A sampling-based method for high-dimensional time-variant reliability analysis

•A sampling method is proposed for high-dimensional time-variant reliability analysis.•Time-variant system is reinterpreted as multi-response time-invariant system.•The proposed method is tested on two high-dimensional dynamic reliability problems.•Results are compared to MCS and the original subset simulation. A new sampling-based method is proposed for high-dimensional time-variant reliability analysis with both random variables and random process as inputs. The new method employs the series expansion methods, e.g., the Karhunen-Loève expansion, to represent the input random process into a set of random variables. Based on the concepts of composite limit state, the time-variant reliability analysis is converted into a series system reliability problem with multiple responses. Then the generalized subset simulation is applied to compute cumulative failure probabilities which are further used to interpolate a completely cumulative failure probability curve for a given time interval. The advantage of the proposed method is that only a single run can provide a cumulative failure probability curve instead of repeated runs. Two high-dimensional time-variant reliability problems with input random process are used to demonstrate the performance of the proposed method.