探索不同设计的故障域。使用设计响应向量，所提出的方法通过单个 GSS 运行确定不同岩土工程极限状态的可行设计域。当达到目标可靠性级别（或其他用户定义的可靠性级别）时，可以终止 GSS 运行，以避免完全探索整个设计空间。这提供了显着的计算节省，特别是设计空间比较大，目标可靠性水平比较高。使用钻孔轴设计示例说明了所提出的方法。结果表明，与直接 MCS 和原始的基于子集模拟的方法相比，它提供了一种有效且稳健的工具来确定完全概率设计的可行设计域。此外，基于由 GSS 生成的失效样本，设计点被近似确定，并可用于反算与案例相关的阻力（或变形）因素，这将基于 MCS 的全概率设计与半概率设计联系起来。结果表明，与直接 MCS 和原始的基于子集模拟的方法相比，它提供了一种有效且稳健的工具来确定完全概率设计的可行设计域。此外，基于由 GSS 生成的失效样本，设计点被大致确定，并可用于反算与案例相关的阻力（或变形）因素，这将基于 MCS 的全概率设计与半概率设计联系起来。结果表明，与直接 MCS 和原始的基于子集仿真的方法相比，它提供了一种有效且稳健的工具，以确定完全概率设计的可行设计域。此外，基于由 GSS 生成的失效样本，设计点被大致确定，并可用于反算与案例相关的阻力（或变形）因素，这将基于 MCS 的全概率设计与半概率设计联系起来。