A novel LSTM-integrated non-intrusive ROM for reliability analysis of hysteretic systems with large stochastic dimension

• Published in: International journal of non-linear mechanics 2024-10, Vol.165, p.104803, Article 104803
• Main Authors: Bharti, Chandan, Ghosh, Debraj
• Format: Article
• Language: English
• Subjects: Large stochastic dimension; LSTM; Machine learning; Non-intrusive reduced order model; Reliability; Uncertainty quantification
• ISSN: 0020-7462; 1878-5638
• DOI: 10.1016/j.ijnonlinmec.2024.104803

Failure probability estimation of hysteretic systems subjected to random process excitation appears in many practical engineering applications, such as structures subjected to earthquake and vibration control. This task is computationally challenging due to the need for repeated solutions of a high-resolution model. This cost grows significantly with the stochastic dimension — characterized by the number of random variables, in this case, arising from the discretization of the random excitation. To address this issue, a novel non-intrusive reduced order model (ROM) is proposed in this paper. Accordingly, the complexity due to spatial discretization is first reduced using proper orthogonal decomposition. However, interpolation among response time histories in the resulting reduced solution space is computationally infeasible due to large stochastic dimensionality. An existing ROM developed by the authors is now improved by adopting a long short-term memory (LSTM) network for this interpolation. Furthermore, this adoption required two additional modifications in the mathematical structure of the ROM related to data compression and nonlinear coupling. The proposed methodology can be seamlessly integrated with black-box nonlinear solvers. Through detailed numerical studies on a beam on Winkler foundation and a multi-storied building frame subjected to stationary and non-stationary excitations, the proposed ROM is found to be accurate, efficient, and robust with respect to the frequency band of interest. The ROM has shown reduction of two orders of magnitude in the computational time. Furthermore, the proposed ROM is inherently parallelizable and holds promise to be scaled to other types of nonlinear systems with large stochastic dimensionality. •New non-intrusive ROM for nonlinear stochastic dynamical system.•It enhances a POD based non-intrusive ROM with LSTM-based surrogate.•Addressing large stochastic dimensionality due to random process excitation.•Bilinear hysteric nonlinearity is considered.•Achieved significant speed-up and robustness.