Abstract | An efficient algorithm is presented that allows the rapid estimation of the influence of model parameter uncertainties on the seismic performance of structures using incremental dynamic analysis (IDA) and Monte Carlo simulation with latin hypercube sampling. The fundamental blocks of this methodology have already been proposed as a means to quantify the uncertainty for structural models with non-deterministic parameters, whereby each model realization out of a predetermined sample size is subjected to a full IDA under multiple ground motion records. However, any practical application is severely restricted due to (a) our inability to determine a priori the required number of samples and (b) the disproportionate increase in the number of analyses when dealing with many random variables. Thus, two fundamental changes are incorporated. First, latin hypercube sampling is applied incrementally by starting with a small sample that is doubled successively until adequate accuracy has been achieved. At the same time, instead of maintaining the same properties for a given model realization over an entire ground-motion record suite, parameter sampling is performed on a record-by-record basis, efficiently expanding the model sample size without increasing the number of nonlinear dynamic analyses. Using a steel moment-resisting frame building as a test case, it is shown that the improved algorithm allows excellent scalability and extends the original methodology to be easily applicable to realistic large-scale applications with hundreds of random variables.