A mixture model approach is presented for combining the results of different models or analysis methods into a single probabilistic demand model for seismic assessment. In general, a structure can be represented using models of different type or different number of degrees of freedom, each offering a distinct compromise in computational load versus accuracy; it may also be analysed via methods of different complexity, most notably static versus dynamic nonlinear approaches. Employing the highest fidelity options is theoretically desirable but practically infeasible, at best limiting their use to calibrating or validating lower fidelity approaches. Instead, a large sample of low fidelity results can be selectively combined with sparse results from higher fidelity models or methods to simultaneously capitalize on the frugal nature of the former and the low bias of the latter to deliver fidelity at an acceptable cost. By employing a minimal 5 parameter power-law-based surrogate model we offer two options for forming mixed probabilistic seismic demand models that (i) can combine different models with varying degree of fidelity at different ranges of structural response, or (ii) nonlinear static and dynamic results into a single output suitable for fragility assessment.

提出了一种混合模型方法，用于将不同模型或分析方法的结果组合成用于地震评估的单个概率需求模型。一般而言，可以使用不同类型或不同自由度的模型来表示结构，每个模型都在计算负载与精度之间提供不同的折衷；它也可以通过不同复杂性的方法进行分析，最显着的是静态与动态非线性方法。采用最高保真度选项在理论上是可取的，但实际上是不可行的，最多只能将其用于校准或验证低保真度方法。反而，大量的低保真结果样本可以选择性地与来自高保真模型或方法的稀疏结果相结合，同时利用前者的节俭性质和后者的低偏差，以可接受的成本提供保真度。通过采用基于最小 5 参数幂律的替代模型，我们提供了两种用于形成混合概率地震需求模型的选项，它们 (i) 可以在不同的结构响应范围内组合具有不同保真度的不同模型，或者 (ii) 非线性静态并将动态结果转换为适合脆弱性评估的单一输出。